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Move strategies to test subfolder

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This commit is contained in:
Matthias
2020-02-18 20:12:10 +01:00
parent 16cbd441ce
commit 1634297685
8 changed files with 12 additions and 11 deletions
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156
tests/strategy/strats/default_strategy.py Normal file
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@@ -0,0 +1,156 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy.interface import IStrategy
class DefaultStrategy(IStrategy):
"""
Default Strategy provided by 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 = 2
# 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 ticker interval for the strategy
ticker_interval = '5m'
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': '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 = {
'buy': 'gtc',
'sell': 'gtc',
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > 0.5)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > 0.5)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > 0.5)
),
'sell'] = 1
return dataframe

0
tests/strategy/failing_strategy.py → tests/strategy/strats/failing_strategy.py
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0
tests/strategy/legacy_strategy.py → tests/strategy/strats/legacy_strategy.py
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2
tests/strategy/test_default_strategy.py
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@@ -1,6 +1,6 @@
from pandas import DataFrame
from freqtrade.strategy.default_strategy import DefaultStrategy
from .strats.default_strategy import DefaultStrategy
def test_default_strategy_structure():

2
tests/strategy/test_interface.py
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@@ -11,7 +11,7 @@ from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.history import load_tickerdata_file
from freqtrade.persistence import Trade
from freqtrade.resolvers import StrategyResolver
from freqtrade.strategy.default_strategy import DefaultStrategy
from .strats.default_strategy import DefaultStrategy
from tests.conftest import get_patched_exchange, log_has
# Avoid to reinit the same object again and again

14
tests/strategy/test_strategy.py
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@@ -31,21 +31,21 @@ def test_search_strategy():
def test_search_all_strategies_no_failed():
directory = Path(__file__).parent
directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
assert isinstance(strategies, list)
assert len(strategies) == 3
assert len(strategies) == 2
assert isinstance(strategies[0], dict)
def test_search_all_strategies_with_failed():
directory = Path(__file__).parent
directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
assert isinstance(strategies, list)
assert len(strategies) == 4
assert len(strategies) == 3
# with enum_failed=True search_all_objects() shall find 3 good strategies
# and 1 which fails to load
assert len([x for x in strategies if x['class'] is not None]) == 3
assert len([x for x in strategies if x['class'] is not None]) == 2
assert len([x for x in strategies if x['class'] is None]) == 1
@@ -326,7 +326,7 @@ def test_strategy_override_use_sell_profit_only(caplog, default_conf):
@pytest.mark.filterwarnings("ignore:deprecated")
def test_deprecate_populate_indicators(result, default_conf):
default_location = path.join(path.dirname(path.realpath(__file__)))
default_location = Path(__file__).parent / "strats"
default_conf.update({'strategy': 'TestStrategyLegacy',
'strategy_path': default_location})
strategy = StrategyResolver.load_strategy(default_conf)
@@ -360,7 +360,7 @@ def test_deprecate_populate_indicators(result, default_conf):
@pytest.mark.filterwarnings("ignore:deprecated")
def test_call_deprecated_function(result, monkeypatch, default_conf):
default_location = path.join(path.dirname(path.realpath(__file__)))
default_location = Path(__file__).parent / "strats"
default_conf.update({'strategy': 'TestStrategyLegacy',
'strategy_path': default_location})
strategy = StrategyResolver.load_strategy(default_conf)