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Merge branch 'develop' into test_coverage

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kryofly
2018-01-19 08:05:52 +01:00
parent fc2e8b321f a4b8db38ca
commit e3088647fc
28 changed files with 743 additions and 308 deletions
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39
freqtrade/analyze.py
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@@ -74,6 +74,8 @@ def populate_indicators(dataframe: DataFrame) -> 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)
@@ -114,13 +116,14 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
"""
# 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)
@@ -210,14 +213,12 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
# 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
@@ -280,29 +281,38 @@ def analyze_ticker(ticker_history: List[Dict]) -> DataFrame:
return dataframe
def get_signal(pair: str, signal: SignalType) -> bool:
# FIX: 20180109, there could be some confusion because we will make a
# boolean result (execute the action or not depending on the signal).
# But the above checks can also return False, and we hide that.
# 20180119 Update to above fix, after an code update we now return
# a tuple (buy, sell). We could take advantage of this
# To distinguish an error from an non-signal situation (False, False)
# by just returning False.
# In short, if we return False it is error, If a tuple we
# get the signal situation.
def get_signal(pair: str) -> (bool, bool):
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format BTC_ANT or BTC-ANT
:return: True if pair is good for buying, False otherwise
:return: (True, False) if pair is good for buying and not for selling
"""
ticker_hist = get_ticker_history(pair)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False
return (False, False)
try:
dataframe = analyze_ticker(ticker_hist)
except ValueError as ex:
logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
return False
return (False, False)
except Exception as ex:
logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
return False
return (False, False)
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False
return (False, False)
latest = dataframe.iloc[-1]
@@ -310,11 +320,8 @@ def get_signal(pair: str, signal: SignalType) -> bool:
signal_date = arrow.get(latest['date'])
if signal_date < arrow.now() - timedelta(minutes=10):
logger.warning('Too old dataframe for pair %s', pair)
return False
return (False, False)
# FIX: 20180109, there could be some confusion because we will make a
# boolean result (execute the action or not depending on the signal).
# But the above checks can also return False, and we hide that.
result = latest[signal.value] == 1
logger.debug('%s_trigger: %s (pair=%s, signal=%s)', signal.value, latest['date'], pair, result)
return result
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s', latest['date'], pair, str(buy), str(sell))
return (buy, sell)