Update docs for populate_exit_trend

docs/bot-basics.md

@@ -30,7 +30,7 @@ By default, loop runs every few seconds (`internals.process_throttle_secs`) and
 * Analyze strategy per pair.
   * Call `populate_indicators()`
   * Call `populate_entry_trend()`
-  * Call `populate_sell_trend()`
+  * Call `populate_exit_trend()`
 * Check timeouts for open orders.
   * Calls `check_buy_timeout()` strategy callback for open buy orders.
   * Calls `check_sell_timeout()` strategy callback for open sell orders.
@@ -55,7 +55,7 @@ This loop will be repeated again and again until the bot is stopped.
 * Load historic data for configured pairlist.
 * Calls `bot_loop_start()` once.
 * Calculate indicators (calls `populate_indicators()` once per pair).
-* Calculate buy / sell signals (calls `populate_entry_trend()` and `populate_sell_trend()` once per pair).
+* Calculate buy / sell signals (calls `populate_entry_trend()` and `populate_exit_trend()` once per pair).
 * Loops per candle simulating entry and exit points.
   * Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
   * Call `custom_entry_price()` (if implemented in the strategy) to determine entry price (Prices are moved to be within the opening candle).

docs/hyperopt.md

@@ -180,7 +180,7 @@ Hyperopt will first load your data into memory and will then run `populate_indic
 
 Hyperopt will then spawn into different processes (number of processors, or `-j <n>`), and run backtesting over and over again, changing the parameters that are part of the `--spaces` defined.
 
-For every new set of parameters, freqtrade will run first `populate_entry_trend()` followed by `populate_sell_trend()`, and then run the regular backtesting process to simulate trades.
+For every new set of parameters, freqtrade will run first `populate_entry_trend()` followed by `populate_exit_trend()`, and then run the regular backtesting process to simulate trades.
 
 After backtesting, the results are passed into the [loss function](#loss-functions), which will evaluate if this result was better or worse than previous results.  
 Based on the loss function result, hyperopt will determine the next set of parameters to try in the next round of backtesting.
@@ -210,7 +210,7 @@ Similar to the entry-signal above, exit-signals can also be optimized.
 Place the corresponding settings into the following methods
 
 * Define the parameters at the class level hyperopt shall be optimizing, either naming them `sell_*`, or by explicitly defining `space='sell'`.
-* Within `populate_sell_trend()` - use defined parameter values instead of raw constants.
+* Within `populate_exit_trend()` - use defined parameter values instead of raw constants.
 
 The configuration and rules are the same than for buy signals.
 
@@ -379,7 +379,7 @@ class MyAwesomeStrategy(IStrategy):
                 'enter_long'] = 1
         return dataframe
 
-    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         conditions = []
         conditions.append(qtpylib.crossed_above(
                 dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']

docs/strategy-advanced.md

@@ -111,7 +111,7 @@ def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_r
 Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
 
 ``` python
-def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     dataframe.loc[
         (
             (dataframe['rsi'] > 70) &

docs/strategy-callbacks.md

@@ -1,6 +1,6 @@
 # Strategy Callbacks
 
-While the main strategy functions (`populate_indicators()`, `populate_buy_trend()`, `populate_sell_trend()`) should be used in a vectorized way, and are only called [once during backtesting](bot-basics.md#backtesting-hyperopt-execution-logic), callbacks are called "whenever needed".
+While the main strategy functions (`populate_indicators()`, `populate_entry_trend()`, `populate_exit_trend()`) should be used in a vectorized way, and are only called [once during backtesting](bot-basics.md#backtesting-hyperopt-execution-logic), callbacks are called "whenever needed".
 
 As such, you should avoid doing heavy calculations in callbacks to avoid delays during operations.
 Depending on the callback used, they may be called when entering / exiting a trade, or throughout the duration of a trade.

docs/strategy-customization.md

@@ -101,7 +101,7 @@ With this section, you have a new column in your dataframe, which has `1` assign
 
 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.
 
-You should only add the indicators used in either `populate_entry_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
+You should only add the indicators used in either `populate_entry_trend()`, `populate_exit_trend()`, or to populate another indicator, otherwise performance may suffer.
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
@@ -263,7 +263,7 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
 
 ### Exit signal rules
 
-Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
+Edit the method `populate_exit_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.
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
@@ -273,7 +273,7 @@ This method will also define a new column, `"exit_long"`, which needs to contain
 Sample from `user_data/strategies/sample_strategy.py`:
 
 ```python
-def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
     """
     Based on TA indicators, populates the sell signal for the given dataframe
     :param dataframe: DataFrame populated with indicators
@@ -297,7 +297,7 @@ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
     Short-trades need to be supported by your exchange and market configuration!
 
     ```python
-    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         dataframe.loc[
             (
                 (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
@@ -397,7 +397,7 @@ Disabling of this will have short signals ignored (also in futures markets).
 
 ### Metadata dict
 
-The metadata-dict (available for `populate_entry_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
+The metadata-dict (available for `populate_entry_trend`, `populate_exit_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`.
 
 The Metadata-dict should not be modified and does not persist information across multiple calls.
@@ -1050,7 +1050,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
 
 ## Print created dataframe
 
-To inspect the created dataframe, you can issue a print-statement in either `populate_entry_trend()` or `populate_sell_trend()`.
+To inspect the created dataframe, you can issue a print-statement in either `populate_entry_trend()` or `populate_exit_trend()`.
 You may also want to print the pair so it's clear what data is currently shown.
 
 ``` python