Update hyperopt documentation with sell-stuff

docs/hyperopt.md

@@ -11,30 +11,29 @@ and still take a long time.
 
 ## Prepare Hyperopting
 
-Before we start digging in Hyperopt, we recommend you to take a look at
+Before we start digging into Hyperopt, we recommend you to take a look at
 an example hyperopt file located into [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py)
 
-### 1. Install a Custom Hyperopt File
+Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
-This is very simple. Put your hyperopt file into the folder
-`user_data/hyperopts`.
 
-Let assume you want a hyperopt file `awesome_hyperopt.py`:<br/>
+### 1. Install a Custom Hyperopt File
+
+Put your hyperopt file into the folder`user_data/hyperopts`.
+
+Let assume you want a hyperopt file `awesome_hyperopt.py`:  
 Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
 
-
 ### 2. Configure your Guards and Triggers
-There are two places you need to change in your hyperopt file to add a
+
-new buy hyperopt for testing:
+There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
-- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L230-L251).
+
-- Inside [indicator_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L207-L223).
+- Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
+- Inside `populate_buy_trend()` - applying the parameters.
 
 There you have two different types of indicators: 1. `guards` and 2. `triggers`.
 
-1. Guards are conditions like "never buy if ADX < 10", or never buy if
+1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
-current price is over EMA10.
+2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower bollinger band".
-2. Triggers are ones that actually trigger buy in specific moment, like
-"buy when EMA5 crosses over EMA10" or "buy when close price touches lower
-bollinger band".
 
 Hyperoptimization will, for each eval round, pick one trigger and possibly
 multiple guards. The constructed strategy will be something like
@@ -45,6 +44,17 @@ If you have updated the buy strategy, ie. changed the contents of
 `populate_buy_trend()` method you have to update the `guards` and
 `triggers` hyperopts must use.
 
+#### Sell optimization
+
+Similar to the buy-signal above, sell-signals can also be optimized. 
+Place the corresponding settings into the following methods
+
+- Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
+- Inside `populate_sell_trend()` - applying the parameters.
+
+The configuration and rules are the same than for buy signals.
+To avoid naming collisions in the search-space, please prefix all sell-spaces with sell-. 
+
 ## Solving a Mystery
 
 Let's say you are curious: should you use MACD crossings or lower Bollinger
@@ -125,11 +135,10 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
 We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
 
 ```bash
-python3 ./freqtrade/main.py -s <strategyname> --hyperopt <hyperoptname> -c config.json hyperopt -e 5000
+python3 ./freqtrade/main.py --hyperopt <hyperoptname> -c config.json hyperopt -e 5000
 ```
 
-Use `<strategyname>` and `<hyperoptname>` as the names of the custom strategy
+Use  `<hyperoptname>` as the name of the custom hyperopt used.
-(only required for generating sells) and the custom hyperopt used.
 
 The `-e` flag will set how many evaluations hyperopt will do. We recommend
 running at least several thousand evaluations.
@@ -162,6 +171,7 @@ Legal values are:
 
 - `all`: optimize everything
 - `buy`: just search for a new buy strategy
+- `sell`: just search for a new sell strategy
 - `roi`: just optimize the minimal profit table for your strategy
 - `stoploss`: search for the best stoploss value
 - space-separated list of any of the above values for example `--spaces roi stoploss`
@@ -237,6 +247,7 @@ Once the optimized strategy has been implemented into your strategy, you should
 To archive the same results (number of trades, ...) than during hyperopt, please use the command line flag `--disable-max-market-positions`.
 This setting is the default for hyperopt for speed reasons. You can overwrite this in the configuration by setting `"position_stacking"=false` or by changing the relevant line in your hyperopt file [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L283).
 
+!!! Note:
 Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.
 
 ## Next Step