enhance migration documentation

docs/strategy-callbacks.md

@@ -713,3 +713,4 @@ class AwesomeStrategy(IStrategy):
         :return: A leverage amount, which is between 1.0 and max_leverage.
         """
         return 1.0
+```

docs/strategy-customization.md

@@ -908,7 +908,7 @@ In some situations it may be confusing to deal with stops relative to current ra
                             current_rate: float, current_profit: float, **kwargs) -> float:
             dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
             candle = dataframe.iloc[-1].squeeze()
-            return stoploss_from_absolute(current_rate - (candle['atr'] * 2, is_short=trade.is_short), current_rate)
+            return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)
 
     ```
 

docs/strategy_analysis_example.md

@@ -73,7 +73,7 @@ df.tail()
 
 ```python
 # Report results
-print(f"Generated {df['buy'].sum()} buy signals")
+print(f"Generated {df['enter_long'].sum()} entry signals")
 data = df.set_index('date', drop=False)
 data.tail()
 ```
@@ -244,7 +244,7 @@ import plotly.figure_factory as ff
 hist_data = [trades.profit_ratio]
 group_labels = ['profit_ratio']  # name of the dataset
 
-fig = ff.create_distplot(hist_data, group_labels,bin_size=0.01)
+fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)
 fig.show()
 
 ```

docs/strategy_migration.md

@@ -5,7 +5,7 @@ We have put a great effort into keeping compatibility with existing strategies,
 To support new markets and trade-types (namely short trades / trades with leverage), some things had to change in the interface.
 If you intend on using markets other than spot markets, please migrate your strategy to the new format.
 
-## Quick summary / checklist
+## Quick summary / migration checklist
 
 * Dataframe columns:
   * `buy` -> `enter_long`
@@ -17,11 +17,152 @@ If you intend on using markets other than spot markets, please migrate your stra
   * `custom_stake_amount`
   * `confirm_trade_entry`
 * Renamed `trade.nr_of_successful_buys` to `trade.nr_of_successful_entries`.
-* Introduced new `leverage` callback
+* Introduced new [`leverage` callback](strategy-callbacks.md#leverage-callback)
+* Informative pairs can now pass a 3rd element in the Tuple, defining the candle type.
 * `@informative` decorator now takes an optional `candle_type` argument
 * helper methods `stoploss_from_open` and `stoploss_from_absolute` now take `is_short` as additional argument.
 * `INTERFACE_VERSION` should be set to 3.
 
 ## Extensive explanation
 
+### `populate_buy_trend`
+
+In `populate_buy_trend()` - you will want to change the columns you assign from `'buy`' to `'enter_long`
+
+```python hl_lines="9"
+def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['buy', 'buy_tag']] = (1, 'rsi_cross')
+
+    return dataframe
+```
+
+After:
+
+```python hl_lines="9"
+def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
+
+    return dataframe
+```
+
+### `populate_sell_trend`
+
+``` python hl_lines="9"
+def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['sell', 'exit_tag']] = (1, 'some_exit_tag')
+    return dataframe
+```
+
+After
+
+``` python hl_lines="9"
+def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['exit_long', 'exit_tag']] = (1, 'some_exit_tag')
+    return dataframe
+```
+
+### Custom-stake-amount
+
+New string argument `side` - which can be either `"long"` or `"short"`.
+
+``` python hl_lines="4"
+class AwesomeStrategy(IStrategy):
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float, max_stake: float,
+                            entry_tag: Optional[str], **kwargs) -> float:
+        # ... 
+        return proposed_stake
+```
+
+``` python hl_lines="4"
+class AwesomeStrategy(IStrategy):
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float, max_stake: float,
+                            entry_tag: Optional[str], side: str, **kwargs) -> float:
+        # ... 
+        return proposed_stake
+```
+
+### `confirm_trade_entry`
+
+New string argument `side` - which can be either `"long"` or `"short"`.
+
+``` python hl_lines="5"
+class AwesomeStrategy(IStrategy):
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str], 
+                            **kwargs) -> bool:
+      return True
+```
+After: 
+
+``` python hl_lines="5"
+class AwesomeStrategy(IStrategy):
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str], 
+                            side: str, **kwargs) -> bool:
+      return True
+```
+
+### Adjust trade position changes
+
+While adjust-trade-position itself did not change, you should no longer use `trade.nr_of_successful_buys` - and instead use `trade.nr_of_successful_entries`, which will also include short entries.
+
+### Helper methods
+
+Added argument "is_short" to `stoploss_from_open` and `stoploss_from_absolute`.
+This should be given the value of `trade.is_short`.
+
+``` python hl_lines="5 7"
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
+        if current_profit > 0.10:
+            return stoploss_from_open(0.07, current_profit)
+
+        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)
+
+        return 1
+
+```
+
+``` python hl_lines="5 7"
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
+        if current_profit > 0.10:
+            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
+
+        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)
+
+
+```
 

freqtrade/templates/strategy_analysis_example.ipynb

@@ -110,7 +110,7 @@
    "outputs": [],
    "source": [
     "# Report results\n",
-    "print(f\"Generated {df['buy'].sum()} buy signals\")\n",
+    "print(f\"Generated {df['enter_long'].sum()} entry signals\")\n",
     "data = df.set_index('date', drop=False)\n",
     "data.tail()"
    ]
@@ -348,7 +348,7 @@
     "hist_data = [trades.profit_ratio]\n",
     "group_labels = ['profit_ratio']  # name of the dataset\n",
     "\n",
-    "fig = ff.create_distplot(hist_data, group_labels,bin_size=0.01)\n",
+    "fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)\n",
     "fig.show()\n"
    ]
   },