Merge pull request #1044 from freqtrade/pair_to_strat

pair to strategy enhancement

docs/bot-optimization.md

@@ -39,7 +39,6 @@ A strategy file contains all the information needed to build a good strategy:
 - Sell strategy rules
 - Minimal ROI recommended
 - Stoploss recommended
-- Hyperopt parameter
 
 The bot also include a sample strategy called `TestStrategy` you can update: `user_data/strategies/test_strategy.py`.
 You can test it with the parameter: `--strategy TestStrategy`
@@ -61,22 +60,22 @@ file as reference.**
 
 ### Buy strategy
 
-Edit the method `populate_buy_trend()` into your strategy file to
+Edit the method `populate_buy_trend()` into your strategy file to update your buy strategy.
-update your buy strategy.
 
 Sample from `user_data/strategies/test_strategy.py`:
 
 ```python
-def populate_buy_trend(self, dataframe: DataFrame) -> 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 dataframe: DataFrame populated with indicators
+    :param metadata: Additional information, like the currently traded pair
     :return: DataFrame with buy column
     """
     dataframe.loc[
         (
             (dataframe['adx'] > 30) &
-            (dataframe['tema'] <= dataframe['blower']) &
+            (dataframe['tema'] <= dataframe['bb_middleband']) &
             (dataframe['tema'] > dataframe['tema'].shift(1))
         ),
         'buy'] = 1
@@ -87,38 +86,47 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
 ### Sell strategy
 
 Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
+Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
 
 Sample from `user_data/strategies/test_strategy.py`:
 
 ```python
-def populate_sell_trend(self, dataframe: DataFrame) -> 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 dataframe: DataFrame populated with indicators
+    :param metadata: Additional information, like the currently traded pair
     :return: DataFrame with buy column
     """
     dataframe.loc[
         (
             (dataframe['adx'] > 70) &
-            (dataframe['tema'] > dataframe['blower']) &
+            (dataframe['tema'] > dataframe['bb_middleband']) &
             (dataframe['tema'] < dataframe['tema'].shift(1))
         ),
         'sell'] = 1
     return dataframe
 ```
 
-## Add more Indicator
+## Add more Indicators
 
-As you have seen, buy and sell strategies need indicators. You can add
+As you have seen, buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
-more indicators by extending the list contained in
+
-the method `populate_indicators()` from your strategy file.
+You should only add the indicators used in either `populate_buy_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
 
 Sample:
 
 ```python
-def populate_indicators(dataframe: DataFrame) -> DataFrame:
+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
     """
     dataframe['sar'] = ta.SAR(dataframe)
     dataframe['adx'] = ta.ADX(dataframe)
@@ -149,6 +157,11 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
     return dataframe
 ```
 
+### Metadata dict
+
+The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
+Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
+
 ### Want more indicator examples
 
 Look into the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py).

freqtrade/optimize/backtesting.py

@@ -57,8 +57,8 @@ class Backtesting(object):
         self.strategy: IStrategy = StrategyResolver(self.config).strategy
         self.ticker_interval = self.strategy.ticker_interval
         self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
-        self.populate_buy_trend = self.strategy.populate_buy_trend
+        self.advise_buy = self.strategy.advise_buy
-        self.populate_sell_trend = self.strategy.populate_sell_trend
+        self.advise_sell = self.strategy.advise_sell
 
         # Reset keys for backtesting
         self.config['exchange']['key'] = ''
@@ -229,8 +229,8 @@ class Backtesting(object):
         for pair, pair_data in processed.items():
             pair_data['buy'], pair_data['sell'] = 0, 0  # cleanup from previous run
 
-            ticker_data = self.populate_sell_trend(
+            ticker_data = self.advise_sell(
-                self.populate_buy_trend(pair_data))[headers].copy()
+                self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
 
             # to avoid using data from future, we buy/sell with signal from previous candle
             ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)

freqtrade/optimize/hyperopt.py

@@ -75,7 +75,7 @@ class Hyperopt(Backtesting):
         return arg_dict
 
     @staticmethod
-    def populate_indicators(dataframe: DataFrame) -> DataFrame:
+    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
         dataframe['adx'] = ta.ADX(dataframe)
         macd = ta.MACD(dataframe)
         dataframe['macd'] = macd['macd']
@@ -228,7 +228,7 @@ class Hyperopt(Backtesting):
         """
         Define the buy strategy parameters to be used by hyperopt
         """
-        def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
             """
