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Simplify functional tests

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Matthias 2018-10-29 20:17:15 +01:00
parent 98050ff594
commit 6096f3ca47
1 changed files with 85 additions and 90 deletions
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175
freqtrade/tests/optimize/test_backtest_detail.py
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@ -1,11 +1,11 @@
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
import logging
from unittest.mock import MagicMock
from typing import NamedTuple
from typing import NamedTuple, List
from pandas import DataFrame
import pytest
from arrow import get as getdate
import arrow
from freqtrade.optimize.backtesting import Backtesting
@ -13,11 +13,15 @@ from freqtrade.strategy.interface import SellType
from freqtrade.tests.conftest import patch_exchange, log_has
ticker_start_time = arrow.get(2018, 10, 3)
ticker_interval_in_minute = 60
class BTContainer(NamedTuple):
"""
NamedTuple Defining BacktestResults inputs.
"""
data: DataFrame
data: List[float]
stop_loss: float
roi: float
trades: int
@ -25,19 +29,24 @@ class BTContainer(NamedTuple):
sell_r: SellType
columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
data_profit = DataFrame([
[getdate('2018-07-08 18:00:00').datetime, 0.0009910,
0.001011, 0.00098618, 0.001000, 12345, 1, 0],
[getdate('2018-07-08 19:00:00').datetime, 0.001000,
0.001010, 0.0009900, 0.0009900, 12345, 0, 0],
[getdate('2018-07-08 20:00:00').datetime, 0.0009900,
0.001011, 0.00091618, 0.0009900, 12345, 0, 0],
[getdate('2018-07-08 21:00:00').datetime, 0.001000,
0.001011, 0.00098618, 0.001100, 12345, 0, 1],
[getdate('2018-07-08 22:00:00').datetime, 0.001000,
0.001011, 0.00098618, 0.0009900, 12345, 0, 0]
], columns=columns)
def _build_dataframe(ticker_with_signals):
columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
frame = DataFrame.from_records(ticker_with_signals, columns=columns)
frame['date'] = frame['date'].apply(lambda x: ticker_start_time.shift(
minutes=(x * ticker_interval_in_minute)).datetime)
# Ensure floats are in place
for column in ['open', 'high', 'low', 'close', 'volume']:
frame[column] = frame[column].astype('float64')
return frame
data_profit = [
[0, 0.0009910, 0.001011, 0.00098618, 0.001000, 12345, 1, 0],
[1, 0.001000, 0.001010, 0.0009900, 0.0009900, 12345, 0, 0],
[2, 0.0009900, 0.001011, 0.00091618, 0.0009900, 12345, 0, 0],
[3, 0.001000, 0.001011, 0.00098618, 0.001100, 12345, 0, 1],
[4, 0.001000, 0.001011, 0.00098618, 0.0009900, 12345, 0, 0]]
tc_profit1 = BTContainer(data=data_profit, stop_loss=-0.01, roi=1, trades=1,
profit_perc=0.10557, sell_r=SellType.STOP_LOSS) # should be stoploss - drops 8%
@ -45,18 +54,12 @@ tc_profit2 = BTContainer(data=data_profit, stop_loss=-0.10, roi=1,
trades=1, profit_perc=0.10557, sell_r=SellType.STOP_LOSS)
tc_loss0 = BTContainer(data=DataFrame([
[getdate('2018-07-08 18:00:00').datetime, 0.0009910,
0.001011, 0.00098618, 0.001000, 12345, 1, 0],
[getdate('2018-07-08 19:00:00').datetime, 0.001000,
0.001010, 0.0009900, 0.001000, 12345, 0, 0],
[getdate('2018-07-08 20:00:00').datetime, 0.001000,
0.001011, 0.0010618, 0.00091618, 12345, 0, 0],
[getdate('2018-07-08 21:00:00').datetime, 0.001000,
0.001011, 0.00098618, 0.00091618, 12345, 0, 0],
[getdate('2018-07-08 22:00:00').datetime, 0.001000,
0.001011, 0.00098618, 0.00091618, 12345, 0, 0]
], columns=columns),
tc_loss0 = BTContainer(data=[
[0, 0.0009910, 0.001011, 0.00098618, 0.001000, 12345, 1, 0],
[1, 0.001000, 0.001010, 0.0009900, 0.001000, 12345, 0, 0],
[2, 0.001000, 0.001011, 0.0010618, 0.00091618, 12345, 0, 0],
[3, 0.001000, 0.001011, 0.00098618, 0.00091618, 12345, 0, 0],
[4, 0.001000, 0.001011, 0.00098618, 0.00091618, 12345, 0, 0]],
stop_loss=-0.05, roi=1, trades=1, profit_perc=-0.08839, sell_r=SellType.STOP_LOSS)
@ -64,30 +67,27 @@ tc_loss0 = BTContainer(data=DataFrame([
