Fix all tests

This commit is contained in:
froggleston 2022-05-24 21:04:23 +01:00
parent 3adda84b96
commit 22b9805e47
5 changed files with 209 additions and 10 deletions

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@ -69,7 +69,7 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, cap
'--export', 'signals', '--export', 'signals',
'--cache', 'none', '--cache', 'none',
'--strategy-list', '--strategy-list',
'StrategyTestV3', 'StrategyTestV3Analysis',
] ]
args = get_args(args) args = get_args(args)
start_backtesting(args) start_backtesting(args)
@ -85,7 +85,7 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, cap
'--datadir', str(testdatadir), '--datadir', str(testdatadir),
'--analysis_groups', '0', '--analysis_groups', '0',
'--strategy', '--strategy',
'StrategyTestV3', 'StrategyTestV3Analysis',
] ]
args = get_args(args) args = get_args(args)
start_analysis_entries_exits(args) start_analysis_entries_exits(args)

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@ -1384,12 +1384,16 @@ def test_api_strategies(botclient):
rc = client_get(client, f"{BASE_URI}/strategies") rc = client_get(client, f"{BASE_URI}/strategies")
assert_response(rc) assert_response(rc)
print(rc.json())
assert rc.json() == {'strategies': [ assert rc.json() == {'strategies': [
'HyperoptableStrategy', 'HyperoptableStrategy',
'InformativeDecoratorTest', 'InformativeDecoratorTest',
'StrategyTestV2', 'StrategyTestV2',
'StrategyTestV3', 'StrategyTestV3',
'StrategyTestV3Futures', 'StrategyTestV3Analysis',
'StrategyTestV3Futures'
]} ]}

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@ -143,13 +143,13 @@ class StrategyTestV3(IStrategy):
(dataframe['adx'] > 65) & (dataframe['adx'] > 65) &
(dataframe['plus_di'] > self.buy_plusdi.value) (dataframe['plus_di'] > self.buy_plusdi.value)
), ),
['enter_long', 'enter_tag']] = 1, 'enter_tag_long' 'enter_long'] = 1
dataframe.loc[ dataframe.loc[
( (
qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value) qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
), ),
['enter_short', 'enter_tag']] = 1, 'enter_tag_short' 'enter_short'] = 1
return dataframe return dataframe
@ -167,13 +167,13 @@ class StrategyTestV3(IStrategy):
(dataframe['adx'] > 70) & (dataframe['adx'] > 70) &
(dataframe['minus_di'] > self.sell_minusdi.value) (dataframe['minus_di'] > self.sell_minusdi.value)
), ),
['exit_long', 'exit_tag']] = 1, 'exit_tag_long' 'exit_long'] = 1
dataframe.loc[ dataframe.loc[
( (
qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value) qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
), ),
['exit_long', 'exit_tag']] = 1, 'exit_tag_short' 'exit_short'] = 1
return dataframe return dataframe

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@ -0,0 +1,195 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from datetime import datetime
import talib.abstract as ta
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
RealParameter)
class StrategyTestV3Analysis(IStrategy):
"""
Strategy used by tests freqtrade bot.
Please do not modify this strategy, it's intended for internal use only.
Please look at the SampleStrategy in the user_data/strategy directory
or strategy repository https://github.com/freqtrade/freqtrade-strategies
for samples and inspiration.
"""
INTERFACE_VERSION = 3
# Minimal ROI designed for the strategy
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
stoploss = -0.10
# Optimal timeframe for the strategy
timeframe = '5m'
# Optional order type mapping
order_types = {
'entry': 'limit',
'exit': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False
}
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 20
# Optional time in force for orders
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc',
}
buy_params = {
'buy_rsi': 35,
# Intentionally not specified, so "default" is tested
# 'buy_plusdi': 0.4
}
sell_params = {
'sell_rsi': 74,
'sell_minusdi': 0.4
}
buy_rsi = IntParameter([0, 50], default=30, space='buy')
buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
load=False)
protection_enabled = BooleanParameter(default=True)
protection_cooldown_lookback = IntParameter([0, 50], default=30)
# TODO: Can this work with protection tests? (replace HyperoptableStrategy implicitly ... )
# @property
# def protections(self):
# prot = []
# if self.protection_enabled.value:
# prot.append({
# "method": "CooldownPeriod",
# "stop_duration_candles": self.protection_cooldown_lookback.value
# })
# return prot
bot_started = False
def bot_start(self):
self.bot_started = True
def informative_pairs(self):
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# Minus Directional Indicator / Movement
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# EMA - Exponential Moving Average
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] < self.buy_rsi.value) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > self.buy_plusdi.value)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > self.buy_plusdi.value)
),
['enter_long', 'enter_tag']] = 1, 'enter_tag_long'
dataframe.loc[
(
qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
),
['enter_short', 'enter_tag']] = 1, 'enter_tag_short'
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > self.sell_minusdi.value)
),
['exit_long', 'exit_tag']] = 1, 'exit_tag_long'
dataframe.loc[
(
qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
),
['exit_long', 'exit_tag']] = 1, 'exit_tag_short'
return dataframe
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, side: str,
**kwargs) -> float:
# Return 3.0 in all cases.
# Bot-logic must make sure it's an allowed leverage and eventually adjust accordingly.
return 3.0
def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, min_stake: float, max_stake: float, **kwargs):
if current_profit < -0.0075:
orders = trade.select_filled_orders(trade.entry_side)
return round(orders[0].cost, 0)
return None

View File

@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
directory = Path(__file__).parent / "strats" directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=False) strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
assert isinstance(strategies, list) assert isinstance(strategies, list)
assert len(strategies) == 5 assert len(strategies) == 6
assert isinstance(strategies[0], dict) assert isinstance(strategies[0], dict)
@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
directory = Path(__file__).parent / "strats" directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True) strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
assert isinstance(strategies, list) assert isinstance(strategies, list)
assert len(strategies) == 6 assert len(strategies) == 7
# with enum_failed=True search_all_objects() shall find 2 good strategies # with enum_failed=True search_all_objects() shall find 2 good strategies
# and 1 which fails to load # and 1 which fails to load
assert len([x for x in strategies if x['class'] is not None]) == 5 assert len([x for x in strategies if x['class'] is not None]) == 6
assert len([x for x in strategies if x['class'] is None]) == 1 assert len([x for x in strategies if x['class'] is None]) == 1