             Buy strategy Hyperopt will build and use
             """
@@ -270,7 +270,7 @@ class Hyperopt(Backtesting):
             self.strategy.minimal_roi = self.generate_roi_table(params)
 
         if self.has_space('buy'):
-            self.populate_buy_trend = self.buy_strategy_generator(params)
+            self.advise_buy = self.buy_strategy_generator(params)
 
         if self.has_space('stoploss'):
             self.strategy.stoploss = params['stoploss']
@@ -351,7 +351,7 @@ class Hyperopt(Backtesting):
         )
 
         if self.has_space('buy'):
-            self.strategy.populate_indicators = Hyperopt.populate_indicators  # type: ignore
+            self.strategy.advise_indicators = Hyperopt.populate_indicators  # type: ignore
         dump(self.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
         self.exchange = None  # type: ignore
         self.load_previous_results()

freqtrade/strategy/default_strategy.py

@@ -28,13 +28,16 @@ class DefaultStrategy(IStrategy):
     # Optimal ticker interval for the strategy
     ticker_interval = '5m'
 
-    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+    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
@@ -196,10 +199,11 @@ class DefaultStrategy(IStrategy):
 
         return dataframe
 
-    def populate_buy_trend(self, dataframe: DataFrame) -> 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[
@@ -217,10 +221,11 @@ class DefaultStrategy(IStrategy):
 
         return dataframe
 
-    def populate_sell_trend(self, dataframe: DataFrame) -> 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[

freqtrade/strategy/interface.py

@@ -7,6 +7,7 @@ from abc import ABC, abstractmethod
 from datetime import datetime
 from enum import Enum
 from typing import Dict, List, NamedTuple, Tuple
+import warnings
 
 import arrow
 from pandas import DataFrame
@@ -57,34 +58,45 @@ class IStrategy(ABC):
         ticker_interval -> str: value of the ticker interval to use for the strategy
     """
 
+    _populate_fun_len: int = 0
+    _buy_fun_len: int = 0
+    _sell_fun_len: int = 0
+    # associated minimal roi
     minimal_roi: Dict
+
+    # associated stoploss
     stoploss: float
+
+    # associated ticker interval
     ticker_interval: str
 
     def __init__(self, config: dict) -> None:
         self.config = config
 
     @abstractmethod
-    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+    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 metadata: Additional information, like the currently traded pair
         :return: a Dataframe with all mandatory indicators for the strategies
         """
 
     @abstractmethod
-    def populate_buy_trend(self, dataframe: DataFrame) -> 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
         """
 
     @abstractmethod
-    def populate_sell_trend(self, dataframe: DataFrame) -> 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 sell column
         """
 
@@ -94,16 +106,16 @@ class IStrategy(ABC):
         """
         return self.__class__.__name__
 
-    def analyze_ticker(self, ticker_history: List[Dict]) -> DataFrame:
+    def analyze_ticker(self, ticker_history: List[Dict], metadata: 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 = parse_ticker_dataframe(ticker_history)
-        dataframe = self.populate_indicators(dataframe)
+        dataframe = self.advise_indicators(dataframe, metadata)
-        dataframe = self.populate_buy_trend(dataframe)
+        dataframe = self.advise_buy(dataframe, metadata)
-        dataframe = self.populate_sell_trend(dataframe)
+        dataframe = self.advise_sell(dataframe, metadata)
         return dataframe
 
     def get_signal(self, pair: str, interval: str, ticker_hist: List[Dict]) -> Tuple[bool, bool]:
@@ -118,7 +130,7 @@ class IStrategy(ABC):
             return False, False
 
         try:
-            dataframe = self.analyze_ticker(ticker_hist)
+            dataframe = self.analyze_ticker(ticker_hist, {'pair': pair})
         except ValueError as error:
             logger.warning(
                 'Unable to analyze ticker for pair %s: %s',
@@ -263,5 +275,50 @@ class IStrategy(ABC):
         """
         Creates a dataframe and populates indicators for given ticker data
         """
-        return {pair: self.populate_indicators(parse_ticker_dataframe(pair_data))
+        return {pair: self.advise_indicators(parse_ticker_dataframe(pair_data), {'pair': pair})
                 for pair, pair_data in tickerdata.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 metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+        if self._populate_fun_len == 2:
+            warnings.warn("deprecated - check out the Sample strategy to see "
+                          "the current function headers!", DeprecationWarning)
+            return self.populate_indicators(dataframe)  # type: ignore
+        else:
+            return self.populate_indicators(dataframe, metadata)
+
+    def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        This method should not be overridden.
