Move Analyze to a class

freqtrade/analyze.py

@@ -1,121 +1,190 @@
 """
 Functions to analyze ticker data with indicators and produce buy and sell signals
 """
-import logging
-from datetime import timedelta
-from enum import Enum
-from typing import Dict, List
-
 import arrow
+from datetime import datetime, timedelta
+from enum import Enum
 from pandas import DataFrame, to_datetime
-
+from typing import Dict, List
 from freqtrade.exchange import get_ticker_history
+from freqtrade.logger import Logger
 from freqtrade.strategy.strategy import Strategy
-
+from freqtrade.persistence import Trade
-logger = logging.getLogger(__name__)
 
 
 class SignalType(Enum):
-    """ Enum to distinguish between buy and sell signals """
+    """
+    Enum to distinguish between buy and sell signals
+    """
     BUY = "buy"
     SELL = "sell"
 
 
-def parse_ticker_dataframe(ticker: list) -> DataFrame:
+class Analyze(object):
     """
-    Analyses the trend for the given ticker history
+    Analyze class contains everything the bot need to determine if the situation is good for
-    :param ticker: See exchange.get_ticker_history
+    buying or selling.
-    :return: DataFrame
     """
-    columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
+    def __init__(self, config: dict) -> None:
-    frame = DataFrame(ticker) \
+        """
-        .rename(columns=columns)
+        Init Analyze
-    if 'BV' in frame:
+        :param config: Bot configuration (use the one from Configuration())
-        frame.drop('BV', 1, inplace=True)
+        """
-    frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
+        self.logger = Logger(name=__name__).get_logger()
-    frame.sort_values('date', inplace=True)
-    return frame
 
+        self.config = config
+        self.strategy = Strategy()
+        self.strategy.init(self.config)
 
-def populate_indicators(dataframe: DataFrame) -> DataFrame:
+    @staticmethod
-    """
+    def parse_ticker_dataframe(ticker: list) -> DataFrame:
-    Adds several different TA indicators to the given DataFrame
+        """
+        Analyses the trend for the given ticker history
+        :param ticker: See exchange.get_ticker_history
+        :return: DataFrame
+        """
+        columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
+        frame = DataFrame(ticker) \
+            .rename(columns=columns)
+        if 'BV' in frame:
+            frame.drop('BV', 1, inplace=True)
+        frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
+        frame.sort_values('date', inplace=True)
+        return frame
 
-    Performance Note: For the best performance be frugal on the number of indicators
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-    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.
+        Adds several different TA indicators to the given DataFrame
-    """
-    strategy = Strategy()
-    return strategy.populate_indicators(dataframe=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.
+        """
 
-def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+        return self.strategy.populate_indicators(dataframe=dataframe)
-    """
-    Based on TA indicators, populates the buy signal for the given dataframe
-    :param dataframe: DataFrame
-    :return: DataFrame with buy column
-    """
-    strategy = Strategy()
-    return strategy.populate_buy_trend(dataframe=dataframe)
 
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        return self.strategy.populate_buy_trend(dataframe=dataframe)
 
-def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
-    """
+        """
-    Based on TA indicators, populates the sell signal for the given dataframe
+        Based on TA indicators, populates the sell signal for the given dataframe
-    :param dataframe: DataFrame
+        :param dataframe: DataFrame
-    :return: DataFrame with buy column
+        :return: DataFrame with buy column
-    """
+        """
-    strategy = Strategy()
+        return self.strategy.populate_sell_trend(dataframe=dataframe)
-    return strategy.populate_sell_trend(dataframe=dataframe)
 
+    def analyze_ticker(self, ticker_history: List[Dict]) -> DataFrame:
+        """
+        Parses the given ticker history and returns a populated DataFrame
+        add several TA indicators and buy signal to it
+        :return DataFrame with ticker data and indicator data
+        """
+        dataframe = self.parse_ticker_dataframe(ticker_history)
+        dataframe = self.populate_indicators(dataframe)
+        dataframe = self.populate_buy_trend(dataframe)
+        dataframe = self.populate_sell_trend(dataframe)
+        return dataframe
 
-def analyze_ticker(ticker_history: List[Dict]) -> DataFrame:
+    # FIX: Maybe return False, if an error has occured,
-    """
+    #      Otherwise we might mask an error as an non-signal-scenario
-    Parses the given ticker history and returns a populated DataFrame
+    def get_signal(self, pair: str, interval: int) -> (bool, bool):
-    add several TA indicators and buy signal to it
+        """
-    :return DataFrame with ticker data and indicator data
+        Calculates current signal based several technical analysis indicators
-    """
+        :param pair: pair in format BTC_ANT or BTC-ANT
-    dataframe = parse_ticker_dataframe(ticker_history)
+        :return: (Buy, Sell) A bool-tuple indicating buy/sell signal
-    dataframe = populate_indicators(dataframe)
+        """
-    dataframe = populate_buy_trend(dataframe)
+        ticker_hist = get_ticker_history(pair, interval)
-    dataframe = populate_sell_trend(dataframe)
+        if not ticker_hist:
-    return dataframe
+            self.logger.warning('Empty ticker history for pair %s', pair)
+            return (False, False)  # return False ?
 
