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Merge branch 'develop' into interface_ordertimeoutcallback

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Matthias
2020-03-15 14:56:14 +01:00
parent 0f2d771634 3b89e7d393
commit a1bbeaa668
101 changed files with 2205 additions and 1228 deletions
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35
freqtrade/strategy/interface.py
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@@ -60,7 +60,7 @@ class IStrategy(ABC):
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> str: value of the ticker interval to use for the strategy
ticker_interval -> str: value of the timeframe (ticker interval) to use with the strategy
"""
# Strategy interface version
# Default to version 2
@@ -126,7 +126,7 @@ class IStrategy(ABC):
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: DataFrame with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@@ -237,11 +237,11 @@ class IStrategy(ABC):
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
:param dataframe: Dataframe containing ticker data
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
@@ -251,12 +251,12 @@ class IStrategy(ABC):
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
:param dataframe: Dataframe containing ticker data
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
pair = str(metadata.get('pair'))
@@ -288,7 +288,7 @@ class IStrategy(ABC):
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty ticker history for pair %s', pair)
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return False, False
try:
@@ -296,7 +296,7 @@ class IStrategy(ABC):
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
except StrategyError as error:
logger.warning(f"Unable to analyze ticker for pair {pair}: {error}")
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
return False, False
@@ -393,7 +393,7 @@ class IStrategy(ABC):
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not
:param current_profit: current profit in percent
:param current_profit: current profit as ratio
"""
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
@@ -456,8 +456,9 @@ class IStrategy(ABC):
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
"""
Based on trade duration, current price and ROI configuration, decides whether bot should
sell. Requires current_profit to be in percent!!
Based on trade duration, current profit of the trade and ROI configuration,
decides whether bot should sell.
:param current_profit: current profit as ratio
:return: True if bot should sell at current rate
"""
# Check if time matches and current rate is above threshold
@@ -468,19 +469,19 @@ class IStrategy(ABC):
else:
return current_profit > roi
def tickerdata_to_dataframe(self, tickerdata: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
Creates a dataframe and populates indicators for given candle (OHLCV) data
Used by optimize operations only, not during dry / live runs.
"""
return {pair: self.advise_indicators(pair_data, {'pair': pair})
for pair, pair_data in tickerdata.items()}
for pair, pair_data in data.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
This method should not be overridden.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""