+        :param dataframe: DataFrame
+        :param pair: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        if self._buy_fun_len == 2:
+            warnings.warn("deprecated - check out the Sample strategy to see "
+                          "the current function headers!", DeprecationWarning)
+            return self.populate_buy_trend(dataframe)  # type: ignore
+        else:
+            return self.populate_buy_trend(dataframe, metadata)
+
+    def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        This method should not be overridden.
+        :param dataframe: DataFrame
+        :param pair: Additional information, like the currently traded pair
+        :return: DataFrame with sell column
+        """
+        if self._sell_fun_len == 2:
+            warnings.warn("deprecated - check out the Sample strategy to see "
+                          "the current function headers!", DeprecationWarning)
+            return self.populate_sell_trend(dataframe)  # type: ignore
+        else:
+            return self.populate_sell_trend(dataframe, metadata)

freqtrade/strategy/resolver.py

@@ -92,6 +92,13 @@ class StrategyResolver(object):
                 strategy = self._search_strategy(path, strategy_name=strategy_name, config=config)
                 if strategy:
                     logger.info('Using resolved strategy %s from \'%s\'', strategy_name, path)
+                    strategy._populate_fun_len = len(
+                        inspect.getfullargspec(strategy.populate_indicators).args)
+                    strategy._buy_fun_len = len(
+                        inspect.getfullargspec(strategy.populate_buy_trend).args)
+                    strategy._sell_fun_len = len(
+                        inspect.getfullargspec(strategy.populate_sell_trend).args)
+
                     return import_strategy(strategy, config=config)
             except FileNotFoundError:
                 logger.warning('Path "%s" does not exist', path)

freqtrade/tests/optimize/test_backtesting.py

@@ -146,7 +146,7 @@ def _trend(signals, buy_value, sell_value):
     return signals
 
 
-def _trend_alternate(dataframe=None):
+def _trend_alternate(dataframe=None, metadata=None):
     signals = dataframe
     low = signals['low']
     n = len(low)
@@ -332,8 +332,8 @@ def test_backtesting_init(mocker, default_conf) -> None:
     assert backtesting.config == default_conf
     assert backtesting.ticker_interval == '5m'
     assert callable(backtesting.tickerdata_to_dataframe)
-    assert callable(backtesting.populate_buy_trend)
+    assert callable(backtesting.advise_buy)
-    assert callable(backtesting.populate_sell_trend)
+    assert callable(backtesting.advise_sell)
     get_fee.assert_called()
     assert backtesting.fee == 0.5
 
@@ -611,42 +611,42 @@ def test_backtest_ticks(default_conf, fee, mocker):
     mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
     patch_exchange(mocker)
     ticks = [1, 5]
-    fun = Backtesting(default_conf).populate_buy_trend
+    fun = Backtesting(default_conf).advise_buy
     for _ in ticks:
         backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
         backtesting = Backtesting(default_conf)
-        backtesting.populate_buy_trend = fun  # Override
+        backtesting.advise_buy = fun  # Override
-        backtesting.populate_sell_trend = fun  # Override
+        backtesting.advise_sell = fun  # Override
         results = backtesting.backtest(backtest_conf)
         assert not results.empty
 
 
 def test_backtest_clash_buy_sell(mocker, default_conf):
     # Override the default buy trend function in our default_strategy
-    def fun(dataframe=None):
+    def fun(dataframe=None, pair=None):
         buy_value = 1
         sell_value = 1
         return _trend(dataframe, buy_value, sell_value)
 
     backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
     backtesting = Backtesting(default_conf)
-    backtesting.populate_buy_trend = fun  # Override
+    backtesting.advise_buy = fun  # Override
-    backtesting.populate_sell_trend = fun  # Override
+    backtesting.advise_sell = fun  # Override
     results = backtesting.backtest(backtest_conf)
     assert results.empty
 
 
 def test_backtest_only_sell(mocker, default_conf):
     # Override the default buy trend function in our default_strategy
-    def fun(dataframe=None):
+    def fun(dataframe=None, pair=None):
         buy_value = 0
         sell_value = 1
         return _trend(dataframe, buy_value, sell_value)
 
     backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
     backtesting = Backtesting(default_conf)
-    backtesting.populate_buy_trend = fun  # Override
+    backtesting.advise_buy = fun  # Override
-    backtesting.populate_sell_trend = fun  # Override
+    backtesting.advise_sell = fun  # Override
     results = backtesting.backtest(backtest_conf)
     assert results.empty
 
@@ -655,8 +655,8 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
     mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
     backtest_conf = _make_backtest_conf(mocker, conf=default_conf, pair='UNITTEST/BTC')
     backtesting = Backtesting(default_conf)
-    backtesting.populate_buy_trend = _trend_alternate  # Override
+    backtesting.advise_buy = _trend_alternate  # Override
-    backtesting.populate_sell_trend = _trend_alternate  # Override
+    backtesting.advise_sell = _trend_alternate  # Override
     results = backtesting.backtest(backtest_conf)
     backtesting._store_backtest_result("test_.json", results)
     assert len(results) == 4

freqtrade/tests/optimize/test_hyperopt.py

@@ -247,7 +247,7 @@ def test_populate_indicators(init_hyperopt) -> None:
     tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
     tickerlist = {'UNITTEST/BTC': tick}
     dataframes = _HYPEROPT.tickerdata_to_dataframe(tickerlist)
-    dataframe = _HYPEROPT.populate_indicators(dataframes['UNITTEST/BTC'])
+    dataframe = _HYPEROPT.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
 
     # Check if some indicators are generated. We will not test all of them
     assert 'adx' in dataframe
@@ -259,7 +259,7 @@ def test_buy_strategy_generator(init_hyperopt) -> None:
     tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
     tickerlist = {'UNITTEST/BTC': tick}
     dataframes = _HYPEROPT.tickerdata_to_dataframe(tickerlist)
-    dataframe = _HYPEROPT.populate_indicators(dataframes['UNITTEST/BTC'])
+    dataframe = _HYPEROPT.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
 
     populate_buy_trend = _HYPEROPT.buy_strategy_generator(
         {
@@ -274,7 +274,7 @@ def test_buy_strategy_generator(init_hyperopt) -> None:
             'trigger': 'bb_lower'
         }
     )
-    result = populate_buy_trend(dataframe)
+    result = populate_buy_trend(dataframe, {'pair': 'UNITTEST/BTC'})
     # Check if some indicators are generated. We will not test all of them
     assert 'buy' in result
     assert 1 in result['buy']

freqtrade/tests/strategy/legacy_strategy.py

@@ -0,0 +1,235 @@
+
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+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.
+class TestStrategyLegacy(IStrategy):
+    """
+    This is a test strategy using the legacy function headers, which will be
+    removed in a future update.
+    Please do not use this as a template, but refer to user_data/strategy/TestStrategy.py
+    for a uptodate version of this template.
+
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.10
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = '5m'
+
+    def populate_indicators(self, dataframe: DataFrame) -> 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.