+        try:
+            dataframe = self.analyze_ticker(ticker_hist)
+        except ValueError as error:
+            self.logger.warning(
+                'Unable to analyze ticker for pair %s: %s',
+                pair,
+                str(error)
+            )
+            return (False, False)  # return False ?
+        except Exception as error:
+            self.logger.exception(
+                'Unexpected error when analyzing ticker for pair %s: %s',
+                pair,
+                str(error)
+            )
+            return (False, False)  # return False ?
 
-# FIX: Maybe return False, if an error has occured,
+        if dataframe.empty:
-#      Otherwise we might mask an error as an non-signal-scenario
+            self.logger.warning('Empty dataframe for pair %s', pair)
-def get_signal(pair: str, interval: int) -> (bool, bool):
+            return (False, False)  # return False ?
-    """
-    Calculates current signal based several technical analysis indicators
-    :param pair: pair in format BTC_ANT or BTC-ANT
-    :return: (Buy, Sell) A bool-tuple indicating buy/sell signal
-    """
-    ticker_hist = get_ticker_history(pair, interval)
-    if not ticker_hist:
-        logger.warning('Empty ticker history for pair %s', pair)
-        return (False, False)  # return False ?
 
-    try:
+        latest = dataframe.iloc[-1]
-        dataframe = analyze_ticker(ticker_hist)
-    except ValueError as ex:
-        logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
-        return (False, False)  # return False ?
-    except Exception as ex:
-        logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
-        return (False, False)  # return False ?
 
-    if dataframe.empty:
+        # Check if dataframe is out of date
-        logger.warning('Empty dataframe for pair %s', pair)
+        signal_date = arrow.get(latest['date'])
-        return (False, False)  # return False ?
+        if signal_date < arrow.now() - timedelta(minutes=(interval + 5)):
+            self.logger.warning('Too old dataframe for pair %s', pair)
+            return (False, False)  # return False ?
 
-    latest = dataframe.iloc[-1]
+        (buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
+        self.logger.debug(
+            'trigger: %s (pair=%s) buy=%s sell=%s',
+            latest['date'],
+            pair,
+            str(buy),
+            str(sell)
+        )
+        return (buy, sell)
 
-    # Check if dataframe is out of date
+    def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool, sell: bool) -> bool:
-    signal_date = arrow.get(latest['date'])
+        """
-    if signal_date < arrow.now() - timedelta(minutes=(interval + 5)):
+        This function evaluate if on the condition required to trigger a sell has been reached
-        logger.warning('Too old dataframe for pair %s', pair)
+        if the threshold is reached and updates the trade record.
-        return (False, False)  # return False ?
+        :return: True if trade should be sold, False otherwise
+        """
+        # Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
+        if self.min_roi_reached(trade=trade, current_rate=rate, current_time=date):
+            self.logger.debug('Executing sell due to ROI ...')
+            return True
 
-    (buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
+        # Experimental: Check if the trade is profitable before selling it (avoid selling at loss)
-    logger.debug('trigger: %s (pair=%s) buy=%s sell=%s', latest['date'], pair, str(buy), str(sell))
+        if self.config.get('experimental', {}).get('sell_profit_only', False):
-    return (buy, sell)
+            self.logger.debug('Checking if trade is profitable ...')
+            if trade.calc_profit(rate=rate) <= 0:
+                return False
+
+        if sell and not buy and self.config.get('experimental', {}).get('use_sell_signal', False):
+            self.logger.debug('Executing sell due to sell signal ...')
+            return True
+
+        return False
+
+    def min_roi_reached(self, trade: Trade, current_rate: float, current_time: datetime) -> bool:
+        """
+        Based an earlier trade and current price and ROI configuration, decides whether bot should
+        sell
+        :return True if bot should sell at current rate
+        """
+        current_profit = trade.calc_profit_percent(current_rate)
+        if self.strategy.stoploss is not None and current_profit < float(self.strategy.stoploss):
+            self.logger.debug('Stop loss hit.')
+            return True
+
+        # Check if time matches and current rate is above threshold
+        time_diff = (current_time - trade.open_date).total_seconds() / 60
+        for duration, threshold in sorted(self.strategy.minimal_roi.items()):
+            if time_diff > float(duration) and current_profit > threshold:
+                return True
+
+        self.logger.debug(
+            'Threshold not reached. (cur_profit: %1.2f%%)',
+            float(current_profit) * 100.0
+        )
+        return False