# Candle Data for test 1 – close at -8% (9200)
# Test with Stop-loss at 1%
# TC1: Stop-Loss Triggered 1% loss
tc1 = BTContainer(data=DataFrame([
[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
[getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9200, 9200, 12345, 0, 0],
[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9960, 9955, 12345, 0, 0],
[getdate('2018-06-10 11:00:00').datetime, 9955, 9975, 9955, 9990, 12345, 0, 0],
[getdate('2018-06-10 12:00:00').datetime, 9990, 9990, 9990, 9900, 12345, 0, 0]
], columns=columns),
stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.01, sell_r=SellType.STOP_LOSS) # should be
# stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.003, sell_r=SellType.FORCE_SELL) #
tc1 = BTContainer(data=[
[0, 10000.0, 10050, 9950, 9975, 12345, 1, 0],
[1, 10000, 10050, 9950, 9975, 12345, 0, 0],
[2, 9975, 10025, 9200, 9200, 12345, 0, 0],
[3, 9950, 10000, 9960, 9955, 12345, 0, 0],
[4, 9955, 9975, 9955, 9990, 12345, 0, 0],
[5, 9990, 9990, 9990, 9900, 12345, 0, 0]],
stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.01, sell_r=SellType.STOP_LOSS)
# Test 2 Minus 4% Low, minus 1% close
# Candle Data for test 2
# Test with Stop-Loss at 3%
# TC2: Stop-Loss Triggered 3% Loss
tc2 = BTContainer(data=DataFrame([
[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
[getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9925, 9950, 12345, 0, 0],
[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9600, 9925, 12345, 0, 0],
[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9875, 9900, 12345, 0, 0],
[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
], columns=columns),
tc2 = BTContainer(data=[
[0, 10000, 10050, 9950, 9975, 12345, 1, 0],
[1, 10000, 10050, 9950, 9975, 12345, 0, 0],
[2, 9975, 10025, 9925, 9950, 12345, 0, 0],
[3, 9950, 10000, 9600, 9925, 12345, 0, 0],
[4, 9925, 9975, 9875, 9900, 12345, 0, 0],
[5, 9900, 9950, 9850, 9900, 12345, 0, 0]],
stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.03, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.007, sell_r=SellType.FORCE_SELL) #
@ -99,15 +99,14 @@ tc2 = BTContainer(data=DataFrame([
# Test with Stop-Loss at 2%
# TC3: Trade-A: Stop-Loss Triggered 2% Loss
# Trade-B: Stop-Loss Triggered 2% Loss
tc3 = BTContainer(data=DataFrame([
[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
[getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9600, 9950, 12345, 0, 0],
[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 1, 0],
[getdate('2018-06-10 11:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0],
[getdate('2018-06-10 12:00:00').datetime, 9925, 9975, 8000, 8000, 12345, 0, 0],
[getdate('2018-06-10 13:00:00').datetime, 9900, 9950, 9950, 9900, 12345, 0, 0]
], columns=columns),
tc3 = BTContainer(data=[
[0, 10000, 10050, 9950, 9975, 12345, 1, 0],
[1, 10000, 10050, 9950, 9975, 12345, 0, 0],
[2, 9975, 10025, 9600, 9950, 12345, 0, 0],
[3, 9950, 10000, 9900, 9925, 12345, 1, 0],
[4, 9950, 10000, 9900, 9925, 12345, 0, 0],
[5, 9925, 9975, 8000, 8000, 12345, 0, 0],
[6, 9900, 9950, 9950, 9900, 12345, 0, 0]],
stop_loss=-0.02, roi=1, trades=2, profit_perc=-0.04, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.02, roi=1, trades=1, profit_perc=-0.02, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.02, roi=1, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
@ -117,14 +116,13 @@ tc3 = BTContainer(data=DataFrame([
# Candle Data for test 4 – Candle drops 3% Closed 15% up
# Test with Stop-loss at 2% ROI 6%
# TC4: Stop-Loss Triggered 2% Loss
tc4 = BTContainer(data=DataFrame([
[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
[getdate('2018-06-10 09:00:00').datetime, 9975, 11500, 9700, 11500, 12345, 0, 0],
[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0],
[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9875, 9900, 12345, 0, 0],
[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
], columns=columns),