+        """
+
+        # Momentum Indicator
+        # ------------------------------------
+
+        # 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:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['adx'] > 30) &
+                (dataframe['tema'] <= dataframe['bb_middleband']) &
+                (dataframe['tema'] > dataframe['tema'].shift(1))
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['adx'] > 70) &
+                (dataframe['tema'] > dataframe['bb_middleband']) &
+                (dataframe['tema'] < dataframe['tema'].shift(1))
+            ),
+            'sell'] = 1
+        return dataframe

freqtrade/tests/strategy/test_default_strategy.py

@@ -25,10 +25,11 @@ def test_default_strategy_structure():
 def test_default_strategy(result):
     strategy = DefaultStrategy({})
 
+    metadata = {'pair': 'ETH/BTC'}
     assert type(strategy.minimal_roi) is dict
     assert type(strategy.stoploss) is float
     assert type(strategy.ticker_interval) is str
-    indicators = strategy.populate_indicators(result)
+    indicators = strategy.populate_indicators(result, metadata)
     assert type(indicators) is DataFrame
-    assert type(strategy.populate_buy_trend(indicators)) is DataFrame
+    assert type(strategy.populate_buy_trend(indicators, metadata)) is DataFrame
-    assert type(strategy.populate_sell_trend(indicators)) is DataFrame
+    assert type(strategy.populate_sell_trend(indicators, metadata)) is DataFrame

freqtrade/tests/strategy/test_strategy.py

@@ -1,8 +1,10 @@
 # pragma pylint: disable=missing-docstring, protected-access, C0103
 import logging
-import os
+from os import path
+import warnings
 
 import pytest
+from pandas import DataFrame
 
 from freqtrade.strategy import import_strategy
 from freqtrade.strategy.default_strategy import DefaultStrategy
@@ -37,8 +39,8 @@ def test_import_strategy(caplog):
 
 def test_search_strategy():
     default_config = {}
-    default_location = os.path.join(os.path.dirname(
+    default_location = path.join(path.dirname(
-        os.path.realpath(__file__)), '..', '..', 'strategy'
+        path.realpath(__file__)), '..', '..', 'strategy'
     )
     assert isinstance(
         StrategyResolver._search_strategy(
@@ -57,12 +59,13 @@ def test_search_strategy():
 
 def test_load_strategy(result):
     resolver = StrategyResolver({'strategy': 'TestStrategy'})
-    assert 'adx' in resolver.strategy.populate_indicators(result)
+    metadata = {'pair': 'ETH/BTC'}
+    assert 'adx' in resolver.strategy.advise_indicators(result, metadata=metadata)
 
 
 def test_load_strategy_invalid_directory(result, caplog):
     resolver = StrategyResolver()
-    extra_dir = os.path.join('some', 'path')
+    extra_dir = path.join('some', 'path')
     resolver._load_strategy('TestStrategy', config={}, extra_dir=extra_dir)
 
     assert (
@@ -70,7 +73,8 @@ def test_load_strategy_invalid_directory(result, caplog):
         logging.WARNING,
         'Path "{}" does not exist'.format(extra_dir),
     ) in caplog.record_tuples
-    assert 'adx' in resolver.strategy.populate_indicators(result)
+
+    assert 'adx' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
 
 
 def test_load_not_found_strategy():
@@ -85,7 +89,7 @@ def test_strategy(result):
     config = {'strategy': 'DefaultStrategy'}
 
     resolver = StrategyResolver(config)
-
+    metadata = {'pair': 'ETH/BTC'}
     assert resolver.strategy.minimal_roi[0] == 0.04
     assert config["minimal_roi"]['0'] == 0.04
 
@@ -95,12 +99,13 @@ def test_strategy(result):
     assert resolver.strategy.ticker_interval == '5m'
     assert config['ticker_interval'] == '5m'
 
-    assert 'adx' in resolver.strategy.populate_indicators(result)
+    df_indicators = resolver.strategy.advise_indicators(result, metadata=metadata)
+    assert 'adx' in df_indicators
 
-    dataframe = resolver.strategy.populate_buy_trend(resolver.strategy.populate_indicators(result))
+    dataframe = resolver.strategy.advise_buy(df_indicators, metadata=metadata)
     assert 'buy' in dataframe.columns
 
-    dataframe = resolver.strategy.populate_sell_trend(resolver.strategy.populate_indicators(result))
+    dataframe = resolver.strategy.advise_sell(df_indicators, metadata=metadata)
     assert 'sell' in dataframe.columns
 
 
@@ -150,3 +155,59 @@ def test_strategy_override_ticker_interval(caplog):
             logging.INFO,
             'Override strategy \'ticker_interval\' with value in config file: 60.'
             ) in caplog.record_tuples
+
+
+def test_deprecate_populate_indicators(result):
+    default_location = path.join(path.dirname(path.realpath(__file__)))
+    resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
+                                 'strategy_path': default_location})
+    with warnings.catch_warnings(record=True) as w:
+        # Cause all warnings to always be triggered.