freqtrade/tests/test_analyze.py

@@ -1,16 +1,45 @@
 # pragma pylint: disable=missing-docstring, C0103
+
+"""
+Unit test file for analyse.py
+"""
+
 import datetime
 from unittest.mock import MagicMock
-
-import arrow
 import logging
+import arrow
 from pandas import DataFrame
 
 import freqtrade.tests.conftest as tt  # test tools
-from freqtrade.analyze import (get_signal, parse_ticker_dataframe,
+from freqtrade.analyze import Analyze, SignalType
-                               populate_buy_trend, populate_indicators,
+
-                               populate_sell_trend)
+
-from freqtrade.strategy.strategy import Strategy
+# Avoid to reinit the same object again and again
+_ANALYZE = Analyze({'strategy': 'default_strategy'})
+
+
+def test_signaltype_object() -> None:
+    """
+    Test the SignalType object has the mandatory Constants
+    :return: None
+    """
+    assert hasattr(SignalType, 'BUY')
+    assert hasattr(SignalType, 'SELL')
+
+
+def test_analyze_object() -> None:
+    """
+    Test the Analyze object has the mandatory methods
+    :return: None
+    """
+    assert hasattr(Analyze, 'parse_ticker_dataframe')
+    assert hasattr(Analyze, 'populate_indicators')
+    assert hasattr(Analyze, 'populate_buy_trend')
+    assert hasattr(Analyze, 'populate_sell_trend')
+    assert hasattr(Analyze, 'analyze_ticker')
+    assert hasattr(Analyze, 'get_signal')
+    assert hasattr(Analyze, 'should_sell')
+    assert hasattr(Analyze, 'min_roi_reached')
 
 
 def test_dataframe_correct_columns(result):
@@ -18,71 +47,75 @@ def test_dataframe_correct_columns(result):
         ['close', 'high', 'low', 'open', 'date', 'volume']
 
 
-def test_dataframe_correct_length(result):
-    # no idea what this check truly does - should we just remove it?
-    assert len(result.index) == 14397
-
-
 def test_populates_buy_trend(result):
     # Load the default strategy for the unit test, because this logic is done in main.py
-    Strategy().init({'strategy': 'default_strategy'})
+    dataframe = _ANALYZE.populate_buy_trend(_ANALYZE.populate_indicators(result))
-
-    dataframe = populate_buy_trend(populate_indicators(result))
     assert 'buy' in dataframe.columns
 
 
 def test_populates_sell_trend(result):
     # Load the default strategy for the unit test, because this logic is done in main.py
-    Strategy().init({'strategy': 'default_strategy'})
+    dataframe = _ANALYZE.populate_sell_trend(_ANALYZE.populate_indicators(result))
-
-    dataframe = populate_sell_trend(populate_indicators(result))
     assert 'sell' in dataframe.columns
 
 
 def test_returns_latest_buy_signal(mocker):
     mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
-    mocker.patch(
-        'freqtrade.analyze.analyze_ticker',
-        return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
-    )
-    assert get_signal('BTC-ETH', 5) == (True, False)
 
-    mocker.patch(
+    mocker.patch.multiple(
-        'freqtrade.analyze.analyze_ticker',
+        'freqtrade.analyze.Analyze',
-        return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
+        analyze_ticker=MagicMock(
+            return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
+        )
     )
-    assert get_signal('BTC-ETH', 5) == (False, True)
+    assert _ANALYZE.get_signal('BTC-ETH', 5) == (True, False)
+
+    mocker.patch.multiple(
+        'freqtrade.analyze.Analyze',
+        analyze_ticker=MagicMock(
+            return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
+        )
+    )
+    assert _ANALYZE.get_signal('BTC-ETH', 5) == (False, True)
 
 
 def test_returns_latest_sell_signal(mocker):
     mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
-    mocker.patch(
+    mocker.patch.multiple(
-        'freqtrade.analyze.analyze_ticker',
+        'freqtrade.analyze.Analyze',
-        return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
+        analyze_ticker=MagicMock(
+            return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
+        )
     )
-    assert get_signal('BTC-ETH', 5) == (False, True)
 