tc4 = BTContainer(data=[
[0, 10000, 10050, 9950, 9975, 12345, 1, 0],
[1, 10000, 10050, 9950, 9975, 12345, 0, 0],
[2, 9975, 11500, 9700, 11500, 12345, 0, 0],
[3, 9950, 10000, 9900, 9925, 12345, 0, 0],
[4, 9925, 9975, 9875, 9900, 12345, 0, 0],
[5, 9900, 9950, 9850, 9900, 12345, 0, 0]],
stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.02, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL)
@ -132,14 +130,13 @@ tc4 = BTContainer(data=DataFrame([
# Candle Data for test 5
# Set stop-loss at 1% ROI 3%
# TC5: ROI triggers 3% Gain
tc5 = BTContainer(data=DataFrame([
[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9960, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9960, 9975, 12345, 0, 0],
[getdate('2018-06-10 09:00:00').datetime, 9975, 10050, 9950, 9975, 12345, 0, 0],
[getdate('2018-06-10 10:00:00').datetime, 9950, 12000, 9950, 12000, 12345, 0, 0],
[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0],
[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
], columns=columns),
tc5 = BTContainer(data=[
[0, 10000, 10050, 9960, 9975, 12345, 1, 0],
[1, 10000, 10050, 9960, 9975, 12345, 0, 0],
[2, 9975, 10050, 9950, 9975, 12345, 0, 0],
[3, 9950, 12000, 9950, 12000, 12345, 0, 0],
[4, 9925, 9975, 9945, 9900, 12345, 0, 0],
[5, 9900, 9950, 9850, 9900, 12345, 0, 0]],
stop_loss=-0.01, roi=0.03, trades=1, profit_perc=0.03, sell_r=SellType.ROI) #should be
# stop_loss=-0.01, roi=0.03, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL)
@ -147,14 +144,13 @@ tc5 = BTContainer(data=DataFrame([
# Candle Data for test 6
# Set stop-loss at 2% ROI at 5%
# TC6: Stop-Loss triggers 2% Loss
tc6 = BTContainer(data=DataFrame([
[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
[getdate('2018-06-10 09:00:00').datetime, 9975, 10600, 9700, 10100, 12345, 0, 0],
[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0],
[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0],
[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
], columns=columns),
tc6 = BTContainer(data=[
[0, 10000, 10050, 9950, 9975, 12345, 1, 0],
[1, 10000, 10050, 9950, 9975, 12345, 0, 0],
[2, 9975, 10600, 9700, 10100, 12345, 0, 0],
[3, 9950, 10000, 9900, 9925, 12345, 0, 0],
[4, 9925, 9975, 9945, 9900, 12345, 0, 0],
[5, 9900, 9950, 9850, 9900, 12345, 0, 0]],
stop_loss=-0.02, roi=0.05, trades=1, profit_perc=-0.02, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.02, roi=0.05, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
@ -162,14 +158,13 @@ tc6 = BTContainer(data=DataFrame([
# Candle Data for test 7
# Set stop-loss at 2% ROI at 3%
# TC7: ROI Triggers 3% Gain
tc7 = BTContainer(data=DataFrame([
[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
[getdate('2018-06-10 09:00:00').datetime, 9975, 10600, 9900, 10100, 12345, 0, 0],
[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0],
[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0],
[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
], columns=columns),
tc7 = BTContainer(data=[
[0, 10000, 10050, 9950, 9975, 12345, 1, 0],
[1, 10000, 10050, 9950, 9975, 12345, 0, 0],
[2, 9975, 10600, 9900, 10100, 12345, 0, 0],
[3, 9950, 10000, 9900, 9925, 12345, 0, 0],
[4, 9925, 9975, 9945, 9900, 12345, 0, 0],
[5, 9900, 9950, 9850, 9900, 12345, 0, 0]],
stop_loss=-0.02, roi=0.03, trades=1, profit_perc=0.03, sell_r=SellType.ROI) #should be
# stop_loss=-0.02, roi=0.03, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
@ -198,10 +193,10 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
# TODO: don't Mock fee to for now
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.0))
patch_exchange(mocker)
frame = _build_dataframe(data.data)
backtesting = Backtesting(default_conf)
backtesting.advise_buy = lambda a, m: data.data
backtesting.advise_sell = lambda a, m: data.data
backtesting.advise_buy = lambda a, m: frame
backtesting.advise_sell = lambda a, m: frame
caplog.set_level(logging.DEBUG)
pair = 'UNITTEST/BTC'