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https://github.com/freqtrade/freqtrade.git
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Remove unused test-strategy
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Matthias
parent
a0b9388757
commit
2595e40e47
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@ -1402,7 +1402,6 @@ def test_api_strategies(botclient):
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'InformativeDecoratorTest',
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'StrategyTestV2',
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'StrategyTestV3',
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'StrategyTestV3Analysis',
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'StrategyTestV3Futures'
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]}
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@ -1,175 +0,0 @@
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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import talib.abstract as ta
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from pandas import DataFrame
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
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RealParameter)
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class StrategyTestV3Analysis(IStrategy):
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"""
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Strategy used by tests freqtrade bot.
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Please do not modify this strategy, it's intended for internal use only.
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Please look at the SampleStrategy in the user_data/strategy directory
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or strategy repository https://github.com/freqtrade/freqtrade-strategies
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for samples and inspiration.
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"""
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INTERFACE_VERSION = 3
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# Minimal ROI designed for the strategy
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minimal_roi = {
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"40": 0.0,
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"30": 0.01,
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"20": 0.02,
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"0": 0.04
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}
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# Optimal stoploss designed for the strategy
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stoploss = -0.10
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# Optimal timeframe for the strategy
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timeframe = '5m'
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# Optional order type mapping
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order_types = {
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'entry': 'limit',
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'exit': 'limit',
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'stoploss': 'limit',
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'stoploss_on_exchange': False
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}
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 20
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# Optional time in force for orders
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order_time_in_force = {
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'entry': 'gtc',
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'exit': 'gtc',
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}
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buy_params = {
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'buy_rsi': 35,
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# Intentionally not specified, so "default" is tested
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# 'buy_plusdi': 0.4
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}
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sell_params = {
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'sell_rsi': 74,
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'sell_minusdi': 0.4
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}
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buy_rsi = IntParameter([0, 50], default=30, space='buy')
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buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
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sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
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sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
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load=False)
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protection_enabled = BooleanParameter(default=True)
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protection_cooldown_lookback = IntParameter([0, 50], default=30)
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# TODO: Can this work with protection tests? (replace HyperoptableStrategy implicitly ... )
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# @property
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# def protections(self):
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# prot = []
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# if self.protection_enabled.value:
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# prot.append({
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# "method": "CooldownPeriod",
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# "stop_duration_candles": self.protection_cooldown_lookback.value
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# })
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# return prot
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bot_started = False
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def bot_start(self):
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self.bot_started = True
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def informative_pairs(self):
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return []
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Momentum Indicator
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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# Minus Directional Indicator / Movement
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Plus Directional Indicator / Movement
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Stoch fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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# EMA - Exponential Moving Average
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe['rsi'] < self.buy_rsi.value) &
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(dataframe['fastd'] < 35) &
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(dataframe['adx'] > 30) &
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(dataframe['plus_di'] > self.buy_plusdi.value)
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) |
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(
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(dataframe['adx'] > 65) &
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(dataframe['plus_di'] > self.buy_plusdi.value)
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),
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['enter_long', 'enter_tag']] = 1, 'enter_tag_long'
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dataframe.loc[
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(
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qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
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),
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['enter_short', 'enter_tag']] = 1, 'enter_tag_short'
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(
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(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
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(qtpylib.crossed_above(dataframe['fastd'], 70))
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) &
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(dataframe['adx'] > 10) &
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(dataframe['minus_di'] > 0)
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) |
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(
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(dataframe['adx'] > 70) &
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(dataframe['minus_di'] > self.sell_minusdi.value)
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),
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['exit_long', 'exit_tag']] = 1, 'exit_tag_long'
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dataframe.loc[
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(
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qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
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),
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['exit_long', 'exit_tag']] = 1, 'exit_tag_short'
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return dataframe
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@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
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directory = Path(__file__).parent / "strats"
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strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
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assert isinstance(strategies, list)
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assert len(strategies) == 7
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assert len(strategies) == 6
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assert isinstance(strategies[0], dict)
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@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
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directory = Path(__file__).parent / "strats"
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strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
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assert isinstance(strategies, list)
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assert len(strategies) == 8
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assert len(strategies) == 7
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# with enum_failed=True search_all_objects() shall find 2 good strategies
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# and 1 which fails to load
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assert len([x for x in strategies if x['class'] is not None]) == 7
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assert len([x for x in strategies if x['class'] is not None]) == 6
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assert len([x for x in strategies if x['class'] is None]) == 1
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