+        warnings.simplefilter("always")
+        indicators = resolver.strategy.advise_indicators(result, 'ETH/BTC')
+        assert len(w) == 1
+        assert issubclass(w[-1].category, DeprecationWarning)
+        assert "deprecated - check out the Sample strategy to see the current function headers!" \
+            in str(w[-1].message)
+
+    with warnings.catch_warnings(record=True) as w:
+            # Cause all warnings to always be triggered.
+        warnings.simplefilter("always")
+        resolver.strategy.advise_buy(indicators, 'ETH/BTC')
+        assert len(w) == 1
+        assert issubclass(w[-1].category, DeprecationWarning)
+        assert "deprecated - check out the Sample strategy to see the current function headers!" \
+            in str(w[-1].message)
+
+    with warnings.catch_warnings(record=True) as w:
+        # Cause all warnings to always be triggered.
+        warnings.simplefilter("always")
+        resolver.strategy.advise_sell(indicators, 'ETH_BTC')
+        assert len(w) == 1
+        assert issubclass(w[-1].category, DeprecationWarning)
+        assert "deprecated - check out the Sample strategy to see the current function headers!" \
+            in str(w[-1].message)
+
+
+def test_call_deprecated_function(result, monkeypatch):
+    default_location = path.join(path.dirname(path.realpath(__file__)))
+    resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
+                                 'strategy_path': default_location})
+    metadata = {'pair': 'ETH/BTC'}
+
+    # Make sure we are using a legacy function
+    assert resolver.strategy._populate_fun_len == 2
+    assert resolver.strategy._buy_fun_len == 2
+    assert resolver.strategy._sell_fun_len == 2
+
+    indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
+    assert type(indicator_df) is DataFrame
+    assert 'adx' in indicator_df.columns
+
+    buydf = resolver.strategy.advise_buy(result, metadata=metadata)
+    assert type(buydf) is DataFrame
+    assert 'buy' in buydf.columns
+
+    selldf = resolver.strategy.advise_sell(result, metadata=metadata)
+    assert type(selldf) is DataFrame
+    assert 'sell' in selldf

freqtrade/tests/test_dataframe.py

@@ -14,7 +14,7 @@ def load_dataframe_pair(pairs, strategy):
     assert isinstance(pairs[0], str)
     dataframe = ld[pairs[0]]
 
-    dataframe = strategy.analyze_ticker(dataframe)
+    dataframe = strategy.analyze_ticker(dataframe, pairs[0])
     return dataframe
 
 

scripts/plot_dataframe.py

@@ -159,8 +159,8 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
     dataframes = strategy.tickerdata_to_dataframe(tickers)
 
     dataframe = dataframes[pair]
-    dataframe = strategy.populate_buy_trend(dataframe)
+    dataframe = strategy.advise_buy(dataframe, {'pair': pair})
-    dataframe = strategy.populate_sell_trend(dataframe)
+    dataframe = strategy.advise_sell(dataframe, {'pair': pair})
 
     if len(dataframe.index) > args.plot_limit:
         logger.warning('Ticker contained more than %s candles as defined '

user_data/strategies/test_strategy.py

@@ -18,6 +18,7 @@ class TestStrategy(IStrategy):
     More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
 
     You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
     - Rename the class name (Do not forget to update class_name)
     - Add any methods you want to build your strategy
     - Add any lib you need to build your strategy
@@ -44,13 +45,16 @@ class TestStrategy(IStrategy):
     # Optimal ticker interval for the strategy
     ticker_interval = '5m'
 
-    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+    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
@@ -211,10 +215,11 @@ class TestStrategy(IStrategy):
 
         return dataframe
 
-    def populate_buy_trend(self, dataframe: DataFrame) -> 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 dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
         :return: DataFrame with buy column
         """
         dataframe.loc[
@@ -227,10 +232,11 @@ class TestStrategy(IStrategy):
 
         return dataframe
 
-    def populate_sell_trend(self, dataframe: DataFrame) -> 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 dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
         :return: DataFrame with buy column
         """
         dataframe.loc[