-    mocker.patch(
+    assert _ANALYZE.get_signal('BTC-ETH', 5) == (False, True)
-        'freqtrade.analyze.analyze_ticker',
+
-        return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
+    mocker.patch.multiple(
+        'freqtrade.analyze.Analyze',
+        analyze_ticker=MagicMock(
+            return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
+        )
     )
-    assert get_signal('BTC-ETH', 5) == (True, False)
+    assert _ANALYZE.get_signal('BTC-ETH', 5) == (True, False)
 
 
 def test_get_signal_empty(default_conf, mocker, caplog):
     caplog.set_level(logging.INFO)
     mocker.patch('freqtrade.analyze.get_ticker_history', return_value=None)
-    assert (False, False) == get_signal('foo', int(default_conf['ticker_interval']))
+    assert (False, False) == _ANALYZE.get_signal('foo', int(default_conf['ticker_interval']))
-    assert tt.log_has('Empty ticker history for pair foo',
+    assert tt.log_has('Empty ticker history for pair foo', caplog.record_tuples)
-                      caplog.record_tuples)
 
 
 def test_get_signal_exception_valueerror(default_conf, mocker, caplog):
     caplog.set_level(logging.INFO)
     mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
-    mocker.patch('freqtrade.analyze.analyze_ticker',
+    mocker.patch.multiple(
-                 side_effect=ValueError('xyz'))
+        'freqtrade.analyze.Analyze',
-    assert (False, False) == get_signal('foo', int(default_conf['ticker_interval']))
+        analyze_ticker=MagicMock(
+            side_effect=ValueError('xyz')
+        )
+    )
+    assert (False, False) == _ANALYZE.get_signal('foo', int(default_conf['ticker_interval']))
     assert tt.log_has('Unable to analyze ticker for pair foo: xyz',
                       caplog.record_tuples)
 
@@ -90,8 +123,13 @@ def test_get_signal_exception_valueerror(default_conf, mocker, caplog):
 def test_get_signal_empty_dataframe(default_conf, mocker, caplog):
     caplog.set_level(logging.INFO)
     mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
-    mocker.patch('freqtrade.analyze.analyze_ticker', return_value=DataFrame([]))
+    mocker.patch.multiple(
-    assert (False, False) == get_signal('xyz', int(default_conf['ticker_interval']))
+        'freqtrade.analyze.Analyze',
+        analyze_ticker=MagicMock(
+            return_value=DataFrame([])
+        )
+    )
+    assert (False, False) == _ANALYZE.get_signal('xyz', int(default_conf['ticker_interval']))
     assert tt.log_has('Empty dataframe for pair xyz',
                       caplog.record_tuples)
 
@@ -102,27 +140,36 @@ def test_get_signal_old_dataframe(default_conf, mocker, caplog):
     # FIX: The get_signal function has hardcoded 10, which we must inturn hardcode
     oldtime = arrow.utcnow() - datetime.timedelta(minutes=11)
     ticks = DataFrame([{'buy': 1, 'date': oldtime}])
-    mocker.patch('freqtrade.analyze.analyze_ticker', return_value=DataFrame(ticks))
+    mocker.patch.multiple(
-    assert (False, False) == get_signal('xyz', int(default_conf['ticker_interval']))
+        'freqtrade.analyze.Analyze',
+        analyze_ticker=MagicMock(
+            return_value=DataFrame(ticks)
+        )
+    )
+    assert (False, False) == _ANALYZE.get_signal('xyz', int(default_conf['ticker_interval']))
     assert tt.log_has('Too old dataframe for pair xyz',
                       caplog.record_tuples)
 
 
 def test_get_signal_handles_exceptions(mocker):
     mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
-    mocker.patch('freqtrade.analyze.analyze_ticker',
+    mocker.patch.multiple(
-                 side_effect=Exception('invalid ticker history '))
+        'freqtrade.analyze.Analyze',
+        analyze_ticker=MagicMock(
+            side_effect=Exception('invalid ticker history ')
+        )
+    )
 
-    assert get_signal('BTC-ETH', 5) == (False, False)
+    assert _ANALYZE.get_signal('BTC-ETH', 5) == (False, False)
 
 
 def test_parse_ticker_dataframe(ticker_history, ticker_history_without_bv):
     columns = ['close', 'high', 'low', 'open', 'date', 'volume']
 
     # Test file with BV data
-    dataframe = parse_ticker_dataframe(ticker_history)
+    dataframe = Analyze.parse_ticker_dataframe(ticker_history)
     assert dataframe.columns.tolist() == columns
 
     # Test file without BV data
-    dataframe = parse_ticker_dataframe(ticker_history_without_bv)
+    dataframe = Analyze.parse_ticker_dataframe(ticker_history_without_bv)
     assert dataframe.columns.tolist() == columns