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Merge branch 'develop' into feat/new_args_system

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This commit is contained in:
Matthias
2019-10-20 19:32:34 +02:00
parent df43b1f533 a52366c45d
commit f3cfe147b5
100 changed files with 2632 additions and 1112 deletions
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158
tests/strategy/legacy_strategy.py
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@@ -6,8 +6,6 @@ from pandas import DataFrame
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
# This class is a sample. Feel free to customize it.
@@ -17,7 +15,6 @@ class TestStrategyLegacy(IStrategy):
removed in a future update.
Please do not use this as a template, but refer to user_data/strategy/sample_strategy.py
for a uptodate version of this template.
"""
# Minimal ROI designed for the strategy.
@@ -51,156 +48,9 @@ class TestStrategyLegacy(IStrategy):
# ADX
dataframe['adx'] = ta.ADX(dataframe)
"""
# Awesome oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# ROC
dataframe['roc'] = ta.ROC(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
"""
# Overlap Studies
# ------------------------------------
# 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['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
"""
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
"""
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
"""
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
@@ -212,8 +62,8 @@ class TestStrategyLegacy(IStrategy):
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
(dataframe['tema'] > dataframe['tema'].shift(1)) &
(dataframe['volume'] > 0)
),
'buy'] = 1
@@ -228,8 +78,8 @@ class TestStrategyLegacy(IStrategy):
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
(dataframe['tema'] < dataframe['tema'].shift(1)) &
(dataframe['volume'] > 0)
),
'sell'] = 1
return dataframe

2
tests/strategy/test_interface.py
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@@ -106,7 +106,7 @@ def test_get_signal_handles_exceptions(mocker, default_conf):
def test_tickerdata_to_dataframe(default_conf, testdatadir) -> None:
strategy = DefaultStrategy(default_conf)
timerange = TimeRange(None, 'line', 0, -100)
timerange = TimeRange.parse_timerange('1510694220-1510700340')
tick = load_tickerdata_file(testdatadir, 'UNITTEST/BTC', '1m', timerange=timerange)
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick, '1m', pair="UNITTEST/BTC",
fill_missing=True)}

27
tests/strategy/test_strategy.py
View File

@@ -1,6 +1,5 @@
# pragma pylint: disable=missing-docstring, protected-access, C0103
import logging
import tempfile
import warnings
from base64 import urlsafe_b64encode
from os import path
@@ -39,7 +38,7 @@ def test_search_strategy():
def test_load_strategy(default_conf, result):
default_conf.update({'strategy': 'SampleStrategy'})
resolver = StrategyResolver(default_conf)
assert 'adx' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
assert 'rsi' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
def test_load_strategy_base64(result, caplog, default_conf):
@@ -48,10 +47,10 @@ def test_load_strategy_base64(result, caplog, default_conf):
default_conf.update({'strategy': 'SampleStrategy:{}'.format(encoded_string)})
resolver = StrategyResolver(default_conf)
assert 'adx' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
assert 'rsi' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
# Make sure strategy was loaded from base64 (using temp directory)!!
assert log_has_re(r"Using resolved strategy SampleStrategy from '"
+ tempfile.gettempdir() + r"/.*/SampleStrategy\.py'\.\.\.", caplog)
r".*(/|\\).*(/|\\)SampleStrategy\.py'\.\.\.", caplog)
def test_load_strategy_invalid_directory(result, caplog, default_conf):
@@ -265,23 +264,23 @@ def test_strategy_override_use_sell_signal(caplog, default_conf):
'strategy': 'DefaultStrategy',
})
resolver = StrategyResolver(default_conf)
assert not resolver.strategy.use_sell_signal
assert resolver.strategy.use_sell_signal
assert isinstance(resolver.strategy.use_sell_signal, bool)
# must be inserted to configuration
assert 'use_sell_signal' in default_conf['experimental']
assert not default_conf['experimental']['use_sell_signal']
assert 'use_sell_signal' in default_conf['ask_strategy']
assert default_conf['ask_strategy']['use_sell_signal']
default_conf.update({
'strategy': 'DefaultStrategy',
'experimental': {
'use_sell_signal': True,
'ask_strategy': {
'use_sell_signal': False,
},
})
resolver = StrategyResolver(default_conf)
assert resolver.strategy.use_sell_signal
assert not resolver.strategy.use_sell_signal
assert isinstance(resolver.strategy.use_sell_signal, bool)
assert log_has("Override strategy 'use_sell_signal' with value in config file: True.", caplog)
assert log_has("Override strategy 'use_sell_signal' with value in config file: False.", caplog)
def test_strategy_override_use_sell_profit_only(caplog, default_conf):
@@ -293,12 +292,12 @@ def test_strategy_override_use_sell_profit_only(caplog, default_conf):
assert not resolver.strategy.sell_profit_only
assert isinstance(resolver.strategy.sell_profit_only, bool)
# must be inserted to configuration
assert 'sell_profit_only' in default_conf['experimental']
assert not default_conf['experimental']['sell_profit_only']
assert 'sell_profit_only' in default_conf['ask_strategy']
assert not default_conf['ask_strategy']['sell_profit_only']
default_conf.update({
'strategy': 'DefaultStrategy',
'experimental': {
'ask_strategy': {
'sell_profit_only': True,
},
})