Introduce strategy_version variable

freqtrade/resolvers/strategy_resolver.py

@@ -153,6 +153,10 @@ class StrategyResolver(IResolver):
             strategy._populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
             strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
             strategy._sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
+            if any([x == 2 for x in [strategy._populate_fun_len,
+                                     strategy._buy_fun_len,
+                                     strategy._sell_fun_len]]):
+                strategy.strategy_version = 1
 
             try:
                 return import_strategy(strategy, config=config)

freqtrade/strategy/default_strategy.py

@@ -13,6 +13,7 @@ class DefaultStrategy(IStrategy):
     Default Strategy provided by freqtrade bot.
     You can override it with your own strategy
     """
+    strategy_version: int = 2
 
     # Minimal ROI designed for the strategy
     minimal_roi = {

freqtrade/strategy/interface.py

@@ -60,6 +60,11 @@ class IStrategy(ABC):
         stoploss -> float: optimal stoploss designed for the strategy
         ticker_interval -> str: value of the ticker interval to use for the strategy
     """
+    # Strategy interface version
+    # Default to version 2
+    # version 1 is the initial interface without metadata dict
+    # Version 2 populate_* include metadata dict
+    strategy_version: int = 2
 
     _populate_fun_len: int = 0
     _buy_fun_len: int = 0

freqtrade/tests/strategy/test_strategy.py

@@ -380,6 +380,31 @@ def test_call_deprecated_function(result, monkeypatch, default_conf):
     assert resolver.strategy._populate_fun_len == 2
     assert resolver.strategy._buy_fun_len == 2
     assert resolver.strategy._sell_fun_len == 2
+    assert resolver.strategy.strategy_version == 1
+
+    indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
+    assert isinstance(indicator_df, DataFrame)
+    assert 'adx' in indicator_df.columns
+
+    buydf = resolver.strategy.advise_buy(result, metadata=metadata)
+    assert isinstance(buydf, DataFrame)
+    assert 'buy' in buydf.columns
+
+    selldf = resolver.strategy.advise_sell(result, metadata=metadata)
+    assert isinstance(selldf, DataFrame)
+    assert 'sell' in selldf
+
+
+def test_strategy_versioning(result, monkeypatch, default_conf):
+    default_conf.update({'strategy': 'DefaultStrategy'})
+    resolver = StrategyResolver(default_conf)
+    metadata = {'pair': 'ETH/BTC'}
+
+    # Make sure we are using a legacy function
+    assert resolver.strategy._populate_fun_len == 3
+    assert resolver.strategy._buy_fun_len == 3
+    assert resolver.strategy._sell_fun_len == 3
+    assert resolver.strategy.strategy_version == 2
 
     indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
     assert isinstance(indicator_df, DataFrame)

user_data/strategies/test_strategy.py

@@ -28,6 +28,9 @@ class TestStrategy(IStrategy):
     - the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
     populate_sell_trend, hyperopt_space, buy_strategy_generator
     """
+    # Strategy intervace version - allow new iterations of the strategy interface.
+    # Check the documentation or the Sample strategy to get the latest version.
+    strategy_version: int = 2
 
     # Minimal ROI designed for the strategy.
     # This attribute will be overridden if the config file contains "minimal_roi"
@@ -256,14 +259,14 @@ class TestStrategy(IStrategy):
         # Retrieve best bid and best ask
         # ------------------------------------
         """
-        # first check if dataprovider is available 
+        # first check if dataprovider is available
         if self.dp:
             if self.dp.runmode in ('live', 'dry_run'):
                 ob = self.dp.orderbook(metadata['pair'], 1)
                 dataframe['best_bid'] = ob['bids'][0][0]
                 dataframe['best_ask'] = ob['asks'][0][0]
         """
-        
+
         return dataframe
 
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: