Merge pull request #5977 from freqtrade/new_release

New release 2021.11

.github/ISSUE_TEMPLATE/bug_report.md

@@ -9,7 +9,7 @@ assignees: ''
 <!-- 
 Have you searched for similar issues before posting it?
 
-If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
+If you have discovered a bug in the bot, please [search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
 If it hasn't been reported, please create a new issue.
 
 Please do not use bug reports to request new features.

.github/ISSUE_TEMPLATE/question.md

@@ -22,4 +22,4 @@ Please do not use the question template to report bugs or to request new feature
   
 ## Your question
 
-*Ask the question you have not been able to find an answer in our [Documentation](https://www.freqtrade.io/en/latest/)*
+*Ask the question you have not been able to find an answer in the [Documentation](https://www.freqtrade.io/en/latest/)*

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,14 +2,16 @@ Thank you for sending your pull request. But first, have you included
 unit tests, and is your code PEP8 conformant? [More details](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
 
 ## Summary
+
 Explain in one sentence the goal of this PR
 
 Solve the issue: #___
 
 ## Quick changelog
 
-- <change log #1>
-- <change log #2>
+- <change log 1>
+- <change log 1>
 
 ## What's new?
+
 *Explain in details what this PR solve or improve. You can include visuals.* 

.github/workflows/ci.yml

@@ -87,7 +87,7 @@ jobs:
       run: |
         cp config_examples/config_bittrex.example.json config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
     - name: Flake8
       run: |
@@ -180,7 +180,7 @@ jobs:
       run: |
         cp config_examples/config_bittrex.example.json config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
     - name: Flake8
       run: |
@@ -247,7 +247,7 @@ jobs:
       run: |
         cp config_examples/config_bittrex.example.json config.json
         freqtrade create-userdir --userdir user_data
-        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
     - name: Flake8
       run: |

.travis.yml

@@ -33,7 +33,7 @@ jobs:
     - script:
       - cp config_examples/config_bittrex.example.json config.json
       - freqtrade create-userdir --userdir user_data
-      - freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily
+      - freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily
       name: hyperopt
     - script: flake8
       name: flake8

CONTRIBUTING.md

@@ -56,6 +56,13 @@ To help with that, we encourage you to install the git pre-commit
 hook that will warn you when you try to commit code that fails these checks. 
 Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
 
+##### Additional styles applied
+
+* Have docstrings on all public methods
+* Use double-quotes for docstrings
+* Multiline docstrings should be indented to the level of the first quote
+* Doc-strings should follow the reST format (`:param xxx: ...`, `:return: ...`, `:raises KeyError: ... `)
+
 ### 3. Test if all type-hints are correct
 
 #### Run mypy

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9.6-slim-buster as base
+FROM python:3.9.9-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8
@@ -13,7 +13,7 @@ RUN mkdir /freqtrade \
   && apt-get update \
   && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev  \
   && apt-get clean \
-  && useradd -u 1000 -G sudo -U -m ftuser \
+  && useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \
   && chown ftuser:ftuser /freqtrade \
   # Allow sudoers
   && echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers

README.md

@@ -28,8 +28,10 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
 
 - [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#binance-blacklist))
 - [X] [Bittrex](https://bittrex.com/)
-- [X] [Kraken](https://kraken.com/)
 - [X] [FTX](https://ftx.com)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [Kraken](https://kraken.com/)
+- [X] [OKEX](https://www.okex.com/)
 - [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
 ### Community tested
@@ -43,7 +45,7 @@ Exchanges confirmed working by the community:
 
 We invite you to read the bot documentation to ensure you understand how the bot is working.
 
-Please find the complete documentation on our [website](https://www.freqtrade.io).
+Please find the complete documentation on the [freqtrade website](https://www.freqtrade.io).
 
 ## Features
 
@@ -52,7 +54,7 @@ Please find the complete documentation on our [website](https://www.freqtrade.io
 - [x] **Dry-run**: Run the bot without paying money.
 - [x] **Backtesting**: Run a simulation of your buy/sell strategy.
 - [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
-- [x] **Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/latest/edge/).
+- [x] **Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/stable/edge/).
 - [x] **Whitelist crypto-currencies**: Select which crypto-currency you want to trade or use dynamic whitelists.
 - [x] **Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.
 - [x] **Manageable via Telegram**: Manage the bot with Telegram.
@@ -65,12 +67,12 @@ Please find the complete documentation on our [website](https://www.freqtrade.io
 Freqtrade provides a Linux/macOS script to install all dependencies and help you to configure the bot.
 
 ```bash
-git clone -b develop https://github.com/freqtrade/freqtrade.git 
+git clone -b develop https://github.com/freqtrade/freqtrade.git
 cd freqtrade
 ./setup.sh --install
 ```
 
-For any other type of installation please refer to [Installation doc](https://www.freqtrade.io/en/latest/installation/).
+For any other type of installation please refer to [Installation doc](https://www.freqtrade.io/en/stable/installation/).
 
 ## Basic Usage
 
@@ -78,22 +80,22 @@ For any other type of installation please refer to [Installation doc](https://ww
 
 ```
 usage: freqtrade [-h] [-V]
-                 {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+                 {trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
                  ...
 
 Free, open source crypto trading bot
 
 positional arguments:
-  {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+  {trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
     trade               Trade module.
     create-userdir      Create user-data directory.
     new-config          Create new config
-    new-hyperopt        Create new hyperopt
     new-strategy        Create new strategy
     download-data       Download backtesting data.
     convert-data        Convert candle (OHLCV) data from one format to
                         another.
     convert-trade-data  Convert trade data from one format to another.
+    list-data           List downloaded data.
     backtesting         Backtesting module.
     edge                Edge module.
     hyperopt            Hyperopt module.
@@ -107,8 +109,10 @@ positional arguments:
     list-timeframes     Print available timeframes for the exchange.
     show-trades         Show trades.
     test-pairlist       Test your pairlist configuration.
+    install-ui          Install FreqUI
     plot-dataframe      Plot candles with indicators.
     plot-profit         Generate plot showing profits.
+    webserver           Webserver module.
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -118,7 +122,7 @@ optional arguments:
 
 ### Telegram RPC commands
 
-Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
+Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on the [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
 
 - `/start`: Starts the trader.
 - `/stop`: Stops the trader.
@@ -149,10 +153,10 @@ For any questions not covered by the documentation or for further information ab
 ### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
 
 If you discover a bug in the bot, please
-[search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
+[search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
 first. If it hasn't been reported, please
 [create a new issue](https://github.com/freqtrade/freqtrade/issues/new/choose) and
-ensure you follow the template guide so that our team can assist you as
+ensure you follow the template guide so that the team can assist you as
 quickly as possible.
 
 ### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
@@ -166,13 +170,13 @@ in the bug reports.
 
 ### [Pull Requests](https://github.com/freqtrade/freqtrade/pulls)
 
-Feel like our bot is missing a feature? We welcome your pull requests!
+Feel like the bot is missing a feature? We welcome your pull requests!
 
-Please read our
+Please read the
 [Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
 to understand the requirements before sending your pull-requests.
 
-Coding is not a necessity to contribute - maybe start with improving our documentation?
+Coding is not a necessity to contribute - maybe start with improving the documentation?
 Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
 
 **Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/p7nuUNVfP7) (please use the #dev channel for this). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.

build_helpers/install_ta-lib.sh

@@ -11,10 +11,18 @@ if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
   && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess \
   && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub \
   && ./configure --prefix=${INSTALL_LOC}/ \
-  && make -j$(nproc) \
-  && which sudo && sudo make install || make install \
-  && cd ..
+  && make
+  if [ $? -ne 0 ]; then
+    echo "Failed building ta-lib."
+    cd .. && rm -rf ./ta-lib/
+    exit 1
+  fi
+  which sudo && sudo make install || make install
+  if [ -x "$(command -v apt-get)" ]; then
+    echo "Updating library path using ldconfig"
+    sudo ldconfig
+  fi
+  cd .. && rm -rf ./ta-lib/
 else
   echo "TA-lib already installed, skipping installation"
 fi
-#  && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \

config_examples/config_binance.example.json

@@ -28,10 +28,8 @@
         "name": "binance",
         "key": "your_exchange_key",
         "secret": "your_exchange_secret",
-        "ccxt_config": {"enableRateLimit": true},
+        "ccxt_config": {},
         "ccxt_async_config": {
-            "enableRateLimit": true,
-            "rateLimit": 200
         },
         "pair_whitelist": [
             "ALGO/BTC",

config_examples/config_ftx.example.json

@@ -28,11 +28,8 @@
         "name": "ftx",
         "key": "your_exchange_key",
         "secret": "your_exchange_secret",
-        "ccxt_config": {"enableRateLimit": true},
-        "ccxt_async_config": {
-            "enableRateLimit": true,
-            "rateLimit": 50
-        },
+        "ccxt_config": {},
+        "ccxt_async_config": {},
         "pair_whitelist": [
             "BTC/USD",
             "ETH/USD",

config_examples/config_full.example.json

@@ -28,6 +28,7 @@
     "unfilledtimeout": {
         "buy": 10,
         "sell": 30,
+        "exit_timeout_count": 0,
         "unit": "minutes"
     },
     "bid_strategy": {
@@ -84,12 +85,8 @@
         "key": "your_exchange_key",
         "secret": "your_exchange_secret",
         "password": "",
-        "ccxt_config": {"enableRateLimit": true},
-        "ccxt_async_config": {
-            "enableRateLimit": true,
-            "rateLimit": 500,
-            "aiohttp_trust_env": false
-        },
+        "ccxt_config": {},
+        "ccxt_async_config": {},
         "pair_whitelist": [
             "ALGO/BTC",
             "ATOM/BTC",
@@ -149,7 +146,9 @@
             },
             "sell_fill": "on",
             "buy_cancel": "on",
-            "sell_cancel": "on"
+            "sell_cancel": "on",
+            "protection_trigger": "off",
+            "protection_trigger_global": "on"
         },
         "reload": true,
         "balance_dust_level": 0.01

config_examples/config_kraken.example.json

@@ -28,10 +28,8 @@
         "name": "kraken",
         "key": "your_exchange_key",
         "secret": "your_exchange_key",
-        "ccxt_config": {"enableRateLimit": true},
+        "ccxt_config": {},
         "ccxt_async_config": {
-            "enableRateLimit": true,
-            "rateLimit": 1000
         },
         "pair_whitelist": [
             "ADA/EUR",

docker-compose.yml

@@ -15,10 +15,10 @@ services:
     volumes:
       - "./user_data:/freqtrade/user_data"
     # Expose api on port 8080 (localhost only)
-    # Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
+    # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
     # before enabling this.
-    # ports:
-    #   - "127.0.0.1:8080:8080"
+    ports:
+      - "127.0.0.1:8080:8080"
     # Default command used when running `docker compose up`
     command: >
       trade

docker/Dockerfile.armhf

@@ -1,4 +1,4 @@
-FROM python:3.7.10-slim-buster as base
+FROM python:3.9.9-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8

docs/advanced-hyperopt.md

@@ -67,10 +67,10 @@ Currently, the arguments are:
 This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
 
 !!! Note
-    This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
+    This function is called once per epoch - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
 
-!!! Note
-    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
+!!! Note "`*args` and `**kwargs`"
+    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface in the future.
 
 ## Overriding pre-defined spaces
 
@@ -80,10 +80,56 @@ To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_sp
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
         # Define a custom stoploss space.
-        def stoploss_space(self):
+        def stoploss_space():
             return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]
+
+        # Define custom ROI space
+        def roi_space() -> List[Dimension]:
+            return [
+                Integer(10, 120, name='roi_t1'),
+                Integer(10, 60, name='roi_t2'),
+                Integer(10, 40, name='roi_t3'),
+                SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
+                SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
+                SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
+            ]
 ```
 
+!!! Note
+    All overrides are optional and can be mixed/matched as necessary.
+
+### Overriding Base estimator
+
+You can define your own estimator for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator():
+            return "RF"
+
+```
+
+Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the [scikit-optimize documentation](https://scikit-optimize.github.io/)), or "an instance of a class that inherits from `RegressorMixin` (from sklearn) and where the `predict` method has an optional `return_std` argument, which returns `std(Y | x)` along with `E[Y | x]`".
+
+Some research will be necessary to find additional Regressors.
+
+Example for `ExtraTreesRegressor` ("ET") with additional parameters:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator():
+            from skopt.learning import ExtraTreesRegressor
+            # Corresponds to "ET" - but allows additional parameters.
+            return ExtraTreesRegressor(n_estimators=100)
+
+```
+
+!!! Note
+    While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
+    If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.
+
 ## Space options
 
 For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:
@@ -105,281 +151,3 @@ from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal,
     Assuming the definition of a rather small space (`SKDecimal(0.10, 0.15, decimals=2, name='xxx')`) - SKDecimal will have 5 possibilities (`[0.10, 0.11, 0.12, 0.13, 0.14, 0.15]`).
 
     A corresponding real space `Real(0.10, 0.15 name='xxx')`  on the other hand has an almost unlimited number of possibilities (`[0.10, 0.010000000001, 0.010000000002, ... 0.014999999999, 0.01500000000]`).
-
----
-
-## Legacy Hyperopt
-
-This Section explains the configuration of an explicit Hyperopt file (separate to the strategy).
-
-!!! Warning "Deprecated / legacy mode"
-    Since the 2021.4 release you no longer have to write a separate hyperopt class, but all strategies can be hyperopted.
-    Please read the [main hyperopt page](hyperopt.md) for more details.
-
-### Prepare hyperopt file
-
-Configuring an explicit hyperopt file is similar to writing your own strategy, and many tasks will be similar.
-
-!!! Tip "About this page"
-    For this page, we will be using a fictional strategy called `AwesomeStrategy` - which will be optimized using the `AwesomeHyperopt` class.
-
-#### Create a Custom Hyperopt File
-
-The simplest way to get started is to use the following command, which will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
-
-Let assume you want a hyperopt file `AwesomeHyperopt.py`:
-
-``` bash
-freqtrade new-hyperopt --hyperopt AwesomeHyperopt
-```
-
-#### Legacy Hyperopt checklist
-
-Checklist on all tasks / possibilities in hyperopt
-
-Depending on the space you want to optimize, only some of the below are required:
-
-* fill `buy_strategy_generator` - for buy signal optimization
-* fill `indicator_space` - for buy signal optimization
-* fill `sell_strategy_generator` - for sell signal optimization
-* fill `sell_indicator_space` - for sell signal optimization
-
-!!! Note
-    `populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
-
-Optional in hyperopt - can also be loaded from a strategy (recommended):
-
-* `populate_indicators` - fallback to create indicators
-* `populate_buy_trend` - fallback if not optimizing for buy space. should come from strategy
-* `populate_sell_trend` - fallback if not optimizing for sell space. should come from strategy
-
-!!! Note
-    You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
-    Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
-
-Rarely you may also need to override:
-
-* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
-* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
-* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
-* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
-
-#### Defining a buy signal optimization
-
-Let's say you are curious: should you use MACD crossings or lower Bollinger
-Bands to trigger your buys. And you also wonder should you use RSI or ADX to
-help with those buy decisions. If you decide to use RSI or ADX, which values
-should I use for them? So let's use hyperparameter optimization to solve this
-mystery.
-
-We will start by defining a search space:
-
-```python
-    def indicator_space() -> List[Dimension]:
-        """
-        Define your Hyperopt space for searching strategy parameters
-        """
-        return [
-            Integer(20, 40, name='adx-value'),
-            Integer(20, 40, name='rsi-value'),
-            Categorical([True, False], name='adx-enabled'),
-            Categorical([True, False], name='rsi-enabled'),
-            Categorical(['bb_lower', 'macd_cross_signal'], name='trigger')
-        ]
-```
-
-Above definition says: I have five parameters I want you to randomly combine
-to find the best combination. Two of them are integer values (`adx-value` and `rsi-value`) and I want you test in the range of values 20 to 40.  
-Then we have three category variables. First two are either `True` or `False`.
-We use these to either enable or disable the ADX and RSI guards.
-The last one we call `trigger` and use it to decide which buy trigger we want to use.
-
-So let's write the buy strategy generator using these values:
-
-```python
-    @staticmethod
-    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
-        """
-        Define the buy strategy parameters to be used by Hyperopt.
-        """
-        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
-            conditions = []
-            # GUARDS AND TRENDS
-            if 'adx-enabled' in params and params['adx-enabled']:
-                conditions.append(dataframe['adx'] > params['adx-value'])
-            if 'rsi-enabled' in params and params['rsi-enabled']:
-                conditions.append(dataframe['rsi'] < params['rsi-value'])
-
-            # TRIGGERS
-            if 'trigger' in params:
-                if params['trigger'] == 'bb_lower':
-                    conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
-                if params['trigger'] == 'macd_cross_signal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['macd'], dataframe['macdsignal']
-                    ))
-
-            # Check that volume is not 0
-            conditions.append(dataframe['volume'] > 0)
-
-            if conditions:
-                dataframe.loc[
-                    reduce(lambda x, y: x & y, conditions),
-                    'buy'] = 1
-
-            return dataframe
-
-        return populate_buy_trend
-```
-
-Hyperopt will now call `populate_buy_trend()` many times (`epochs`) with different value combinations.  
-It will use the given historical data and make buys based on the buy signals generated with the above function.  
-Based on the results, hyperopt will tell you which parameter combination produced the best results (based on the configured [loss function](#loss-functions)).
-
-!!! Note
-    The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
-    When you want to test an indicator that isn't used by the bot currently, remember to
-    add it to the `populate_indicators()` method in your strategy or hyperopt file.
-
-#### 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.
-* Within `sell_strategy_generator()` - populate the nested method `populate_sell_trend()` to apply 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-`.
-
-### Execute Hyperopt
-
-Once you have updated your hyperopt configuration you can run it.
-Because hyperopt tries a lot of combinations to find the best parameters it will take time to get a good result. More time usually results in better results.
-
-We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
-
-```bash
-freqtrade hyperopt --config config.json --hyperopt <hyperoptname> --hyperopt-loss <hyperoptlossname> --strategy <strategyname> -e 500 --spaces all
-```
-
-Use `<hyperoptname>` as the name of the custom hyperopt used.
-
-The `-e` option will set how many evaluations hyperopt will do. Since hyperopt uses Bayesian search, running too many epochs at once may not produce greater results. Experience has shown that best results are usually not improving much after 500-1000 epochs.  
-Doing multiple runs (executions) with a few 1000 epochs and different random state will most likely produce different results.
-
-The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
-
-!!! Note
-    Hyperopt will store hyperopt results with the timestamp of the hyperopt start time.
-    Reading commands (`hyperopt-list`, `hyperopt-show`) can use `--hyperopt-filename <filename>` to read and display older hyperopt results.
-    You can find a list of filenames with `ls -l user_data/hyperopt_results/`.
-
-#### Running Hyperopt using methods from a strategy
-
-Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
-
-```bash
-freqtrade hyperopt --hyperopt AwesomeHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy AwesomeStrategy
-```
-
-### Understand the Hyperopt Result
-
-Once Hyperopt is completed you can use the result to create a new strategy.
-Given the following result from hyperopt:
-
-```
-Best result:
-
-    44/100:    135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722%). Avg duration 180.4 mins. Objective: 1.94367
-
-Buy hyperspace params:
-{    'adx-value': 44,
-     'rsi-value': 29,
-     'adx-enabled': False,
-     'rsi-enabled': True,
-     'trigger': 'bb_lower'}
-```
-
-You should understand this result like:
-
-* The buy trigger that worked best was `bb_lower`.
-* You should not use ADX because `adx-enabled: False`)
-* You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
-
-You have to look inside your strategy file into `buy_strategy_generator()`
-method, what those values match to.
-
-So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
-
-```python
-(dataframe['rsi'] < 29.0)
-```
-
-Translating your whole hyperopt result as the new buy-signal would then look like:
-
-```python
-def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
-    dataframe.loc[
-        (
-            (dataframe['rsi'] < 29.0) &  # rsi-value
-            dataframe['close'] < dataframe['bb_lowerband']  # trigger
-        ),
-        'buy'] = 1
-    return dataframe
-```
-
-### Validate backtesting results
-
-Once the optimized parameters and conditions have been implemented into your strategy, you should backtest the strategy to make sure everything is working as expected.
-
-To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
-
-Should results don't match, please double-check to make sure you transferred all conditions correctly.
-Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
-You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).
-
-### Sharing methods with your strategy
-
-Hyperopt classes provide access to the Strategy via the `strategy` class attribute.
-This can be a great way to reduce code duplication if used correctly, but will also complicate usage for inexperienced users.
-
-``` python
-from pandas import DataFrame
-from freqtrade.strategy.interface import IStrategy
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-
-class MyAwesomeStrategy(IStrategy):
-
-    buy_params = {
-        'rsi-value': 30,
-        'adx-value': 35,
-    }
-
-    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        return self.buy_strategy_generator(self.buy_params, dataframe, metadata)
-
-    @staticmethod
-    def buy_strategy_generator(params, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe.loc[
-            (
-                qtpylib.crossed_above(dataframe['rsi'], params['rsi-value']) &
-                dataframe['adx'] > params['adx-value']) &
-                dataframe['volume'] > 0
-            )
-            , 'buy'] = 1
-        return dataframe
-
-class MyAwesomeHyperOpt(IHyperOpt):
-    ...
-    @staticmethod
-    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
-        """
-        Define the buy strategy parameters to be used by Hyperopt.
-        """
-        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
-            # Call strategy's buy strategy generator
-            return self.StrategyClass.buy_strategy_generator(params, dataframe, metadata)
-
-        return populate_buy_trend
-```

docs/advanced-setup.md

@@ -52,6 +52,71 @@ freqtrade trade -c MyConfigUSDT.json -s MyCustomStrategy --db-url sqlite:///user
 
 For more information regarding usage of the sqlite databases, for example to manually enter or remove trades, please refer to the [SQL Cheatsheet](sql_cheatsheet.md).
 
+### Multiple instances using docker
+
+To run multiple instances of freqtrade using docker you will need to edit the docker-compose.yml file and add all the instances you want as separate services. Remember, you can separate your configuration into multiple files, so it's a good idea to think about making them modular, then if you need to edit something common to all bots, you can do that in a single config file. 
+``` yml
+---
+version: '3'
+services:
+  freqtrade1:
+    image: freqtradeorg/freqtrade:stable
+    # image: freqtradeorg/freqtrade:develop
+    # Use plotting image
+    # image: freqtradeorg/freqtrade:develop_plot
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: always
+    container_name: freqtrade1
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
+    # before enabling this.
+     ports:
+     - "127.0.0.1:8080:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade1.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3_freqtrade1.sqlite
+      --config /freqtrade/user_data/config.json
+      --config /freqtrade/user_data/config.freqtrade1.json
+      --strategy SampleStrategy
+  
+  freqtrade2:
+    image: freqtradeorg/freqtrade:stable
+    # image: freqtradeorg/freqtrade:develop
+    # Use plotting image
+    # image: freqtradeorg/freqtrade:develop_plot
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: always
+    container_name: freqtrade2
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
+    # before enabling this.
+    ports:
+      - "127.0.0.1:8081:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade2.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3_freqtrade2.sqlite
+      --config /freqtrade/user_data/config.json
+      --config /freqtrade/user_data/config.freqtrade2.json
+      --strategy SampleStrategy
+
+```
+You can use whatever naming convention you want, freqtrade1 and 2 are arbitrary. Note, that you will need to use different database files, port mappings and telegram configurations for each instance, as mentioned above. 
+
+
 ## Configure the bot running as a systemd service
 
 Copy the `freqtrade.service` file to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.

docs/backtesting.md

@@ -18,8 +18,10 @@ usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                              [-p PAIRS [PAIRS ...]] [--eps] [--dmmp]
                              [--enable-protections]
                              [--dry-run-wallet DRY_RUN_WALLET]
+                             [--timeframe-detail TIMEFRAME_DETAIL]
                              [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
                              [--export {none,trades}] [--export-filename PATH]
+                             [--breakdown {day,week,month} [{day,week,month} ...]]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -29,7 +31,7 @@ optional arguments:
                         Specify what timerange of data to use.
   --data-format-ohlcv {json,jsongz,hdf5}
                         Storage format for downloaded candle (OHLCV) data.
-                        (default: `None`).
+                        (default: `json`).
   --max-open-trades INT
                         Override the value of the `max_open_trades`
                         configuration setting.
@@ -55,14 +57,16 @@ optional arguments:
   --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
                         Starting balance, used for backtesting / hyperopt and
                         dry-runs.
+  --timeframe-detail TIMEFRAME_DETAIL
+                        Specify detail timeframe for backtesting (`1m`, `5m`,
+                        `30m`, `1h`, `1d`).
   --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
                         Provide a space-separated list of strategies to
                         backtest. Please note that ticker-interval needs to be
                         set either in config or via command line. When using
                         this together with `--export trades`, the strategy-
                         name is injected into the filename (so `backtest-
-                        data.json` becomes `backtest-data-
-                        SampleStrategy.json`
+                        data.json` becomes `backtest-data-SampleStrategy.json`
   --export {none,trades}
                         Export backtest results (default: trades).
   --export-filename PATH
@@ -70,6 +74,8 @@ optional arguments:
                         Requires `--export` to be set as well. Example:
                         `--export-filename=user_data/backtest_results/backtest
                         _today.json`
+  --breakdown {day,week,month} [{day,week,month} ...]
+                        Show backtesting breakdown per [day, week, month].
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -425,7 +431,37 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
 - `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
 
-### Assumptions made by backtesting
+### Daily / Weekly / Monthly breakdown
+
+You can get an overview over daily / weekly or monthly results by using the `--breakdown <>` switch.
+
+To visualize daily and weekly breakdowns, you can use the following:
+
+``` bash
+freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day month
+```
+
+``` output
+======================== DAY BREAKDOWN =========================
+|        Day |   Tot Profit USDT |   Wins |   Draws |   Losses |
+|------------+-------------------+--------+---------+----------|
+| 03/07/2021 |           200.0   |      2 |       0 |        0 |
+| 04/07/2021 |           -50.31  |      0 |       0 |        2 |
+| 05/07/2021 |           220.611 |      3 |       2 |        0 |
+| 06/07/2021 |           150.974 |      3 |       0 |        2 |
+| 07/07/2021 |           -70.193 |      1 |       0 |        2 |
+| 08/07/2021 |           212.413 |      2 |       0 |        3 |
+
+```
+
+The output will show a table containing the realized absolute Profit (in stake currency) for the given timeperiod, as well as wins, draws and losses that materialized (closed) on this day.
+
+### Further backtest-result analysis
+
+To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
+You can then load the trades to perform further analysis as shown in the [data analysis](data-analysis.md#backtesting) backtesting section.
+
+## Assumptions made by backtesting
 
 Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
 
@@ -442,6 +478,7 @@ Since backtesting lacks some detailed information about what happens within a ca
 - Low happens before high for stoploss, protecting capital first
 - Trailing stoploss
   - Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
+  - On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point
   - High happens first - adjusting stoploss
   - Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
   - ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
@@ -456,10 +493,30 @@ Also, keep in mind that past results don't guarantee future success.
 
 In addition to the above assumptions, strategy authors should carefully read the [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) section, to avoid using data in backtesting which is not available in real market conditions.
 
-### Further backtest-result analysis
+### Improved backtest accuracy
 
-To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
-You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
+One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).
+So assuming you run backtesting with a 1h timeframe, there will be 4 prices for that candle (Open, High, Low, Close).
+
+While backtesting does take some assumptions (read above) about this - this can never be perfect, and will always be biased in one way or the other.
+To mitigate this, freqtrade can use a lower (faster) timeframe to simulate intra-candle movements.
+
+To utilize this, you can append `--timeframe-detail 5m` to your regular backtesting command.
+
+``` bash
+freqtrade backtesting --strategy AwesomeStrategy --timeframe 1h --timeframe-detail 5m
+```
+
+This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe - and for every "open trade candle" (candles where a trade is open) the 5m data will be used to simulate intra-candle movements.
+All callback functions (`custom_sell()`, `custom_stoploss()`, ... ) will be running for each 5m candle once the trade is opened (so 12 times in the above example of 1h timeframe, and 5m detailed timeframe).
+
+`--timeframe-detail` must be smaller than the original timeframe, otherwise backtesting will fail to start.
+
+Obviously this will require more memory (5m data is bigger than 1h data), and will also impact runtime (depending on the amount of trades and trade durations).
+Also, data must be available / downloaded already.
+
+!!! Tip
+    You can use this function as the last part of strategy development, to ensure your strategy is not exploiting one of the [backtesting assumptions](#assumptions-made-by-backtesting). Strategies that perform similarly well with this mode have a good chance to perform well in dry/live modes too (although only forward-testing (dry-mode) can really confirm a strategy).
 
 ## Backtesting multiple strategies
 

docs/bot-basics.md

@@ -7,7 +7,7 @@ This page provides you some basic concepts on how Freqtrade works and operates.
 * **Strategy**: Your trading strategy, telling the bot what to do.
 * **Trade**: Open position.
 * **Open Order**: Order which is currently placed on the exchange, and is not yet complete.
-* **Pair**: Tradable pair, usually in the format of Quote/Base (e.g. XRP/USDT).
+* **Pair**: Tradable pair, usually in the format of Base/Quote (e.g. XRP/USDT).
 * **Timeframe**: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
 * **Indicators**: Technical indicators (SMA, EMA, RSI, ...).
 * **Limit order**: Limit orders which execute at the defined limit price or better.

docs/bot-usage.md

@@ -12,22 +12,22 @@ This page explains the different parameters of the bot and how to run it.
 
 ```
 usage: freqtrade [-h] [-V]
-                 {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+                 {trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
                  ...
 
 Free, open source crypto trading bot
 
 positional arguments:
-  {trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
+  {trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
     trade               Trade module.
     create-userdir      Create user-data directory.
     new-config          Create new config
-    new-hyperopt        Create new hyperopt
     new-strategy        Create new strategy
     download-data       Download backtesting data.
     convert-data        Convert candle (OHLCV) data from one format to
                         another.
     convert-trade-data  Convert trade data from one format to another.
+    list-data           List downloaded data.
     backtesting         Backtesting module.
     edge                Edge module.
     hyperopt            Hyperopt module.
@@ -41,8 +41,10 @@ positional arguments:
     list-timeframes     Print available timeframes for the exchange.
     show-trades         Show trades.
     test-pairlist       Test your pairlist configuration.
+    install-ui          Install FreqUI
     plot-dataframe      Plot candles with indicators.
     plot-profit         Generate plot showing profits.
+    webserver           Webserver module.
 
 optional arguments:
   -h, --help            show this help message and exit

docs/configuration.md

@@ -37,6 +37,15 @@ Using this scheme, all configuration settings will also be available as environm
 
 Please note that Environment variables will overwrite corresponding settings in your configuration, but command line Arguments will always win.
 
+Common example:
+
+```
+FREQTRADE__TELEGRAM__CHAT_ID=<telegramchatid>
+FREQTRADE__TELEGRAM__TOKEN=<telegramToken>
+FREQTRADE__EXCHANGE__KEY=<yourExchangeKey>
+FREQTRADE__EXCHANGE__SECRET=<yourExchangeSecret>
+```
+
 !!! Note
     Environment variables detected are logged at startup - so if you can't find why a value is not what you think it should be based on the configuration, make sure it's not loaded from an environment variable.
 
@@ -93,6 +102,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `unfilledtimeout.buy` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
 | `unfilledtimeout.sell` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
 | `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy). <br> *Defaults to `minutes`.* <br> **Datatype:** String
+| `unfilledtimeout.exit_timeout_count` | How many times can exit orders time out. Once this number of timeouts is reached, an emergency sell is triggered. 0 to disable and allow unlimited order cancels. [Strategy Override](#parameters-in-the-strategy).<br>*Defaults to `0`.* <br> **Datatype:** Integer
 | `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
 | `bid_strategy.ask_last_balance` | **Required.** Interpolate the bidding price. More information [below](#buy-price-without-orderbook-enabled).
 | `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
@@ -192,9 +202,8 @@ There are several methods to configure how much of the stake currency the bot wi
 #### Minimum trade stake
 
 The minimum stake amount will depend on exchange and pair and is usually listed in the exchange support pages.
-Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.6$.
 
-The minimum stake amount to buy this pair is, therefore, `20 * 0.6 ~= 12`.
+Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.6$, the minimum stake amount to buy this pair is `20 * 0.6 ~= 12`.
 This exchange has also a limit on USD - where all orders must be > 10$ - which however does not apply in this case.
 
 To guarantee safe execution, freqtrade will not allow buying with a stake-amount of 10.1$, instead, it'll make sure that there's enough space to place a stoploss below the pair (+ an offset, defined by `amount_reserve_percent`, which defaults to 5%).
@@ -204,7 +213,7 @@ With a reserve of 5%, the minimum stake amount would be ~12.6$ (`12 * (1 + 0.05)
 To limit this calculation in case of large stoploss values, the calculated minimum stake-limit will never be more than 50% above the real limit.
 
 !!! Warning
-    Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange.
+    Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange. Freqtrade adjusts the stake-amount to this value, unless it's > 30% more than the calculated/desired stake-amount - in which case the trade is rejected.
 
 #### Tradable balance
 
@@ -444,47 +453,8 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
 ```
 
 !!! Warning
-    This is ongoing work. For now, it is supported only for binance.
-    Please don't change the default value unless you know what you are doing and have researched the impact of using different values.
-
-### Exchange configuration
-
-Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
-exchange markets and trading APIs. The complete up-to-date list can be found in the
-[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
- However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
- so these are the only officially supported exchanges:
-
-- [Bittrex](https://bittrex.com/): "bittrex"
-- [Binance](https://www.binance.com/): "binance"
-- [Kraken](https://kraken.com/): "kraken"
-
-Feel free to test other exchanges and submit your PR to improve the bot.
-
-Some exchanges require special configuration, which can be found on the [Exchange-specific Notes](exchanges.md) documentation page.
-
-#### Sample exchange configuration
-
-A exchange configuration for "binance" would look as follows:
-
-```json
-"exchange": {
-    "name": "binance",
-    "key": "your_exchange_key",
-    "secret": "your_exchange_secret",
-    "ccxt_config": {"enableRateLimit": true},
-    "ccxt_async_config": {
-        "enableRateLimit": true,
-        "rateLimit": 200
-    },
-```
-
-This configuration enables binance, as well as rate-limiting to avoid bans from the exchange.
-`"rateLimit": 200` defines a wait-event of 0.2s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
-
-!!! Note
-    Optimal settings for rate-limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
-    We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
+    This is ongoing work. For now, it is supported only for binance and kucoin.
+    Please don't change the default value unless you know what you are doing and have researched the impact of using different values for your particular exchange.
 
 ### What values can be used for fiat_display_currency?
 

docs/data-download.md

@@ -11,7 +11,7 @@ Otherwise `--exchange` becomes mandatory.
 You can use a relative timerange (`--days 20`) or an absolute starting point (`--timerange 20200101-`). For incremental downloads, the relative approach should be used.
 
 !!! Tip "Tip: Updating existing data"
-    If you already have backtesting data available in your data-directory and would like to refresh this data up to today, do not use `--days` or `--timerange` parameters. Freqtrade will keep the available data and only download the missing data.
+    If you already have backtesting data available in your data-directory and would like to refresh this data up to today, freqtrade will automatically calculate the data missing for the existing pairs and the download will occur from the latest available point until "now", neither --days or --timerange parameters are required. Freqtrade will keep the available data and only download the missing data.
     If you are updating existing data after inserting new pairs that you have no data for, use `--new-pairs-days xx` parameter. Specified number of days will be downloaded for new pairs while old pairs will be updated with missing data only.
     If you use `--days xx` parameter alone - data for specified number of days will be downloaded for _all_ pairs. Be careful, if specified number of days is smaller than gap between now and last downloaded candle - freqtrade will delete all existing data to avoid gaps in candle data.
 
@@ -22,6 +22,7 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                [-d PATH] [--userdir PATH]
                                [-p PAIRS [PAIRS ...]] [--pairs-file FILE]
                                [--days INT] [--new-pairs-days INT]
+                               [--include-inactive-pairs]
                                [--timerange TIMERANGE] [--dl-trades]
                                [--exchange EXCHANGE]
                                [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
@@ -38,6 +39,8 @@ optional arguments:
   --days INT            Download data for given number of days.
   --new-pairs-days INT  Download data of new pairs for given number of days.
                         Default: `None`.
+  --include-inactive-pairs
+                        Also download data from inactive pairs.
   --timerange TIMERANGE
                         Specify what timerange of data to use.
   --dl-trades           Download trades instead of OHLCV data. The bot will
@@ -52,10 +55,10 @@ optional arguments:
                         exchange/pairs/timeframes.
   --data-format-ohlcv {json,jsongz,hdf5}
                         Storage format for downloaded candle (OHLCV) data.
-                        (default: `None`).
+                        (default: `json`).
   --data-format-trades {json,jsongz,hdf5}
                         Storage format for downloaded trades data. (default:
-                        `None`).
+                        `jsongz`).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -80,6 +83,82 @@ Common arguments:
 
     For that reason, `download-data` does not care about the "startup-period" defined in a strategy. It's up to the user to download additional days if the backtest should start at a specific point in time (while respecting startup period).
 
+### Pairs file
+
+In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.
+If you are using Binance for example:
+
+- create a directory `user_data/data/binance` and copy or create the `pairs.json` file in that directory.
+- update the `pairs.json` file to contain the currency pairs you are interested in.
+
+```bash
+mkdir -p user_data/data/binance
+touch user_data/data/binance/pairs.json
+```
+
+The format of the `pairs.json` file is a simple json list.
+Mixing different stake-currencies is allowed for this file, since it's only used for downloading.
+
+``` json
+[
+    "ETH/BTC",
+    "ETH/USDT",
+    "BTC/USDT",
+    "XRP/ETH"
+]
+```
+
+!!! Tip "Downloading all data for one quote currency"
+    Often, you'll want to download data for all pairs of a specific quote-currency. In such cases, you can use the following shorthand:
+    `freqtrade download-data --exchange binance --pairs .*/USDT <...>`. The provided "pairs" string will be expanded to contain all active pairs on the exchange.
+    To also download data for inactive (delisted) pairs, add `--include-inactive-pairs` to the command.
+
+??? Note "Permission denied errors"
+    If your configuration directory `user_data` was made by docker, you may get the following error:
+
+    ```
+    cp: cannot create regular file 'user_data/data/binance/pairs.json': Permission denied
+    ```
+
+    You can fix the permissions of your user-data directory as follows:
+
+    ```
+    sudo chown -R $UID:$GID user_data
+    ```
+
+### Start download
+
+Then run:
+
+```bash
+freqtrade download-data --exchange binance
+```
+
+This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
+
+Alternatively, specify the pairs directly
+
+```bash
+freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT
+```
+
+or as regex (to download all active USDT pairs)
+
+```bash
+freqtrade download-data --exchange binance --pairs .*/USDT
+```
+
+### Other Notes
+
+- To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
+- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust rate limits etc.)
+- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
+- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
+- To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020. Eventually set end dates are ignored.
+- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
+- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
+
+
 ### Data format
 
 Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
@@ -204,6 +283,61 @@ It'll also remove original jsongz data files (`--erase` parameter).
 freqtrade convert-trade-data --format-from jsongz --format-to json --datadir ~/.freqtrade/data/kraken --erase
 ```
 
+### Sub-command trades to ohlcv
+
+When you need to use `--dl-trades` (kraken only) to download data, conversion of trades data to ohlcv data is the last step.
+This command will allow you to repeat this last step for additional timeframes without re-downloading the data.
+
+```
+usage: freqtrade trades-to-ohlcv [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                 [-d PATH] [--userdir PATH]
+                                 [-p PAIRS [PAIRS ...]]
+                                 [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
+                                 [--exchange EXCHANGE]
+                                 [--data-format-ohlcv {json,jsongz,hdf5}]
+                                 [--data-format-trades {json,jsongz,hdf5}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
+                        config is provided.
+  --data-format-ohlcv {json,jsongz,hdf5}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `json`).
+  --data-format-trades {json,jsongz,hdf5}
+                        Storage format for downloaded trades data. (default:
+                        `jsongz`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
+
+#### Example trade-to-ohlcv conversion
+
+``` bash
+freqtrade trades-to-ohlcv --exchange kraken -t 5m 1h 1d --pairs BTC/EUR ETH/EUR
+```
+
 ### Sub-command list-data
 
 You can get a list of downloaded data using the `list-data` sub-command.
@@ -257,64 +391,6 @@ ETH/BTC     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d
 ETH/USDT    5m, 15m, 30m, 1h, 2h, 4h
 ```
 
-### Pairs file
-
-In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.
-
-If you are using Binance for example:
-
-- create a directory `user_data/data/binance` and copy or create the `pairs.json` file in that directory.
-- update the `pairs.json` file to contain the currency pairs you are interested in.
-
-```bash
-mkdir -p user_data/data/binance
-cp tests/testdata/pairs.json user_data/data/binance
-```
-
-If your configuration directory `user_data` was made by docker, you may get the following error:
-
-```
-cp: cannot create regular file 'user_data/data/binance/pairs.json': Permission denied
-```
-
-You can fix the permissions of your user-data directory as follows:
-
-```
-sudo chown -R $UID:$GID user_data
-```
-
-The format of the `pairs.json` file is a simple json list.
-Mixing different stake-currencies is allowed for this file, since it's only used for downloading.
-
-``` json
-[
-    "ETH/BTC",
-    "ETH/USDT",
-    "BTC/USDT",
-    "XRP/ETH"
-]
-```
-
-### Start download
-
-Then run:
-
-```bash
-freqtrade download-data --exchange binance
-```
-
-This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
-
-### Other Notes
-
-- To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
-- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust rate limits etc.)
-- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
-- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
-- To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020. Eventually set end dates are ignored.
-- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
-- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
-
 ### Trades (tick) data
 
 By default, `download-data` sub-command downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.

docs/deprecated.md

@@ -38,3 +38,8 @@ Since only quoteVolume can be compared between assets, the other options (bidVol
 
 Using `order_book_min` and `order_book_max` used to allow stepping the orderbook and trying to find the next ROI slot - trying to place sell-orders early.
 As this does however increase risk and provides no benefit, it's been removed for maintainability purposes in 2021.7.
+
+### Legacy Hyperopt mode
+
+Using separate hyperopt files was deprecated in 2021.4 and was removed in 2021.9.
+Please switch to the new [Parametrized Strategies](hyperopt.md) to benefit from the new hyperopt interface.

docs/developer.md

@@ -8,7 +8,7 @@ All contributions, bug reports, bug fixes, documentation improvements, enhanceme
 
 Documentation is available at [https://freqtrade.io](https://www.freqtrade.io/) and needs to be provided with every new feature PR.
 
-Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/extensions/admonition/).
+Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/reference/admonitions/).
 
 To test the documentation locally use the following commands.
 
@@ -26,6 +26,8 @@ Alternatively (e.g. if your system is not supported by the setup.sh script), fol
 
 This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
 
+Before opening a pull request, please familiarize yourself with our [Contributing Guidelines](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md).
+
 ### Devcontainer setup
 
 The fastest and easiest way to get started is to use [VSCode](https://code.visualstudio.com/) with the Remote container extension.
@@ -250,7 +252,23 @@ Most exchanges supported by CCXT should work out of the box.
 To quickly test the public endpoints of an exchange, add a configuration for your exchange to `test_ccxt_compat.py` and run these tests with `pytest --longrun tests/exchange/test_ccxt_compat.py`.
 Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
 
-Also try to use `freqtrade download-data` for an extended timerange and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
+Also try to use `freqtrade download-data` for an extended timerange (multiple months) and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
+
+These are prerequisites to have an exchange listed as either Supported or Community tested (listed on the homepage).
+The below are "extras", which will make an exchange better (feature-complete) - but are not absolutely necessary for either of the 2 categories.
+
+Additional tests / steps to complete:
+
+* Verify data provided by `fetch_ohlcv()` - and eventually adjust `ohlcv_candle_limit` for this exchange
+* Check L2 orderbook limit range (API documentation) - and eventually set as necessary
+* Check if balance shows correctly (*)
+* Create market order (*)
+* Create limit order (*)
+* Complete trade (buy + sell) (*)
+  * Compare result calculation between exchange and bot
+  * Ensure fees are applied correctly (check the database against the exchange)
+
+(*) Requires API keys and Balance on the exchange.
 
 ### Stoploss On Exchange
 

docs/docker_quickstart.md

@@ -70,6 +70,18 @@ docker-compose up -d
 !!! Warning "Default configuration"
     While the configuration generated will be mostly functional, you will still need to verify that all options correspond to what you want (like Pricing, pairlist, ...) before starting the bot.
 
+#### Accessing the UI
+
+If you've selected to enable FreqUI in the `new-config` step, you will have freqUI available at port `localhost:8080`.
+
+You can now access the UI by typing localhost:8080 in your browser.
+
+??? Note "UI Access on a remote servers"
+    If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
+    This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
+    Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
+    Please also read the [API configuration with docker](rest-api.md#configuration-with-docker) section to learn more about this configuration.
+
 #### Monitoring the bot
 
 You can check for running instances with `docker-compose ps`.
@@ -109,6 +121,7 @@ All freqtrade arguments will be available by running `docker-compose run --rm fr
 !!! Warning "`docker-compose` for trade commands"
     Trade commands (`freqtrade trade <...>`) should not be ran via `docker-compose run` - but should use `docker-compose up -d` instead.
     This makes sure that the container is properly started (including port forwardings) and will make sure that the container will restart after a system reboot.
+    If you intend to use freqUI, please also ensure to adjust the [configuration accordingly](rest-api.md#configuration-with-docker), otherwise the UI will not be available.
 
 !!! Note "`docker-compose run --rm`"
     Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
@@ -147,9 +160,9 @@ You'll then also need to modify the `docker-compose.yml` file and uncomment the
       dockerfile: "./Dockerfile.<yourextension>"
 ```
 
-You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
+You can then run `docker-compose build --pull` to build the docker image, and run it using the commands described above.
 
-## Plotting with docker-compose
+### Plotting with docker-compose
 
 Commands `freqtrade plot-profit` and `freqtrade plot-dataframe` ([Documentation](plotting.md)) are available by changing the image to `*_plot` in your docker-compose.yml file.
 You can then use these commands as follows:
@@ -160,7 +173,7 @@ docker-compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p B
 
 The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
 
-## Data analysis using docker compose
+### Data analysis using docker compose
 
 Freqtrade provides a docker-compose file which starts up a jupyter lab server.
 You can run this server using the following command:
@@ -177,3 +190,22 @@ Since part of this image is built on your machine, it is recommended to rebuild
 ``` bash
 docker-compose -f docker/docker-compose-jupyter.yml build --no-cache
 ```
+
+## Troubleshooting
+
+### Docker on Windows
+
+* Error: `"Timestamp for this request is outside of the recvWindow."`
+  * The market api requests require a synchronized clock but the time in the docker container shifts a bit over time into the past.
+    To fix this issue temporarily you need to run `wsl --shutdown` and restart docker again (a popup on windows 10 will ask you to do so).
+    A permanent solution is either to host the docker container on a linux host or restart the wsl from time to time with the scheduler.
+
+    ``` bash
+    taskkill /IM "Docker Desktop.exe" /F
+    wsl --shutdown
+    start "" "C:\Program Files\Docker\Docker\Docker Desktop.exe"
+    ```
+
+!!! Warning
+    Due to the above, we do not recommend the usage of docker on windows for production setups, but only for experimentation, datadownload and backtesting.
+    Best use a linux-VPS for running freqtrade reliably.

docs/edge.md

@@ -3,7 +3,7 @@
 The `Edge Positioning` module uses probability to calculate your win rate and risk reward ratio. It will use these statistics to control your strategy trade entry points, position size and, stoploss.
 
 !!! Warning
-    WHen using `Edge positioning` with a dynamic whitelist (VolumePairList), make sure to also use `AgeFilter` and set it to at least `calculate_since_number_of_days` to avoid problems with missing data.
+    When using `Edge positioning` with a dynamic whitelist (VolumePairList), make sure to also use `AgeFilter` and set it to at least `calculate_since_number_of_days` to avoid problems with missing data.
 
 !!! Note
     `Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.

docs/exchanges.md

@@ -2,8 +2,60 @@
 
 This page combines common gotchas and informations which are exchange-specific and most likely don't apply to other exchanges.
 
+## Exchange configuration
+
+Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
+exchange markets and trading APIs. The complete up-to-date list can be found in the
+[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
+However, the bot was tested by the development team with only a few exchanges.
+A current list of these can be found in the "Home" section of this documentation.
+
+Feel free to test other exchanges and submit your feedback or PR to improve the bot or confirm exchanges that work flawlessly..
+
+Some exchanges require special configuration, which can be found below.
+
+### Sample exchange configuration
+
+A exchange configuration for "binance" would look as follows:
+
+```json
+"exchange": {
+    "name": "binance",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    // ... 
+```
+
+### Setting rate limits
+
+Usually, rate limits set by CCXT are reliable and work well.
+In case of problems related to rate-limits (usually DDOS Exceptions in your logs), it's easy to change rateLimit settings to other values.
+
+```json
+"exchange": {
+    "name": "kraken",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "ccxt_config": {"enableRateLimit": true},
+    "ccxt_async_config": {
+        "enableRateLimit": true,
+        "rateLimit": 3100
+    },
+```
+
+This configuration enables kraken, as well as rate-limiting to avoid bans from the exchange.
+`"rateLimit": 3100` defines a wait-event of 3.1s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
+
+!!! Note
+    Optimal settings for rate-limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
+    We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
+
 ## Binance
 
+Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
+
 !!! Tip "Stoploss on Exchange"
     Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
 
@@ -56,6 +108,12 @@ Bittrex does not support market orders. If you have a message at the bot startup
 Bittrex also does not support `VolumePairlist` due to limited / split API constellation at the moment.
 Please use `StaticPairlist`. Other pairlists (other than `VolumePairlist`) should not be affected.
 
+### Volume pairlist
+
+Bittrex does not support the direct usage of VolumePairList. This can however be worked around by using the advanced mode with `lookback_days: 1` (or more), which will emulate 24h volume.
+
+Read more in the [pairlist documentation](plugins.md#volumepairlist-advanced-mode).
+
 ### Restricted markets
 
 Bittrex split its exchange into US and International versions.
@@ -113,13 +171,34 @@ Kucoin requires a passphrase for each api key, you will therefore need to add th
     "key": "your_exchange_key",
     "secret": "your_exchange_secret",
     "password": "your_exchange_api_key_password",
+    // ...
+}
 ```
 
+Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force).
+
 ### Kucoin Blacklists
 
 For Kucoin, please add `"KCS/<STAKE>"` to your blacklist to avoid issues.
 Accounts having KCS accounts use this to pay for fees - if your first trade happens to be on `KCS`, further trades will consume this position and make the initial KCS trade unsellable as the expected amount is not there anymore.
 
+## OKEX
+
+OKEX requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
+
+```json
+"exchange": {
+    "name": "okex",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "password": "your_exchange_api_key_password",
+    // ...
+}
+```
+
+!!! Warning
+    OKEX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
+
 ## All exchanges
 
 Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
@@ -158,6 +237,8 @@ For example, to test the order type `FOK` with Kraken, and modify candle limit t
         "order_time_in_force": ["gtc", "fok"],
         "ohlcv_candle_limit": 200
         }
+    //...
+}
 ```
 
 !!! Warning

docs/faq.md

@@ -42,7 +42,7 @@ position for a trade. Be patient!
 ### I have made 12 trades already, why is my total profit negative?
 
 I understand your disappointment but unfortunately 12 trades is just
-not enough to say anything. If you run backtesting, you can see that our
+not enough to say anything. If you run backtesting, you can see that the
 current algorithm does leave you on the plus side, but that is after
 thousands of trades and even there, you will be left with losses on
 specific coins that you have traded tens if not hundreds of times. We
@@ -54,9 +54,26 @@ you can't say much from few trades.
 
 Yes. You can edit your config and use the `/reload_config` command to reload the configuration. The bot will stop, reload the configuration and strategy and will restart with the new configuration and strategy.
 
-### I want to improve the bot with a new strategy
+### Why does my bot not sell everything it bought?
 
-That's great. We have a nice backtesting and hyperoptimization setup. See the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
+This is called "coin dust" and can happen on all exchanges.
+It happens because many exchanges subtract fees from the "receiving currency" - so you buy 100 COIN - but you only get 99.9 COIN.
+As COIN is trading in full lot sizes (1COIN steps), you cannot sell 0.9 COIN (or 99.9 COIN) - but you need to round down to 99 COIN.
+
+This is not a bot-problem, but will also happen while manual trading.
+
+While freqtrade can handle this (it'll sell 99 COIN), fees are often below the minimum tradable lot-size (you can only trade full COIN, not 0.9 COIN).
+Leaving the dust (0.9 COIN) on the exchange makes usually sense, as the next time freqtrade buys COIN, it'll eat into the remaining small balance, this time selling everything it bought, and therefore slowly declining the dust balance (although it most likely will never reach exactly 0).
+
+Where possible (e.g. on binance), the use of the exchange's dedicated fee currency will fix this.  
+On binance, it's sufficient to have BNB in your account, and have "Pay fees in BNB" enabled in your profile. Your BNB balance will slowly decline (as it's used to pay fees) - but you'll no longer encounter dust (Freqtrade will include the fees in the profit calculations).
+Other exchanges don't offer such possibilities, where it's simply something you'll have to accept or move to a different exchange.
+
+### I want to use incomplete candles
+
+Freqtrade will not provide incomplete candles to strategies. Using incomplete candles will lead to repainting and consequently to strategies with "ghost" buys, which are impossible to both backtest, and verify after they happened.
+
+You can use "current" market data by using the [dataprovider](strategy-customization.md#orderbookpair-maximum)'s orderbook or ticker methods - which however cannot be used during backtesting.
 
 ### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
 
@@ -76,17 +93,29 @@ If this happens for all pairs in the pairlist, this might indicate a recent exch
 
 Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
 
+### I'm getting "Outdated history for pair xxx" in the log
+
+The bot is trying to tell you that it got an outdated last candle (not the last complete candle).
+As a consequence, Freqtrade will not enter a trade for this pair - as trading on old information is usually not what is desired.
+
+This warning can point to one of the below problems:
+
+* Exchange downtime -> Check your exchange status page / blog / twitter feed for details.
+* Wrong system time -> Ensure your system-time is correct.
+* Barely traded pair -> Check the pair on the exchange webpage, look at the timeframe your strategy uses. If the pair does not have any volume in some candles (usually visualized with a "volume 0" bar, and a "_" as candle), this pair did not have any trades in this timeframe. These pairs should ideally be avoided, as they can cause problems with order-filling.
+* API problem -> API returns wrong data (this only here for completeness, and should not happen with supported exchanges).
+
 ### I'm getting the "RESTRICTED_MARKET" message in the log
 
 Currently known to happen for US Bittrex users.  
 
 Read [the Bittrex section about restricted markets](exchanges.md#restricted-markets) for more information.
 
-### I'm getting the "Exchange Bittrex does not support market orders." message and cannot run my strategy
+### I'm getting the "Exchange XXX does not support market orders." message and cannot run my strategy
 
-As the message says, Bittrex does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Your strategy was probably written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex).
+As the message says, your exchange does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Your strategy was probably written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex and Gate.io).
 
-To fix it for Bittrex, redefine order types in the strategy to use "limit" instead of "market":
+To fix this, redefine order types in the strategy to use "limit" instead of "market":
 
 ```
     order_types = {
@@ -136,6 +165,8 @@ On Windows, the `--logfile` option is also supported by Freqtrade and you can us
 > type \path\to\mylogfile.log | findstr "something"
 ```
 
+## Hyperopt module
+
 ### Why does freqtrade not have GPU support?
 
 First of all, most indicator libraries don't have GPU support - as such, there would be little benefit for indicator calculations.
@@ -152,8 +183,6 @@ The benefit of using GPU would therefore be pretty slim - and will not justify t
 
 There is however nothing preventing you from using GPU-enabled indicators within your strategy if you think you must have this - you will however probably be disappointed by the slim gain that will give you (compared to the complexity).
 
-## Hyperopt module
-
 ### How many epochs do I need to get a good Hyperopt result?
 
 Per default Hyperopt called without the `-e`/`--epochs` command line option will only
@@ -167,7 +196,7 @@ Since hyperopt uses Bayesian search, running for too many epochs may not produce
 It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
 
 ```bash
-freqtrade hyperopt --hyperopt SampleHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
+freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
 ```
 
 ### Why does it take a long time to run hyperopt?

docs/hyperopt.md

@@ -44,14 +44,14 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                           [--data-format-ohlcv {json,jsongz,hdf5}]
                           [--max-open-trades INT]
                           [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
-                          [-p PAIRS [PAIRS ...]] [--hyperopt NAME]
-                          [--hyperopt-path PATH] [--eps] [--dmmp]
-                          [--enable-protections]
+                          [-p PAIRS [PAIRS ...]] [--hyperopt-path PATH]
+                          [--eps] [--dmmp] [--enable-protections]
                           [--dry-run-wallet DRY_RUN_WALLET] [-e INT]
                           [--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
                           [--print-all] [--no-color] [--print-json] [-j JOBS]
                           [--random-state INT] [--min-trades INT]
                           [--hyperopt-loss NAME] [--disable-param-export]
+                          [--ignore-missing-spaces]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -61,7 +61,7 @@ optional arguments:
                         Specify what timerange of data to use.
   --data-format-ohlcv {json,jsongz,hdf5}
                         Storage format for downloaded candle (OHLCV) data.
-                        (default: `None`).
+                        (default: `json`).
   --max-open-trades INT
                         Override the value of the `max_open_trades`
                         configuration setting.
@@ -73,10 +73,8 @@ optional arguments:
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
                         Limit command to these pairs. Pairs are space-
                         separated.
-  --hyperopt NAME       Specify hyperopt class name which will be used by the
-                        bot.
-  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
-                        Hyperopt Loss functions.
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt Loss
+                        functions.
   --eps, --enable-position-stacking
                         Allow buying the same pair multiple times (position
                         stacking).
@@ -117,9 +115,13 @@ optional arguments:
                         Hyperopt-loss-functions are:
                         ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
                         SharpeHyperOptLoss, SharpeHyperOptLossDaily,
-                        SortinoHyperOptLoss, SortinoHyperOptLossDaily
+                        SortinoHyperOptLoss, SortinoHyperOptLossDaily,
+                        CalmarHyperOptLoss, MaxDrawDownHyperOptLoss
   --disable-param-export
                         Disable automatic hyperopt parameter export.
+  --ignore-missing-spaces, --ignore-unparameterized-spaces
+                        Suppress errors for any requested Hyperopt spaces that
+                        do not contain any parameters.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -456,7 +458,7 @@ class MyAwesomeStrategy(IStrategy):
                 "only_per_pair": False
             })
 
-        return protection
+        return prot
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # ...
@@ -515,12 +517,14 @@ This class should be in its own file within the `user_data/hyperopts/` directory
 
 Currently, the following loss functions are builtin:
 
-* `ShortTradeDurHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function) - Mostly for short trade duration and avoiding losses.
-* `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
-* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
-* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation)
-* `SortinoHyperOptLoss` (optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation)
-* `SortinoHyperOptLossDaily` (optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation)
+* `ShortTradeDurHyperOptLoss` - (default legacy Freqtrade hyperoptimization loss function) - Mostly for short trade duration and avoiding losses.
+* `OnlyProfitHyperOptLoss` - takes only amount of profit into consideration.
+* `SharpeHyperOptLoss` - optimizes Sharpe Ratio calculated on trade returns relative to standard deviation.
+* `SharpeHyperOptLossDaily` - optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation.
+* `SortinoHyperOptLoss` - optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation.
+* `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation.
+* `MaxDrawDownHyperOptLoss` - Optimizes Maximum drawdown.
+* `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown.
 
 Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 
@@ -558,7 +562,7 @@ For example, to use one month of data, pass `--timerange 20210101-20210201` (fro
 Full command:
 
 ```bash
-freqtrade hyperopt --hyperopt <hyperoptname> --strategy <strategyname> --timerange 20210101-20210201
+freqtrade hyperopt --strategy <strategyname> --timerange 20210101-20210201
 ```
 
 ### Running Hyperopt with Smaller Search Space
@@ -680,11 +684,11 @@ If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace f
 
 These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
 
-If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
+If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
 
 Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps).
 
-A sample for these methods can be found in [sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
+A sample for these methods can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
 
 !!! Note "Reduced search space"
     To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
@@ -726,7 +730,7 @@ If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimiza
 
 If you have the `stoploss_space()` method in your custom hyperopt file, remove it in order to utilize Stoploss hyperoptimization space generated by Freqtrade by default.
 
-Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
+Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
 
 !!! Note "Reduced search space"
     To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
@@ -764,10 +768,10 @@ As stated in the comment, you can also use it as the values of the corresponding
 
 If you are optimizing trailing stop values, Freqtrade creates the 'trailing' optimization hyperspace for you. By default, the `trailing_stop` parameter is always set to True in that hyperspace, the value of the `trailing_only_offset_is_reached` vary between True and False, the values of the `trailing_stop_positive` and `trailing_stop_positive_offset` parameters vary in the ranges 0.02...0.35 and 0.01...0.1 correspondingly, which is sufficient in most cases.
 
-Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
+Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
 
 !!! Note "Reduced search space"
-    To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
+    To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#overriding-pre-defined-spaces) to change this to your needs.
 
 ### Reproducible results
 
@@ -827,8 +831,8 @@ After you run Hyperopt for the desired amount of epochs, you can later list all
 
 Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
 
-To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
+To achieve same the results (number of trades, their durations, profit, etc.) as during Hyperopt, please use the same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
 
-Should results don't match, please double-check to make sure you transferred all conditions correctly.
+Should results not match, please double-check to make sure you transferred all conditions correctly.
 Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
 You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).

docs/includes/pairlists.md

@@ -52,6 +52,8 @@ To skip pair validation against active markets, set `"allow_inactive": true` wit
 This can be useful for backtesting expired pairs (like quarterly spot-markets).
 This option must be configured along with `exchange.skip_pair_validation` in the exchange configuration.
 
+When used in a "follow-up" position (e.g. after VolumePairlist), all pairs in `'pair_whitelist'` will be added to the end of the pairlist.
+
 #### Volume Pair List
 
 `VolumePairList` employs sorting/filtering of pairs by their trading volume. It selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
@@ -82,6 +84,8 @@ Filtering instances (not the first position in the list) will not apply any cach
 
 You can define a minimum volume with `min_value` - which will filter out pairs with a volume lower than the specified value in the specified timerange.
 
+### VolumePairList Advanced mode
+
 `VolumePairList` can also operate in an advanced mode to build volume over a given timerange of specified candle size. It utilizes exchange historical candle data, builds a typical price (calculated by (open+high+low)/3) and multiplies the typical price with every candle's volume. The sum is the `quoteVolume` over the given range. This allows different scenarios, for a  more smoothened volume, when using longer ranges with larger candle sizes, or the opposite when using a short range with small candles.
 
 For convenience `lookback_days` can be specified, which will imply that 1d candles will be used for the lookback. In the example below the pairlist would be created based on the last 7 days:
@@ -105,6 +109,24 @@ For convenience `lookback_days` can be specified, which will imply that 1d candl
 !!! Warning "Performance implications when using lookback range"
     If used in first position in combination with lookback, the computation of the range based volume can be time and resource consuming, as it downloads candles for all tradable pairs. Hence it's highly advised to use the standard approach with `VolumeFilter` to narrow the pairlist down for further range volume calculation.
 
+??? Tip "Unsupported exchanges (Bittrex, Gemini)"
+    On some exchanges (like Bittrex and Gemini), regular VolumePairList does not work as the api does not natively provide 24h volume. This can be worked around by using candle data to build the volume.
+    To roughly simulate 24h volume, you can use the following configuration.
+    Please note that These pairlists will only refresh once per day.
+
+    ```json
+    "pairlists": [
+        {
+            "method": "VolumePairList",
+            "number_assets": 20,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 86400,
+            "lookback_days": 1
+        }
+    ],
+    ```
+
 More sophisticated approach can be used, by using `lookback_timeframe` for candle size and `lookback_period` which specifies the amount of candles. This example will build the volume pairs based on a rolling period of 3 days of 1h candles:
 
 ```json
@@ -145,6 +167,7 @@ Example to remove the first 10 pairs from the pairlist:
 
 ```json
 "pairlists": [
+    // ...
     {
         "method": "OffsetFilter",
         "offset": 10
@@ -170,7 +193,27 @@ Sorts pairs by past trade performance, as follows:
 
 Trade count is used as a tie breaker.
 
-!!! Note
+You can use the `minutes` parameter to only consider performance of the past X minutes (rolling window).
+Not defining this parameter (or setting it to 0) will use all-time performance.
+
+The optional `min_profit` parameter defines the minimum profit a pair must have to be considered.
+Pairs below this level will be filtered out.
+Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without a way to recover.
+
+```json
+"pairlists": [
+    // ...
+    {
+        "method": "PerformanceFilter",
+        "minutes": 1440,  // rolling 24h
+        "min_profit": 0.01
+    }
+],
+```
+
+As this Filter uses past performance of the bot, it'll have some startup-period - and should only be used after the bot has a few 100 trades in the database.
+
+!!! Warning "Backtesting"
     `PerformanceFilter` does not support backtesting mode.
 
 #### PrecisionFilter
@@ -249,7 +292,7 @@ If the trading range over the last 10 days is <1% or >99%, remove the pair from
 
 #### VolatilityFilter
 
-Volatility is the degree of historical variation of a pairs over time, is is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
+Volatility is the degree of historical variation of a pairs over time, it is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
 
 This filter removes pairs if the average volatility over a `lookback_days` days is below `min_volatility` or above `max_volatility`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
 
@@ -303,5 +346,5 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
         "refresh_period": 86400
     },
     {"method": "ShuffleFilter", "seed": 42}
-    ],
+],
 ```

docs/index.md

@@ -36,10 +36,12 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
 
 Please read the [exchange specific notes](exchanges.md) to learn about eventual, special configurations needed for each exchange.
 
-- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#binance-blacklist))
+- [X] [Binance](https://www.binance.com/) ([*Note for binance users](exchanges.md#binance-blacklist))
 - [X] [Bittrex](https://bittrex.com/)
 - [X] [FTX](https://ftx.com)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
 - [X] [Kraken](https://kraken.com/)
+- [X] [OKEX](https://www.okex.com/)
 - [ ] [potentially many others through <img alt="ccxt" width="30px" src="assets/ccxt-logo.svg" />](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
 ### Community tested
@@ -79,4 +81,4 @@ For any questions not covered by the documentation or for further information ab
 
 ## Ready to try?
 
-Begin by reading our installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).
+Begin by reading the installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).

docs/installation.md

@@ -60,7 +60,7 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
     sudo apt-get update
 
     # install packages
-    sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git
+    sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git curl
     ```
 
 === "RaspberryPi/Raspbian"
@@ -71,7 +71,7 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
 
 
     ```bash
-    sudo apt-get install python3-venv libatlas-base-dev cmake
+    sudo apt-get install python3-venv libatlas-base-dev cmake curl
     # Use pywheels.org to speed up installation
     sudo echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > tee /etc/pip.conf
 
@@ -113,6 +113,13 @@ git checkout develop
 
 You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
 
+??? Note "Install from pypi"
+    An alternative way to install Freqtrade is from [pypi](https://pypi.org/project/freqtrade/). The downside is that this method requires ta-lib to be correctly installed beforehand, and is therefore currently not the recommended way to install Freqtrade.
+
+    ``` bash
+    pip install freqtrade
+    ```
+
 ------
 
 ## Script Installation

docs/requirements-docs.txt

@@ -1,4 +1,4 @@
-mkdocs==1.2.2
-mkdocs-material==7.2.4
+mkdocs==1.2.3
+mkdocs-material==7.3.6
 mdx_truly_sane_lists==1.2
-pymdown-extensions==8.2
+pymdown-extensions==9.1

docs/rest-api.md

@@ -38,6 +38,11 @@ Sample configuration:
 !!! Danger "Security warning"
     By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
 
+??? Note "API/UI Access on a remote servers"
+    If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
+    This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
+    Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
+
 You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
 This should return the response:
 
@@ -78,7 +83,7 @@ If you run your bot using docker, you'll need to have the bot listen to incoming
     },
 ```
 
-Uncomment the following from your docker-compose file:
+Make sure that the following 2 lines are available in your docker-compose file:
 
 ```yml
     ports:
@@ -330,12 +335,15 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
 
 ### CORS
 
-All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
-Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
-Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
+This whole section is only necessary in cross-origin cases (where you multiple bot API's running on `localhost:8081`, `localhost:8082`, ...), and want to combine them into one FreqUI instance.
 
-Users can configure this themselves via the `CORS_origins` configuration setting.
-It consists of a list of allowed sites that are allowed to consume resources from the bot's API.
+??? info "Technical explanation"
+    All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
+    Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
+    Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
+
+Users can allow access from different origin URL's to the bot API via the `CORS_origins` configuration setting.
+It consists of a list of allowed URL's that are allowed to consume resources from the bot's API.
 
 Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - this would mean that the following configuration becomes necessary:
 
@@ -348,5 +356,19 @@ Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - t
 }
 ```
 
+In the following (pretty common) case, FreqUI is accessible on `http://localhost:8080/trade` (this is what you see in your navbar when navigating to freqUI).
+![freqUI url](assets/frequi_url.png)
+
+The correct configuration for this case is `http://localhost:8080` - the main part of the URL including the port.
+
+```jsonc
+{
+    //...
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": ["http://localhost:8080"],
+    //...
+}
+```
+
 !!! Note
     We strongly recommend to also set `jwt_secret_key` to something random and known only to yourself to avoid unauthorized access to your bot.

docs/stoploss.md

@@ -182,7 +182,7 @@ For example, simplified math:
 * the bot buys an asset at a price of 100$
 * the stop loss is defined at -10%
 * the stop loss would get triggered once the asset drops below 90$
-* stoploss will remain at 90$ unless asset increases to or above our configured offset
+* stoploss will remain at 90$ unless asset increases to or above the configured offset
 * assuming the asset now increases to 103$ (where we have the offset configured)
 * the stop loss will now be -2% of 103$ = 100.94$
 * now the asset drops in value to 101\$, the stop loss will still be 100.94$ and would trigger at 100.94$

docs/strategy-advanced.md

@@ -77,43 +77,6 @@ class AwesomeStrategy(IStrategy):
 
 ***
 
-## Custom sell signal
-
-It is possible to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need the trade profit to take the sell decision. 
-
-For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
-
-Using custom_sell() signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
-
-!!! Note
-    Returning a `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
-
-An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
-
-``` python
-class AwesomeStrategy(IStrategy):
-    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
-                    current_profit: float, **kwargs):
-        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-        last_candle = dataframe.iloc[-1].squeeze()
-
-        # Above 20% profit, sell when rsi < 80
-        if current_profit > 0.2:
-            if last_candle['rsi'] < 80:
-                return 'rsi_below_80'
-
-        # Between 2% and 10%, sell if EMA-long above EMA-short
-        if 0.02 < current_profit < 0.1:
-            if last_candle['emalong'] > last_candle['emashort']:
-                return 'ema_long_below_80'
-
-        # Sell any positions at a loss if they are held for more than one day.
-        if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
-            return 'unclog'
-```
-
-See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
-
 ## Buy Tag
 
 When your strategy has multiple buy signals, you can name the signal that triggered.
@@ -143,500 +106,26 @@ def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_r
 !!! Note
     `buy_tag` is limited to 100 characters, remaining data will be truncated.
 
+## Exit tag
 
-## Custom stoploss
-
-The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss.
-
-The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
-The method must return a stoploss value (float / number) as a percentage of the current price.
-E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
-
-The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
-
-To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
+Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
 
 ``` python
-# additional imports required
-from datetime import datetime
-from freqtrade.persistence import Trade
+def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (dataframe['rsi'] > 70) &
+            (dataframe['volume'] > 0)
+        ),
+        ['sell', 'exit_tag']] = (1, 'exit_rsi')
 
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-        """
-        Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
-        e.g. returning -0.05 would create a stoploss 5% below current_rate.
-        The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns the initial stoploss value
-        Only called when use_custom_stoploss is set to True.
-
-        :param pair: Pair that's currently analyzed
-        :param trade: trade object.
-        :param current_time: datetime object, containing the current datetime
-        :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
-        :param current_profit: Current profit (as ratio), calculated based on current_rate.
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        :return float: New stoploss value, relative to the current rate
-        """
-        return -0.04
+    return dataframe
 ```
 
-Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
-
-!!! Note "Use of dates"
-    All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
-
-!!! Tip "Trailing stoploss"
-    It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
-
-### Custom stoploss examples
-
-The next section will show some examples on what's possible with the custom stoploss function.
-Of course, many more things are possible, and all examples can be combined at will.
-
-#### Time based trailing stop
-
-Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
-
-``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
-        if current_time - timedelta(minutes=120) > trade.open_date_utc:
-            return -0.05
-        elif current_time - timedelta(minutes=60) > trade.open_date_utc:
-            return -0.10
-        return 1
-```
-
-#### Different stoploss per pair
-
-Use a different stoploss depending on the pair.
-In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
-
-``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        if pair in ('ETH/BTC', 'XRP/BTC'):
-            return -0.10
-        elif pair in ('LTC/BTC'):
-            return -0.05
-        return -0.15
-```
-
-#### Trailing stoploss with positive offset
-
-Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
-
-Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
-
-``` python
-from datetime import datetime, timedelta
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        if current_profit < 0.04:
-            return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
-
-        # After reaching the desired offset, allow the stoploss to trail by half the profit
-        desired_stoploss = current_profit / 2
-
-        # Use a minimum of 2.5% and a maximum of 5%
-        return max(min(desired_stoploss, 0.05), 0.025)
-```
-
-#### Calculating stoploss relative to open price
-
-Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
-
-The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
-
-#### Stepped stoploss
-
-Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
-
-* Use the regular stoploss until 20% profit is reached
-* Once profit is > 20% - set stoploss to 7% above open price.
-* Once profit is > 25% - set stoploss to 15% above open price.
-* Once profit is > 40% - set stoploss to 25% above open price.
-
-``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
-from freqtrade.strategy import stoploss_from_open
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        # evaluate highest to lowest, so that highest possible stop is used
-        if current_profit > 0.40:
-            return stoploss_from_open(0.25, current_profit)
-        elif current_profit > 0.25:
-            return stoploss_from_open(0.15, current_profit)
-        elif current_profit > 0.20:
-            return stoploss_from_open(0.07, current_profit)
-
-        # return maximum stoploss value, keeping current stoploss price unchanged
-        return 1
-```
-
-#### Custom stoploss using an indicator from dataframe example
-
-Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
-
-``` python
-class AwesomeStrategy(IStrategy):
-
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        # <...>
-        dataframe['sar'] = ta.SAR(dataframe)
-
-    use_custom_stoploss = True
-
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-
-        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-        last_candle = dataframe.iloc[-1].squeeze()
-
-        # Use parabolic sar as absolute stoploss price
-        stoploss_price = last_candle['sar']
-
-        # Convert absolute price to percentage relative to current_rate
-        if stoploss_price < current_rate:
-            return (stoploss_price / current_rate) - 1
-
-        # return maximum stoploss value, keeping current stoploss price unchanged
-        return 1
-```
-
-See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
-
----
-
-## Custom order price rules
-
-By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
-
-You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
+The provided exit-tag is then used as sell-reason - and shown as such in backtest results.
 
 !!! Note
-    If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
-
-### Custom order entry and exit price example
-
-``` python
-from datetime import datetime, timedelta, timezone
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def custom_entry_price(self, pair: str, current_time: datetime,
-                           proposed_rate, **kwargs) -> float:
-
-        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
-                                                                timeframe=self.timeframe)
-        new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
-        
-        return new_entryprice
-
-    def custom_exit_price(self, pair: str, trade: Trade,
-                          current_time: datetime, proposed_rate: float,
-                          current_profit: float, **kwargs) -> float:
-
-        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
-                                                                timeframe=self.timeframe)
-        new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
-        
-        return new_exitprice
-
-```
-
-!!! Warning
-    Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.
-
-!!! Example
-    If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98.
-
-!!! Warning "No backtesting support"
-    Custom entry-prices are currently not supported during backtesting.
-
-## Custom order timeout rules
-
-Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
-
-However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if an order did time out or not.
-
-!!! Note
-    Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
-
-### Custom order timeout example
-
-A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
-It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
-
-The function must return either `True` (cancel order) or `False` (keep order alive).
-
-``` python
-from datetime import datetime, timedelta, timezone
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
-    unfilledtimeout = {
-        'buy': 60 * 25,
-        'sell': 60 * 25
-    }
-
-    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
-            return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
-            return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
-           return True
-        return False
-
-
-    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
-            return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
-            return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
-           return True
-        return False
-```
-
-!!! Note
-    For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
-
-### Custom order timeout example (using additional data)
-
-``` python
-from datetime import datetime
-from freqtrade.persistence import Trade
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
-    unfilledtimeout = {
-        'buy': 60 * 25,
-        'sell': 60 * 25
-    }
-
-    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
-        ob = self.dp.orderbook(pair, 1)
-        current_price = ob['bids'][0][0]
-        # Cancel buy order if price is more than 2% above the order.
-        if current_price > order['price'] * 1.02:
-            return True
-        return False
-
-
-    def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
-        ob = self.dp.orderbook(pair, 1)
-        current_price = ob['asks'][0][0]
-        # Cancel sell order if price is more than 2% below the order.
-        if current_price < order['price'] * 0.98:
-            return True
-        return False
-```
-
----
-
-## Bot loop start callback
-
-A simple callback which is called once at the start of every bot throttling iteration.
-This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
-
-``` python
-import requests
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def bot_loop_start(self, **kwargs) -> None:
-        """
-        Called at the start of the bot iteration (one loop).
-        Might be used to perform pair-independent tasks
-        (e.g. gather some remote resource for comparison)
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        """
-        if self.config['runmode'].value in ('live', 'dry_run'):
-            # Assign this to the class by using self.*
-            # can then be used by populate_* methods
-            self.remote_data = requests.get('https://some_remote_source.example.com')
-
-```
-
-## Bot order confirmation
-
-### Trade entry (buy order) confirmation
-
-`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
-
-``` python
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
-                            time_in_force: str, current_time: datetime, **kwargs) -> bool:
-        """
-        Called right before placing a buy order.
-        Timing for this function is critical, so avoid doing heavy computations or
-        network requests in this method.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns True (always confirming).
-
-        :param pair: Pair that's about to be bought.
-        :param order_type: Order type (as configured in order_types). usually limit or market.
-        :param amount: Amount in target (quote) currency that's going to be traded.
-        :param rate: Rate that's going to be used when using limit orders
-        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
-        :param current_time: datetime object, containing the current datetime
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        :return bool: When True is returned, then the buy-order is placed on the exchange.
-            False aborts the process
-        """
-        return True
-
-```
-
-### Trade exit (sell order) confirmation
-
-`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
-
-``` python
-from freqtrade.persistence import Trade
-
-
-class AwesomeStrategy(IStrategy):
-
-    # ... populate_* methods
-
-    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
-                           rate: float, time_in_force: str, sell_reason: str,
-                           current_time: datetime, **kwargs) -> bool:
-        """
-        Called right before placing a regular sell order.
-        Timing for this function is critical, so avoid doing heavy computations or
-        network requests in this method.
-
-        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
-
-        When not implemented by a strategy, returns True (always confirming).
-
-        :param pair: Pair that's about to be sold.
-        :param order_type: Order type (as configured in order_types). usually limit or market.
-        :param amount: Amount in quote currency.
-        :param rate: Rate that's going to be used when using limit orders
-        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
-        :param sell_reason: Sell reason.
-            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
-                           'sell_signal', 'force_sell', 'emergency_sell']
-        :param current_time: datetime object, containing the current datetime
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        :return bool: When True is returned, then the sell-order is placed on the exchange.
-            False aborts the process
-        """
-        if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
-            # Reject force-sells with negative profit
-            # This is just a sample, please adjust to your needs
-            # (this does not necessarily make sense, assuming you know when you're force-selling)
-            return False
-        return True
-
-```
-
-### Stake size management
-
-It is possible to manage your risk by reducing or increasing stake amount when placing a new trade.
-
-```python
-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,
-                            **kwargs) -> float:
-
-        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
-        current_candle = dataframe.iloc[-1].squeeze()
-
-        if current_candle['fastk_rsi_1h'] > current_candle['fastd_rsi_1h']:
-            if self.config['stake_amount'] == 'unlimited':
-                # Use entire available wallet during favorable conditions when in compounding mode.
-                return max_stake
-            else:
-                # Compound profits during favorable conditions instead of using a static stake.
-                return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
-
-        # Use default stake amount.
-        return proposed_stake
-```
-
-Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
-
-!!! Tip
-    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this acton will be logged.
-
-!!! Tip
-    Returning `0` or `None` will prevent trades from being placed.
-
----
+    `sell_reason` is limited to 100 characters, remaining data will be truncated.
 
 ## Derived strategies
 
@@ -695,3 +184,33 @@ The variable 'content', will contain the strategy file in a BASE64 encoded form.
 ```
 
 Please ensure that 'NameOfStrategy' is identical to the strategy name!
+
+## Performance warning
+
+When executing a strategy, one can sometimes be greeted by the following in the logs
+
+> PerformanceWarning: DataFrame is highly fragmented.
+
+This is a warning from [`pandas`](https://github.com/pandas-dev/pandas) and as the warning continues to say:
+use `pd.concat(axis=1)`.
+This can have slight performance implications, which are usually only visible during hyperopt (when optimizing an indicator).
+
+For example:
+
+```python
+for val in self.buy_ema_short.range:
+    dataframe[f'ema_short_{val}'] = ta.EMA(dataframe, timeperiod=val)
+```
+
+should be rewritten to
+
+```python
+frames = [dataframe]
+for val in self.buy_ema_short.range:
+    frames.append({
+        f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
+    })
+
+# Append columns to existing dataframe
+merged_frame = pd.concat(frames, axis=1)
+```

docs/strategy-callbacks.md

@@ -0,0 +1,568 @@
+# 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".
+
+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.
+
+Currently available callbacks:
+
+* [`bot_loop_start()`](#bot-loop-start)
+* [`custom_stake_amount()`](#custom-stake-size)
+* [`custom_sell()`](#custom-sell-signal)
+* [`custom_stoploss()`](#custom-stoploss)
+* [`custom_entry_price()` and `custom_exit_price()`](#custom-order-price-rules)
+* [`check_buy_timeout()` and `check_sell_timeout()](#custom-order-timeout-rules)
+* [`confirm_trade_entry()`](#trade-entry-buy-order-confirmation)
+* [`confirm_trade_exit()`](#trade-exit-sell-order-confirmation)
+
+!!! Tip "Callback calling sequence"
+    You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
+
+## Bot loop start
+
+A simple callback which is called once at the start of every bot throttling iteration (roughly every 5 seconds, unless configured differently).
+This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
+
+``` python
+import requests
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def bot_loop_start(self, **kwargs) -> None:
+        """
+        Called at the start of the bot iteration (one loop).
+        Might be used to perform pair-independent tasks
+        (e.g. gather some remote resource for comparison)
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        if self.config['runmode'].value in ('live', 'dry_run'):
+            # Assign this to the class by using self.*
+            # can then be used by populate_* methods
+            self.remote_data = requests.get('https://some_remote_source.example.com')
+
+```
+
+## Custom Stake size
+
+Called before entering a trade, makes it possible to manage your position size when placing a new trade.
+
+```python
+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,
+                            **kwargs) -> float:
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+        current_candle = dataframe.iloc[-1].squeeze()
+
+        if current_candle['fastk_rsi_1h'] > current_candle['fastd_rsi_1h']:
+            if self.config['stake_amount'] == 'unlimited':
+                # Use entire available wallet during favorable conditions when in compounding mode.
+                return max_stake
+            else:
+                # Compound profits during favorable conditions instead of using a static stake.
+                return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
+
+        # Use default stake amount.
+        return proposed_stake
+```
+
+Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
+
+!!! Tip
+    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this acton will be logged.
+
+!!! Tip
+    Returning `0` or `None` will prevent trades from being placed.
+
+## Custom sell signal
+
+Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed.
+
+Allows to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need trade data to make an exit decision.
+
+For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
+
+Using custom_sell() signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
+
+!!! Note
+    Returning a (none-empty) `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
+
+An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
+
+``` python
+class AwesomeStrategy(IStrategy):
+    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+                    current_profit: float, **kwargs):
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        # Above 20% profit, sell when rsi < 80
+        if current_profit > 0.2:
+            if last_candle['rsi'] < 80:
+                return 'rsi_below_80'
+
+        # Between 2% and 10%, sell if EMA-long above EMA-short
+        if 0.02 < current_profit < 0.1:
+            if last_candle['emalong'] > last_candle['emashort']:
+                return 'ema_long_below_80'
+
+        # Sell any positions at a loss if they are held for more than one day.
+        if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
+            return 'unclog'
+```
+
+See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
+
+## Custom stoploss
+
+Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed. 
+The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
+
+The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss (before this method is called for the first time for a trade).
+
+The method must return a stoploss value (float / number) as a percentage of the current price.
+E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
+
+The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
+
+To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
+
+``` python
+# additional imports required
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+        """
+        Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
+        e.g. returning -0.05 would create a stoploss 5% below current_rate.
+        The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns the initial stoploss value
+        Only called when use_custom_stoploss is set to True.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: New stoploss value, relative to the current rate
+        """
+        return -0.04
+```
+
+Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
+
+!!! Note "Use of dates"
+    All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
+
+!!! Tip "Trailing stoploss"
+    It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
+
+### Custom stoploss examples
+
+The next section will show some examples on what's possible with the custom stoploss function.
+Of course, many more things are possible, and all examples can be combined at will.
+
+#### Time based trailing stop
+
+Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
+
+``` python
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
+        if current_time - timedelta(minutes=120) > trade.open_date_utc:
+            return -0.05
+        elif current_time - timedelta(minutes=60) > trade.open_date_utc:
+            return -0.10
+        return 1
+```
+
+#### Different stoploss per pair
+
+Use a different stoploss depending on the pair.
+In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        if pair in ('ETH/BTC', 'XRP/BTC'):
+            return -0.10
+        elif pair in ('LTC/BTC'):
+            return -0.05
+        return -0.15
+```
+
+#### Trailing stoploss with positive offset
+
+Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
+
+Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
+
+``` python
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        if current_profit < 0.04:
+            return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
+
+        # After reaching the desired offset, allow the stoploss to trail by half the profit
+        desired_stoploss = current_profit / 2
+
+        # Use a minimum of 2.5% and a maximum of 5%
+        return max(min(desired_stoploss, 0.05), 0.025)
+```
+
+#### Stepped stoploss
+
+Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
+
+* Use the regular stoploss until 20% profit is reached
+* Once profit is > 20% - set stoploss to 7% above open price.
+* Once profit is > 25% - set stoploss to 15% above open price.
+* Once profit is > 40% - set stoploss to 25% above open price.
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade
+from freqtrade.strategy import stoploss_from_open
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        # evaluate highest to lowest, so that highest possible stop is used
+        if current_profit > 0.40:
+            return stoploss_from_open(0.25, current_profit)
+        elif current_profit > 0.25:
+            return stoploss_from_open(0.15, current_profit)
+        elif current_profit > 0.20:
+            return stoploss_from_open(0.07, current_profit)
+
+        # return maximum stoploss value, keeping current stoploss price unchanged
+        return 1
+```
+
+#### Custom stoploss using an indicator from dataframe example
+
+Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
+
+``` python
+class AwesomeStrategy(IStrategy):
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # <...>
+        dataframe['sar'] = ta.SAR(dataframe)
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        # Use parabolic sar as absolute stoploss price
+        stoploss_price = last_candle['sar']
+
+        # Convert absolute price to percentage relative to current_rate
+        if stoploss_price < current_rate:
+            return (stoploss_price / current_rate) - 1
+
+        # return maximum stoploss value, keeping current stoploss price unchanged
+        return 1
+```
+
+See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
+
+### Common helpers for stoploss calculations
+
+#### Stoploss relative to open price
+
+Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
+
+The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
+
+#### Stoploss percentage from absolute price
+
+Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
+
+The helper function [`stoploss_from_absolute()`](strategy-customization.md#stoploss_from_absolute) can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`.
+
+---
+
+## Custom order price rules
+
+By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
+
+You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
+
+Each of these methods are called right before placing an order on the exchange.
+
+!!! Note
+    If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
+
+### Custom order entry and exit price example
+
+``` python
+from datetime import datetime, timedelta, timezone
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def custom_entry_price(self, pair: str, current_time: datetime,
+                           proposed_rate, **kwargs) -> float:
+
+        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
+                                                                timeframe=self.timeframe)
+        new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
+        
+        return new_entryprice
+
+    def custom_exit_price(self, pair: str, trade: Trade,
+                          current_time: datetime, proposed_rate: float,
+                          current_profit: float, **kwargs) -> float:
+
+        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
+                                                                timeframe=self.timeframe)
+        new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
+        
+        return new_exitprice
+
+```
+
+!!! Warning
+    Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.  
+    **Example**:  
+    If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98, which is 2% below the current (proposed) rate.
+
+!!! Warning "No backtesting support"
+    Custom entry-prices are currently not supported during backtesting.
+
+## Custom order timeout rules
+
+Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
+
+However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if an order did time out or not.
+
+!!! Note
+    Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
+
+### Custom order timeout example
+
+Called for every open order until that order is either filled or cancelled.
+`check_buy_timeout()` is called for trade entries, while `check_sell_timeout()` is called for trade exit orders.
+
+A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
+It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
+
+The function must return either `True` (cancel order) or `False` (keep order alive).
+
+``` python
+from datetime import datetime, timedelta, timezone
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'buy': 60 * 25,
+        'sell': 60 * 25
+    }
+
+    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
+           return True
+        return False
+
+
+    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
+           return True
+        return False
+```
+
+!!! Note
+    For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
+
+### Custom order timeout example (using additional data)
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'buy': 60 * 25,
+        'sell': 60 * 25
+    }
+
+    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob['bids'][0][0]
+        # Cancel buy order if price is more than 2% above the order.
+        if current_price > order['price'] * 1.02:
+            return True
+        return False
+
+
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob['asks'][0][0]
+        # Cancel sell order if price is more than 2% below the order.
+        if current_price < order['price'] * 0.98:
+            return True
+        return False
+```
+
+---
+
+## Bot order confirmation
+
+Confirm trade entry / exits.
+This are the last methods that will be called before an order is placed.
+
+### Trade entry (buy order) confirmation
+
+`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
+
+``` python
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, **kwargs) -> bool:
+        """
+        Called right before placing a buy order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair that's about to be bought.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in target (quote) currency that's going to be traded.
+        :param rate: Rate that's going to be used when using limit orders
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the buy-order is placed on the exchange.
+            False aborts the process
+        """
+        return True
+
+```
+
+### Trade exit (sell order) confirmation
+
+`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
+
+``` python
+from freqtrade.persistence import Trade
+
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
+                           rate: float, time_in_force: str, sell_reason: str,
+                           current_time: datetime, **kwargs) -> bool:
+        """
+        Called right before placing a regular sell order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair that's about to be sold.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in quote currency.
+        :param rate: Rate that's going to be used when using limit orders
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param sell_reason: Sell reason.
+            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
+                           'sell_signal', 'force_sell', 'emergency_sell']
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the sell-order is placed on the exchange.
+            False aborts the process
+        """
+        if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
+            # Reject force-sells with negative profit
+            # This is just a sample, please adjust to your needs
+            # (this does not necessarily make sense, assuming you know when you're force-selling)
+            return False
+        return True
+
+```

docs/strategy-customization.md

@@ -4,33 +4,23 @@ This page explains how to customize your strategies, add new indicators and set
 
 Please familiarize yourself with [Freqtrade basics](bot-basics.md) first, which provides overall info on how the bot operates.
 
-## Install a custom strategy file
-
-This is very simple. Copy paste your strategy file into the directory `user_data/strategies`.
-
-Let assume you have a class called `AwesomeStrategy` in the file `AwesomeStrategy.py`:
-
-1. Move your file into `user_data/strategies` (you should have `user_data/strategies/AwesomeStrategy.py`
-2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
-
-```bash
-freqtrade trade --strategy AwesomeStrategy
-```
-
 ## Develop your own strategy
 
 The bot includes a default strategy file.
 Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
 
 You will however most likely have your own idea for a strategy.
-This document intends to help you develop one for yourself.
+This document intends to help you convert your strategy idea into your own strategy.
 
-To get started, use `freqtrade new-strategy --strategy AwesomeStrategy`.
+To get started, use `freqtrade new-strategy --strategy AwesomeStrategy` (you can obviously use your own naming for your strategy).
 This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
 
 !!! Note
     This is just a template file, which will most likely not be profitable out of the box.
 
+??? Hint "Different template levels"
+    `freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with most callbacks defined.
+
 ### Anatomy of a strategy
 
 A strategy file contains all the information needed to build a good strategy:
@@ -67,6 +57,46 @@ file as reference.**
     needs to take care to avoid having the strategy utilize data from the future.
     Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
 
+### Dataframe
+
+Freqtrade uses [pandas](https://pandas.pydata.org/) to store/provide the candlestick (OHLCV) data.
+Pandas is a great library developed for processing large amounts of data.
+
+Each row in a dataframe corresponds to one candle on a chart, with the latest candle always being the last in the dataframe (sorted by date).
+
+``` output
+> dataframe.head()
+                       date      open      high       low     close     volume
+0 2021-11-09 23:25:00+00:00  67279.67  67321.84  67255.01  67300.97   44.62253
+1 2021-11-09 23:30:00+00:00  67300.97  67301.34  67183.03  67187.01   61.38076
+2 2021-11-09 23:35:00+00:00  67187.02  67187.02  67031.93  67123.81  113.42728
+3 2021-11-09 23:40:00+00:00  67123.80  67222.40  67080.33  67160.48   78.96008
+4 2021-11-09 23:45:00+00:00  67160.48  67160.48  66901.26  66943.37  111.39292
+```
+
+Pandas provides fast ways to calculate metrics. To benefit from this speed, it's advised to not use loops, but use vectorized methods instead.
+
+Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
+
+As a dataframe is a table, simple python comparisons like the following will not work
+
+``` python
+    if dataframe['rsi'] > 30:
+        dataframe['buy'] = 1
+```
+
+The above section will fail with `The truth value of a Series is ambiguous. [...]`.
+
+This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe.
+
+``` python
+    dataframe.loc[
+        (dataframe['rsi'] > 30)
+    , 'buy'] = 1
+```
+
+With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
+
 ### Customize Indicators
 
 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.
@@ -122,9 +152,19 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
     Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py).
     Then uncomment indicators you need.
 
+#### Indicator libraries
+
+Out of the box, freqtrade installs the following technical libraries:
+
+* [ta-lib](http://mrjbq7.github.io/ta-lib/)
+* [pandas-ta](https://twopirllc.github.io/pandas-ta/)
+* [technical](https://github.com/freqtrade/technical/)
+
+Additional technical libraries can be installed as necessary, or custom indicators may be written / invented by the strategy author.
+
 ### Strategy startup period
 
-Most indicators have an instable startup period, in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
+Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
 To account for this, the strategy can be assigned the `startup_candle_count` attribute.
 This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
 
@@ -136,8 +176,14 @@ In this example strategy, this should be set to 100 (`startup_candle_count = 100
 
 By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
 
+!!! Warning "Using x calls to get OHLCV"
+    If you receive a warning like `WARNING - Using 3 calls to get OHLCV. This can result in slower operations for the bot. Please check if you really need 1500 candles for your strategy` - you should consider if you really need this much historic data for your signals.
+    Having this will cause Freqtrade to make multiple calls for the same pair, which will obviously be slower than one network request.
+    As a consequence, Freqtrade will take longer to refresh candles - and should therefore be avoided if possible.
+    This is capped to 5 total calls to avoid overloading the exchange, or make freqtrade too slow.
+
 !!! Warning
-    `startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
+    `startup_candle_count` should be below `ohlcv_candle_limit * 5` (which is 500 * 5 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
 
 #### Example
 
@@ -271,20 +317,14 @@ class AwesomeStrategy(IStrategy):
 
 Setting a stoploss is highly recommended to protect your capital from strong moves against you.
 
-Sample:
+Sample of setting a 10% stoploss:
 
 ``` python
 stoploss = -0.10
 ```
 
-This would signify a stoploss of -10%.
-
 For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md).
 
-If your exchange supports it, it's recommended to also set `"stoploss_on_exchange"` in the order_types dictionary, so your stoploss is on the exchange and cannot be missed due to network problems, high load or other reasons.
-
-For more information on order_types please look [here](configuration.md#understand-order_types).
-
 ### Timeframe (formerly ticker interval)
 
 This is the set of candles the bot should download and use for the analysis.
@@ -300,9 +340,22 @@ The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `p
 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.
-Instead, have a look at the section [Storing information](strategy-advanced.md#Storing-information)
+Instead, have a look at the [Storing information](strategy-advanced.md#Storing-information) section.
 
-## Additional data (informative_pairs)
+## Strategy file loading
+
+By default, freqtrade will attempt to load strategies from all `.py` files within `user_data/strategies`.
+
+Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade with `freqtrade trade --strategy AwesomeStrategy`.
+Note that we're using the class-name, not the file name.
+
+You can use `freqtrade list-strategies` to see a list of all strategies Freqtrade is able to load (all strategies in the correct folder).
+It will also include a "status" field, highlighting potential problems.
+
+??? Hint "Customize strategy directory"
+    You can use a different directory by using `--strategy-path user_data/otherPath`. This parameter is available to all commands that require a strategy.
+
+## Informative Pairs
 
 ### Get data for non-tradeable pairs
 
@@ -331,6 +384,133 @@ A full sample can be found [in the DataProvider section](#complete-data-provider
 
 ***
 
+### Informative pairs decorator (`@informative()`)
+
+In most common case it is possible to easily define informative pairs by using a decorator. All decorated `populate_indicators_*` methods run in isolation,
+not having access to data from other informative pairs, in the end all informative dataframes are merged and passed to main `populate_indicators()` method.
+When hyperopting, use of hyperoptable parameter `.value` attribute is not supported. Please use `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter)
+for more information.
+
+??? info "Full documentation"
+    ``` python
+    def informative(timeframe: str, asset: str = '',
+                    fmt: Optional[Union[str, Callable[[KwArg(str)], str]]] = None,
+                    ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
+        """
+        A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
+        define informative indicators.
+
+        Example usage:
+
+            @informative('1h')
+            def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+                dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+                return dataframe
+
+        :param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe.
+        :param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use
+        current pair.
+        :param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not
+        specified, defaults to:
+        * {base}_{quote}_{column}_{timeframe} if asset is specified. 
+        * {column}_{timeframe} if asset is not specified.
+        Format string supports these format variables:
+        * {asset} - full name of the asset, for example 'BTC/USDT'.
+        * {base} - base currency in lower case, for example 'eth'.
+        * {BASE} - same as {base}, except in upper case.
+        * {quote} - quote currency in lower case, for example 'usdt'.
+        * {QUOTE} - same as {quote}, except in upper case.
+        * {column} - name of dataframe column.
+        * {timeframe} - timeframe of informative dataframe.
+        :param ffill: ffill dataframe after merging informative pair.
+        """
+    ```
+
+??? Example "Fast and easy way to define informative pairs"
+
+    Most of the time we do not need power and flexibility offered by `merge_informative_pair()`, therefore we can use a decorator to quickly define informative pairs.
+
+    ``` python
+
+    from datetime import datetime
+    from freqtrade.persistence import Trade
+    from freqtrade.strategy import IStrategy, informative
+
+    class AwesomeStrategy(IStrategy):
+        
+        # This method is not required. 
+        # def informative_pairs(self): ...
+
+        # Define informative upper timeframe for each pair. Decorators can be stacked on same 
+        # method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'.
+        @informative('30m')
+        @informative('1h')
+        def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+            return dataframe
+
+        # Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
+        # 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable 
+        # instead of hardcoding actual stake currency. Available in populate_indicators and other 
+        # methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
+        @informative('1h', 'BTC/{stake}')
+        def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+            return dataframe
+
+        # Define BTC/ETH informative pair. You must specify quote currency if it is different from
+        # stake currency. Available in populate_indicators and other methods as 'eth_btc_rsi_1h'.
+        @informative('1h', 'ETH/BTC')
+        def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+            return dataframe
+    
+        # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
+        # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
+        # formatting. Available in populate_indicators and other methods as 'rsi_upper'.
+        @informative('1h', 'BTC/{stake}', '{column}')
+        def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['rsi_upper'] = ta.RSI(dataframe, timeperiod=14)
+            return dataframe
+    
+        def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            # Strategy timeframe indicators for current pair.
+            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+            # Informative pairs are available in this method.
+            dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
+            return dataframe
+
+    ```
+
+!!! Note
+    Do not use `@informative` decorator if you need to use data of one informative pair when generating another informative pair. Instead, define informative pairs
+    manually as described [in the DataProvider section](#complete-data-provider-sample).
+
+!!! Note
+    Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code.
+
+    ``` python
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        stake = self.config['stake_currency']
+        dataframe.loc[
+            (
+                (dataframe[f'btc_{stake}_rsi_1h'] < 35)
+                &
+                (dataframe['volume'] > 0)
+            ),
+            ['buy', 'buy_tag']] = (1, 'buy_signal_rsi')
+    
+        return dataframe
+    ```
+
+    Alternatively column renaming may be used to remove stake currency from column names: `@informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}')`.
+
+!!! Warning "Duplicate method names"
+    Methods tagged with `@informative()` decorator must always have unique names! Re-using same name (for example when copy-pasting already defined informative method)
+    will overwrite previously defined method and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators
+    created in earlier-defined methods are not available in the dataframe. Carefully review method names and make sure they are unique!
+
+
 ## Additional data (DataProvider)
 
 The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
@@ -374,9 +554,9 @@ The strategy might look something like this:
 
 *Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.*
 
-Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
+Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
 
-Since we can't resample our data we will have to use an informative pair; and since our whitelist will be dynamic we don't know which pair(s) to use.
+Since we can't resample the data we will have to use an informative pair; and since the whitelist will be dynamic we don't know which pair(s) to use.
 
 This is where calling `self.dp.current_whitelist()` comes in handy.
 
@@ -639,6 +819,42 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 
     Full examples can be found in the [Custom stoploss](strategy-advanced.md#custom-stoploss) section of the Documentation.
 
+!!! Note
+    Providing invalid input to `stoploss_from_open()` may produce "CustomStoploss function did not return valid stoploss" warnings.
+    This may happen if `current_profit` parameter is below specified `open_relative_stop`. Such situations may arise when closing trade
+    is blocked by `confirm_trade_exit()` method. Warnings can be solved by never blocking stop loss sells by checking `sell_reason` in
+    `confirm_trade_exit()`, or by using `return stoploss_from_open(...) or 1` idiom, which will request to not change stop loss when
+    `current_profit < open_relative_stop`.
+
+### *stoploss_from_absolute()*
+
+In some situations it may be confusing to deal with stops relative to current rate. Instead, you may define a stoploss level using an absolute price.
+
+??? Example "Returning a stoploss using absolute price from the custom stoploss function"
+
+    If we want to trail a stop price at 2xATR below current proce we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)`.
+
+    ``` python
+
+    from datetime import datetime
+    from freqtrade.persistence import Trade
+    from freqtrade.strategy import IStrategy, stoploss_from_open
+
+    class AwesomeStrategy(IStrategy):
+
+        use_custom_stoploss = True
+
+        def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
+            return dataframe
+
+        def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                            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), current_rate)
+
+    ```
 
 ## Additional data (Wallets)
 
@@ -723,7 +939,8 @@ Sometimes it may be desired to lock a pair after certain events happen (e.g. mul
 Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until, [reason])`.
 `until` must be a datetime object in the future, after which trading will be re-enabled for that pair, while `reason` is an optional string detailing why the pair was locked.
 
-Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
+Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)` - providing reason the pair was locked with.
+`self.unlock_reason(<reason>)` will unlock all pairs currently locked with the provided reason.
 
 To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
 
@@ -781,6 +998,8 @@ Printing more than a few rows is also possible (simply use  `print(dataframe)` i
 
 ## Common mistakes when developing strategies
 
+### Peeking into the future while backtesting
+
 Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
 This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.
 
@@ -791,9 +1010,13 @@ The following lists some common patterns which should be avoided to prevent frus
 - don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
 - don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
 
+### Colliding signals
+
+When buy and sell signals collide (both `'buy'` and `'sell'` are 1), freqtrade will do nothing and ignore the entry (buy) signal. This will avoid trades that buy, and sell immediately. Obviously, this can potentially lead to missed entries.
+
 ## Further strategy ideas
 
-To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
+To get additional Ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
 Feel free to use any of them as inspiration for your own strategies.
 We're happy to accept Pull Requests containing new Strategies to that repo.
 

docs/telegram-usage.md

@@ -58,6 +58,8 @@ For the Freqtrade configuration, you can then use the the full value (including
 ```json
    "chat_id": "-1001332619709"
 ```
+!!! Warning "Using telegram groups"
+    When using telegram groups, you're giving every member of the telegram group access to your freqtrade bot and to all commands possible via telegram. Please make sure that you can trust everyone in the telegram group to avoid unpleasent surprises.
 
 ## Control telegram noise
 
@@ -93,7 +95,9 @@ Example configuration showing the different settings:
         "buy_cancel": "silent",
         "sell_cancel": "on",
         "buy_fill": "off",
-        "sell_fill": "off"
+        "sell_fill": "off",
+        "protection_trigger": "off",
+        "protection_trigger_global": "on"
     },
     "reload": true,
     "balance_dust_level": 0.01
@@ -103,6 +107,7 @@ Example configuration showing the different settings:
 `buy` notifications are sent when the order is placed, while `buy_fill` notifications are sent when the order is filled on the exchange.
 `sell` notifications are sent when the order is placed, while `sell_fill` notifications are sent when the order is filled on the exchange.
 `*_fill` notifications are off by default and must be explicitly enabled.
+`protection_trigger` notifications are sent when a protection triggers and `protection_trigger_global` notifications trigger when global protections are triggered.
 
 
 `balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
@@ -168,10 +173,12 @@ official commands. You can ask at any moment for help with `/help`.
 | `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
 | `/forcesell <trade_id>` | Instantly sells the given trade  (Ignoring `minimum_roi`).
 | `/forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
-| `/forcebuy <pair> [rate]` | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
+| `/forcebuy <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`forcebuy_enable` must be set to True)
 | `/performance` | Show performance of each finished trade grouped by pair
 | `/balance` | Show account balance per currency
 | `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
+| `/weekly <n>` | Shows profit or loss per week, over the last n weeks (n defaults to 8)
+| `/monthly <n>` | Shows profit or loss per month, over the last n months (n defaults to 6)
 | `/stats` | Shows Wins / losses by Sell reason as well as Avg. holding durations for buys and sells
 | `/whitelist` | Show the current whitelist
 | `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
@@ -304,8 +311,7 @@ Return the balance of all crypto-currency your have on the exchange.
 
 ### /daily <n>
 
-Per default `/daily` will return the 7 last days.
-The example below if for `/daily 3`:
+Per default `/daily` will return the 7 last days. The example below if for `/daily 3`:
 
 > **Daily Profit over the last 3 days:**
 ```
@@ -316,6 +322,34 @@ Day         Profit BTC      Profit USD
 2018-01-01  0.00269130 BTC  34.986 USD
 ```
 
+### /weekly <n>
+
+Per default `/weekly` will return the 8 last weeks, including the current week. Each week starts
+from Monday. The example below if for `/weekly 3`:
+
+> **Weekly Profit over the last 3 weeks (starting from Monday):**
+```
+Monday         Profit BTC      Profit USD
+----------  --------------  ------------
+2018-01-03  0.00224175 BTC  29,142 USD
+2017-12-27  0.00033131 BTC   4,307 USD
+2017-12-20  0.00269130 BTC  34.986 USD
+```
+
+### /monthly <n>
+
+Per default `/monthly` will return the 6 last months, including the current month. The example below
+if for `/monthly 3`:
+
+> **Monthly Profit over the last 3 months:**
+```
+Month         Profit BTC      Profit USD
+----------  --------------  ------------
+2018-01     0.00224175 BTC  29,142 USD
+2017-12     0.00033131 BTC   4,307 USD
+2017-11     0.00269130 BTC  34.986 USD
+```
+
 ### /whitelist
 
 Shows the current whitelist

docs/utils.md

@@ -26,9 +26,7 @@ optional arguments:
 ├── data
 ├── hyperopt_results
 ├── hyperopts
-│   ├── sample_hyperopt_advanced.py
 │   ├── sample_hyperopt_loss.py
-│   └── sample_hyperopt.py
 ├── notebooks
 │   └── strategy_analysis_example.ipynb
 ├── plot
@@ -111,46 +109,11 @@ Using the advanced template (populates all optional functions and methods)
 freqtrade new-strategy --strategy AwesomeStrategy --template advanced
 ```
 
-## Create new hyperopt
+## List Strategies
 
-Creates a new hyperopt from a template similar to SampleHyperopt.
-The file will be named inline with your class name, and will not overwrite existing files.
+Use the `list-strategies` subcommand to see all strategies in one particular directory.
 
-Results will be located in `user_data/hyperopts/<classname>.py`.
-
-``` output
-usage: freqtrade new-hyperopt [-h] [--userdir PATH] [--hyperopt NAME]
-                              [--template {full,minimal,advanced}]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-  --hyperopt NAME       Specify hyperopt class name which will be used by the
-                        bot.
-  --template {full,minimal,advanced}
-                        Use a template which is either `minimal`, `full`
-                        (containing multiple sample indicators) or `advanced`.
-                        Default: `full`.
-```
-
-### Sample usage of new-hyperopt
-
-```bash
-freqtrade new-hyperopt --hyperopt AwesomeHyperopt
-```
-
-With custom user directory
-
-```bash
-freqtrade new-hyperopt --userdir ~/.freqtrade/ --hyperopt AwesomeHyperopt
-```
-
-## List Strategies and List Hyperopts
-
-Use the `list-strategies` subcommand to see all strategies in one particular directory and the `list-hyperopts` subcommand to list custom Hyperopts.
-
-These subcommands are useful for finding problems in your environment with loading strategies or hyperopt classes: modules with strategies or hyperopt classes that contain errors and failed to load are printed in red (LOAD FAILED), while strategies or hyperopt classes with duplicate names are printed in yellow (DUPLICATE NAME).
+This subcommand is useful for finding problems in your environment with loading strategies: modules with strategies that contain errors and failed to load are printed in red (LOAD FAILED), while strategies with duplicate names are printed in yellow (DUPLICATE NAME).
 
 ```
 usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH]
@@ -164,34 +127,6 @@ optional arguments:
   --no-color            Disable colorization of hyperopt results. May be
                         useful if you are redirecting output to a file.
 
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are:
-                        'syslog', 'journald'. See the documentation for more
-                        details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default: `config.json`).
-                        Multiple --config options may be used. Can be set to
-                        `-` to read config from stdin.
-  -d PATH, --datadir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-```
-```
-usage: freqtrade list-hyperopts [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                                [-d PATH] [--userdir PATH]
-                                [--hyperopt-path PATH] [-1] [--no-color]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
-                        Hyperopt Loss functions.
-  -1, --one-column      Print output in one column.
-  --no-color            Disable colorization of hyperopt results. May be
-                        useful if you are redirecting output to a file.
-
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
   --logfile FILE        Log to the file specified. Special values are:
@@ -211,18 +146,16 @@ Common arguments:
 !!! Warning
     Using these commands will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
 
-Example: Search default strategies and hyperopts directories (within the default userdir).
+Example: Search default strategies directories (within the default userdir).
 
 ``` bash
 freqtrade list-strategies
-freqtrade list-hyperopts
 ```
 
-Example: Search strategies and hyperopts directory within the userdir.
+Example: Search strategies  directory within the userdir.
 
 ``` bash
 freqtrade list-strategies --userdir ~/.freqtrade/
-freqtrade list-hyperopts --userdir ~/.freqtrade/
 ```
 
 Example: Search dedicated strategy path.
@@ -231,12 +164,6 @@ Example: Search dedicated strategy path.
 freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
 ```
 
-Example: Search dedicated hyperopt path.
-
-``` bash
-freqtrade list-hyperopt --hyperopt-path ~/.freqtrade/hyperopts/
-```
-
 ## List Exchanges
 
 Use the `list-exchanges` subcommand to see the exchanges available for the bot.
@@ -354,7 +281,7 @@ bitmax              True     missing opt: fetchMyTrades
 bitmex              False    Various reasons.
 bitpanda            True
 bitso               False    missing: fetchOHLCV
-bitstamp            False    Does not provide history. Details in https://github.com/freqtrade/freqtrade/issues/1983
+bitstamp            True     missing opt: fetchTickers
 bitstamp1           False    missing: fetchOrder, fetchOHLCV
 bittrex             True
 bitvavo             True
@@ -650,6 +577,46 @@ Common arguments:
 
 ```
 
+## Show previous Backtest results
+
+Allows you to show previous backtest results.
+Adding `--show-pair-list` outputs a sorted pair list you can easily copy/paste into your configuration (omitting bad pairs).
+
+??? Warning "Strategy overfitting"
+    Only using winning pairs can lead to an overfitted strategy, which will not work well on future data. Make sure to extensively test your strategy in dry-run before risking real money.
+
+```
+usage: freqtrade backtesting-show [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                  [-d PATH] [--userdir PATH]
+                                  [--export-filename PATH] [--show-pair-list]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --export-filename PATH
+                        Save backtest results to the file with this filename.
+                        Requires `--export` to be set as well. Example:
+                        `--export-filename=user_data/backtest_results/backtest
+                        _today.json`
+  --show-pair-list      Show backtesting pairlist sorted by profit.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
+
 ## List Hyperopt results
 
 You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` sub-command.
@@ -740,6 +707,7 @@ usage: freqtrade hyperopt-show [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                [--profitable] [-n INT] [--print-json]
                                [--hyperopt-filename FILENAME] [--no-header]
                                [--disable-param-export]
+                               [--breakdown {day,week,month} [{day,week,month} ...]]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -753,6 +721,8 @@ optional arguments:
   --no-header           Do not print epoch details header.
   --disable-param-export
                         Disable automatic hyperopt parameter export.
+  --breakdown {day,week,month} [{day,week,month} ...]
+                        Show backtesting breakdown per [day, week, month].
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).

docs/webhook-config.md

@@ -48,7 +48,7 @@ Sample configuration (tested using IFTTT).
     },
 ```
 
-The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert our event and key to the url.
+The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert your event and key to the url.
 
 You can set the POST body format to Form-Encoded (default) or JSON-Encoded. Use `"format": "form"` or `"format": "json"` respectively. Example configuration for Mattermost Cloud integration:
 

environment.yml

@@ -16,7 +16,6 @@ dependencies:
     - cachetools
     - requests
     - urllib3
-    - wrapt
     - jsonschema
     - TA-Lib
     - tabulate
@@ -64,7 +63,6 @@ dependencies:
         - py_find_1st
         - tables
         - pytest-random-order
-        - flake8-type-annotations
         - ccxt
         - flake8-tidy-imports
         - -e .

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2021.8'
+__version__ = '2021.11'
 
 if __version__ == 'develop':
 
@@ -22,7 +22,7 @@ if __version__ == 'develop':
         #  subprocess.check_output(
         #     ['git', 'log', '--format="%h"', '-n 1'],
         #     stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
-    except Exception:
+    except Exception:  # pragma: no cover
         # git not available, ignore
         try:
             # Try Fallback to freqtrade_commit file (created by CI while building docker image)

freqtrade/commands/__init__.py

@@ -8,15 +8,16 @@ Note: Be careful with file-scoped imports in these subfiles.
 """
 from freqtrade.commands.arguments import Arguments
 from freqtrade.commands.build_config_commands import start_new_config
-from freqtrade.commands.data_commands import (start_convert_data, start_download_data,
-                                              start_list_data)
+from freqtrade.commands.data_commands import (start_convert_data, start_convert_trades,
+                                              start_download_data, start_list_data)
 from freqtrade.commands.deploy_commands import (start_create_userdir, start_install_ui,
-                                                start_new_hyperopt, start_new_strategy)
+                                                start_new_strategy)
 from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show
-from freqtrade.commands.list_commands import (start_list_exchanges, start_list_hyperopts,
-                                              start_list_markets, start_list_strategies,
-                                              start_list_timeframes, start_show_trades)
-from freqtrade.commands.optimize_commands import start_backtesting, start_edge, start_hyperopt
+from freqtrade.commands.list_commands import (start_list_exchanges, start_list_markets,
+                                              start_list_strategies, start_list_timeframes,
+                                              start_show_trades)
+from freqtrade.commands.optimize_commands import (start_backtesting, start_backtesting_show,
+                                                  start_edge, start_hyperopt)
 from freqtrade.commands.pairlist_commands import start_test_pairlist
 from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit
 from freqtrade.commands.trade_commands import start_trading

freqtrade/commands/arguments.py

@@ -22,8 +22,9 @@ ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
                         "max_open_trades", "stake_amount", "fee", "pairs"]
 
 ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
-                                        "enable_protections", "dry_run_wallet",
-                                        "strategy_list", "export", "exportfilename"]
+                                        "enable_protections", "dry_run_wallet", "timeframe_detail",
+                                        "strategy_list", "export", "exportfilename",
+                                        "backtest_breakdown"]
 
 ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
                                         "position_stacking", "use_max_market_positions",
@@ -31,7 +32,8 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
                                         "epochs", "spaces", "print_all",
                                         "print_colorized", "print_json", "hyperopt_jobs",
                                         "hyperopt_random_state", "hyperopt_min_trades",
-                                        "hyperopt_loss", "disableparamexport"]
+                                        "hyperopt_loss", "disableparamexport",
+                                        "hyperopt_ignore_missing_space"]
 
 ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
 
@@ -39,6 +41,8 @@ ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized"]
 
 ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
 
+ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list"]
+
 ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
 
 ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
@@ -55,16 +59,16 @@ ARGS_BUILD_CONFIG = ["config"]
 
 ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
 
-ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
-
 ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
 ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
 
+ARGS_CONVERT_TRADES = ["pairs", "timeframes", "exchange", "dataformat_ohlcv", "dataformat_trades"]
+
 ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
 
-ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "timerange",
-                      "download_trades", "exchange", "timeframes", "erase", "dataformat_ohlcv",
-                      "dataformat_trades"]
+ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "include_inactive",
+                      "timerange", "download_trades", "exchange", "timeframes",
+                      "erase", "dataformat_ohlcv", "dataformat_trades"]
 
 ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
                        "db_url", "trade_source", "export", "exportfilename",
@@ -73,7 +77,7 @@ ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
 ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
                     "trade_source", "timeframe", "plot_auto_open"]
 
-ARGS_INSTALL_UI = ["erase_ui_only"]
+ARGS_INSTALL_UI = ["erase_ui_only", 'ui_version']
 
 ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
 
@@ -88,14 +92,14 @@ ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
 
 ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
                       "print_json", "hyperoptexportfilename", "hyperopt_show_no_header",
-                      "disableparamexport"]
+                      "disableparamexport", "backtest_breakdown"]
 
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies", "list-data",
-                    "list-hyperopts", "hyperopt-list", "hyperopt-show",
-                    "plot-dataframe", "plot-profit", "show-trades"]
+                    "hyperopt-list", "hyperopt-show", "backtest-filter",
+                    "plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv"]
 
-NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
+NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
 
 
 class Arguments:
@@ -171,15 +175,15 @@ class Arguments:
         self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
         self._build_args(optionlist=['version'], parser=self.parser)
 
-        from freqtrade.commands import (start_backtesting, start_convert_data, start_create_userdir,
-                                        start_download_data, start_edge, start_hyperopt,
-                                        start_hyperopt_list, start_hyperopt_show, start_install_ui,
-                                        start_list_data, start_list_exchanges, start_list_hyperopts,
+        from freqtrade.commands import (start_backtesting, start_backtesting_show,
+                                        start_convert_data, start_convert_trades,
+                                        start_create_userdir, start_download_data, start_edge,
+                                        start_hyperopt, start_hyperopt_list, start_hyperopt_show,
+                                        start_install_ui, start_list_data, start_list_exchanges,
                                         start_list_markets, start_list_strategies,
-                                        start_list_timeframes, start_new_config, start_new_hyperopt,
-                                        start_new_strategy, start_plot_dataframe, start_plot_profit,
-                                        start_show_trades, start_test_pairlist, start_trading,
-                                        start_webserver)
+                                        start_list_timeframes, start_new_config, start_new_strategy,
+                                        start_plot_dataframe, start_plot_profit, start_show_trades,
+                                        start_test_pairlist, start_trading, start_webserver)
 
         subparsers = self.parser.add_subparsers(dest='command',
                                                 # Use custom message when no subhandler is added
@@ -206,12 +210,6 @@ class Arguments:
         build_config_cmd.set_defaults(func=start_new_config)
         self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
 
-        # add new-hyperopt subcommand
-        build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
-                                                   help="Create new hyperopt")
-        build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
-        self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
-
         # add new-strategy subcommand
         build_strategy_cmd = subparsers.add_parser('new-strategy',
                                                    help="Create new strategy")
@@ -245,6 +243,15 @@ class Arguments:
         convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
         self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
 
+        # Add trades-to-ohlcv subcommand
+        convert_trade_data_cmd = subparsers.add_parser(
+            'trades-to-ohlcv',
+            help='Convert trade data to OHLCV data.',
+            parents=[_common_parser],
+        )
+        convert_trade_data_cmd.set_defaults(func=start_convert_trades)
+        self._build_args(optionlist=ARGS_CONVERT_TRADES, parser=convert_trade_data_cmd)
+
         # Add list-data subcommand
         list_data_cmd = subparsers.add_parser(
             'list-data',
@@ -260,6 +267,15 @@ class Arguments:
         backtesting_cmd.set_defaults(func=start_backtesting)
         self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
 
+        # Add backtesting-show subcommand
+        backtesting_show_cmd = subparsers.add_parser(
+            'backtesting-show',
+            help='Show past Backtest results',
+            parents=[_common_parser],
+        )
+        backtesting_show_cmd.set_defaults(func=start_backtesting_show)
+        self._build_args(optionlist=ARGS_BACKTEST_SHOW, parser=backtesting_show_cmd)
+
         # Add edge subcommand
         edge_cmd = subparsers.add_parser('edge', help='Edge module.',
                                          parents=[_common_parser, _strategy_parser])
@@ -300,15 +316,6 @@ class Arguments:
         list_exchanges_cmd.set_defaults(func=start_list_exchanges)
         self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
 
-        # Add list-hyperopts subcommand
-        list_hyperopts_cmd = subparsers.add_parser(
-            'list-hyperopts',
-            help='Print available hyperopt classes.',
-            parents=[_common_parser],
-        )
-        list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
-        self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
-
         # Add list-markets subcommand
         list_markets_cmd = subparsers.add_parser(
             'list-markets',

freqtrade/commands/build_config_commands.py

@@ -61,27 +61,41 @@ def ask_user_config() -> Dict[str, Any]:
             "type": "text",
             "name": "stake_currency",
             "message": "Please insert your stake currency:",
-            "default": 'BTC',
+            "default": 'USDT',
         },
         {
             "type": "text",
             "name": "stake_amount",
-            "message": "Please insert your stake amount:",
-            "default": "0.01",
+            "message": f"Please insert your stake amount (Number or '{UNLIMITED_STAKE_AMOUNT}'):",
+            "default": "100",
             "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_float(val),
+            "filter": lambda val: '"' + UNLIMITED_STAKE_AMOUNT + '"'
+            if val == UNLIMITED_STAKE_AMOUNT
+            else val
         },
         {
             "type": "text",
             "name": "max_open_trades",
             "message": f"Please insert max_open_trades (Integer or '{UNLIMITED_STAKE_AMOUNT}'):",
             "default": "3",
-            "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_int(val)
+            "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_int(val),
+            "filter": lambda val: '"' + UNLIMITED_STAKE_AMOUNT + '"'
+            if val == UNLIMITED_STAKE_AMOUNT
+            else val
+        },
+        {
+            "type": "select",
+            "name": "timeframe_in_config",
+            "message": "Tim",
+            "choices": ["Have the strategy define timeframe.", "Override in configuration."]
         },
         {
             "type": "text",
             "name": "timeframe",
             "message": "Please insert your desired timeframe (e.g. 5m):",
             "default": "5m",
+            "when": lambda x: x["timeframe_in_config"] == 'Override in configuration.'
+
         },
         {
             "type": "text",
@@ -99,6 +113,9 @@ def ask_user_config() -> Dict[str, Any]:
                 "bittrex",
                 "kraken",
                 "ftx",
+                "kucoin",
+                "gateio",
+                "okex",
                 Separator(),
                 "other",
             ],
@@ -122,6 +139,12 @@ def ask_user_config() -> Dict[str, Any]:
             "message": "Insert Exchange Secret",
             "when": lambda x: not x['dry_run']
         },
+        {
+            "type": "password",
+            "name": "exchange_key_password",
+            "message": "Insert Exchange API Key password",
+            "when": lambda x: not x['dry_run'] and x['exchange_name'] in ('kucoin', 'okex')
+        },
         {
             "type": "confirm",
             "name": "telegram",
@@ -149,7 +172,8 @@ def ask_user_config() -> Dict[str, Any]:
         {
             "type": "text",
             "name": "api_server_listen_addr",
-            "message": "Insert Api server Listen Address (best left untouched default!)",
+            "message": ("Insert Api server Listen Address (0.0.0.0 for docker, "
+                        "otherwise best left untouched)"),
             "default": "127.0.0.1",
             "when": lambda x: x['api_server']
         },

freqtrade/commands/cli_options.py

@@ -1,7 +1,7 @@
 """
 Definition of cli arguments used in arguments.py
 """
-from argparse import ArgumentTypeError
+from argparse import SUPPRESS, ArgumentTypeError
 
 from freqtrade import __version__, constants
 from freqtrade.constants import HYPEROPT_LOSS_BUILTIN
@@ -135,6 +135,10 @@ AVAILABLE_CLI_OPTIONS = {
         help='Override the value of the `stake_amount` configuration setting.',
     ),
     # Backtesting
+    "timeframe_detail": Arg(
+        '--timeframe-detail',
+        help='Specify detail timeframe for backtesting (`1m`, `5m`, `30m`, `1h`, `1d`).',
+    ),
     "position_stacking": Arg(
         '--eps', '--enable-position-stacking',
         help='Allow buying the same pair multiple times (position stacking).',
@@ -148,6 +152,12 @@ AVAILABLE_CLI_OPTIONS = {
         action='store_false',
         default=True,
     ),
+    "backtest_show_pair_list": Arg(
+        '--show-pair-list',
+        help='Show backtesting pairlist sorted by profit.',
+        action='store_true',
+        default=False,
+    ),
     "enable_protections": Arg(
         '--enable-protections', '--enableprotections',
         help='Enable protections for backtesting.'
@@ -189,6 +199,12 @@ AVAILABLE_CLI_OPTIONS = {
         type=float,
         metavar='FLOAT',
     ),
+    "backtest_breakdown": Arg(
+        '--breakdown',
+        help='Show backtesting breakdown per [day, week, month].',
+        nargs='+',
+        choices=constants.BACKTEST_BREAKDOWNS
+    ),
     # Edge
     "stoploss_range": Arg(
         '--stoplosses',
@@ -199,13 +215,13 @@ AVAILABLE_CLI_OPTIONS = {
     # Hyperopt
     "hyperopt": Arg(
         '--hyperopt',
-        help='Specify hyperopt class name which will be used by the bot.',
+        help=SUPPRESS,
         metavar='NAME',
         required=False,
     ),
     "hyperopt_path": Arg(
         '--hyperopt-path',
-        help='Specify additional lookup path for Hyperopt and Hyperopt Loss functions.',
+        help='Specify additional lookup path for Hyperopt Loss functions.',
         metavar='PATH',
     ),
     "epochs": Arg(
@@ -351,6 +367,11 @@ AVAILABLE_CLI_OPTIONS = {
         type=check_int_positive,
         metavar='INT',
     ),
+    "include_inactive": Arg(
+        '--include-inactive-pairs',
+        help='Also download data from inactive pairs.',
+        action='store_true',
+    ),
     "new_pairs_days": Arg(
         '--new-pairs-days',
         help='Download data of new pairs for given number of days. Default: `%(default)s`.',
@@ -377,12 +398,12 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "dataformat_ohlcv": Arg(
         '--data-format-ohlcv',
-        help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
+        help='Storage format for downloaded candle (OHLCV) data. (default: `json`).',
         choices=constants.AVAILABLE_DATAHANDLERS,
     ),
     "dataformat_trades": Arg(
         '--data-format-trades',
-        help='Storage format for downloaded trades data. (default: `%(default)s`).',
+        help='Storage format for downloaded trades data. (default: `jsongz`).',
         choices=constants.AVAILABLE_DATAHANDLERS,
     ),
     "exchange": Arg(
@@ -410,6 +431,12 @@ AVAILABLE_CLI_OPTIONS = {
         action='store_true',
         default=False,
     ),
+    "ui_version": Arg(
+        '--ui-version',
+        help=('Specify a specific version of FreqUI to install. '
+              'Not specifying this installs the latest version.'),
+        type=str,
+    ),
     # Templating options
     "template": Arg(
         '--template',
@@ -548,4 +575,10 @@ AVAILABLE_CLI_OPTIONS = {
         help='Do not print epoch details header.',
         action='store_true',
     ),
+    "hyperopt_ignore_missing_space": Arg(
+        "--ignore-missing-spaces", "--ignore-unparameterized-spaces",
+        help=("Suppress errors for any requested Hyperopt spaces "
+              "that do not contain any parameters."),
+        action="store_true",
+    ),
 }

freqtrade/commands/data_commands.py

@@ -11,6 +11,7 @@ from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_oh
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.exchange.exchange import market_is_active
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.resolvers import ExchangeResolver
 
@@ -47,11 +48,13 @@ def start_download_data(args: Dict[str, Any]) -> None:
 
     # Init exchange
     exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    markets = [p for p, m in exchange.markets.items() if market_is_active(m)
+               or config.get('include_inactive')]
+    expanded_pairs = expand_pairlist(config['pairs'], markets)
+
     # Manual validations of relevant settings
     if not config['exchange'].get('skip_pair_validation', False):
-        exchange.validate_pairs(config['pairs'])
-    expanded_pairs = expand_pairlist(config['pairs'], list(exchange.markets))
-
+        exchange.validate_pairs(expanded_pairs)
     logger.info(f"About to download pairs: {expanded_pairs}, "
                 f"intervals: {config['timeframes']} to {config['datadir']}")
 
@@ -89,6 +92,41 @@ def start_download_data(args: Dict[str, Any]) -> None:
                         f"on exchange {exchange.name}.")
 
 
+def start_convert_trades(args: Dict[str, Any]) -> None:
+
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    timerange = TimeRange()
+
+    # Remove stake-currency to skip checks which are not relevant for datadownload
+    config['stake_currency'] = ''
+
+    if 'pairs' not in config:
+        raise OperationalException(
+            "Downloading data requires a list of pairs. "
+            "Please check the documentation on how to configure this.")
+
+    # Init exchange
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    # Manual validations of relevant settings
+    if not config['exchange'].get('skip_pair_validation', False):
+        exchange.validate_pairs(config['pairs'])
+    expanded_pairs = expand_pairlist(config['pairs'], list(exchange.markets))
+
+    logger.info(f"About to Convert pairs: {expanded_pairs}, "
+                f"intervals: {config['timeframes']} to {config['datadir']}")
+
+    for timeframe in config['timeframes']:
+        exchange.validate_timeframes(timeframe)
+    # Convert downloaded trade data to different timeframes
+    convert_trades_to_ohlcv(
+        pairs=expanded_pairs, timeframes=config['timeframes'],
+        datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
+        data_format_ohlcv=config['dataformat_ohlcv'],
+        data_format_trades=config['dataformat_trades'],
+    )
+
+
 def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
     """
     Convert data from one format to another

freqtrade/commands/deploy_commands.py

@@ -7,7 +7,7 @@ import requests
 
 from freqtrade.configuration import setup_utils_configuration
 from freqtrade.configuration.directory_operations import copy_sample_files, create_userdata_dir
-from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
+from freqtrade.constants import USERPATH_STRATEGIES
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import render_template, render_template_with_fallback
@@ -87,56 +87,6 @@ def start_new_strategy(args: Dict[str, Any]) -> None:
         raise OperationalException("`new-strategy` requires --strategy to be set.")
 
 
-def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: str) -> None:
-    """
-    Deploys a new hyperopt template to hyperopt_path
-    """
-    fallback = 'full'
-    buy_guards = render_template_with_fallback(
-        templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",
-        templatefallbackfile=f"subtemplates/hyperopt_buy_guards_{fallback}.j2",
-    )
-    sell_guards = render_template_with_fallback(
-        templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",
-        templatefallbackfile=f"subtemplates/hyperopt_sell_guards_{fallback}.j2",
-    )
-    buy_space = render_template_with_fallback(
-        templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",
-        templatefallbackfile=f"subtemplates/hyperopt_buy_space_{fallback}.j2",
-    )
-    sell_space = render_template_with_fallback(
-        templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",
-        templatefallbackfile=f"subtemplates/hyperopt_sell_space_{fallback}.j2",
-    )
-
-    strategy_text = render_template(templatefile='base_hyperopt.py.j2',
-                                    arguments={"hyperopt": hyperopt_name,
-                                               "buy_guards": buy_guards,
-                                               "sell_guards": sell_guards,
-                                               "buy_space": buy_space,
-                                               "sell_space": sell_space,
-                                               })
-
-    logger.info(f"Writing hyperopt to `{hyperopt_path}`.")
-    hyperopt_path.write_text(strategy_text)
-
-
-def start_new_hyperopt(args: Dict[str, Any]) -> None:
-
-    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
-
-    if 'hyperopt' in args and args['hyperopt']:
-
-        new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args['hyperopt'] + '.py')
-
-        if new_path.exists():
-            raise OperationalException(f"`{new_path}` already exists. "
-                                       "Please choose another Hyperopt Name.")
-        deploy_new_hyperopt(args['hyperopt'], new_path, args['template'])
-    else:
-        raise OperationalException("`new-hyperopt` requires --hyperopt to be set.")
-
-
 def clean_ui_subdir(directory: Path):
     if directory.is_dir():
         logger.info("Removing UI directory content.")
@@ -178,7 +128,7 @@ def download_and_install_ui(dest_folder: Path, dl_url: str, version: str):
         f.write(version)
 
 
-def get_ui_download_url() -> Tuple[str, str]:
+def get_ui_download_url(version: Optional[str] = None) -> Tuple[str, str]:
     base_url = 'https://api.github.com/repos/freqtrade/frequi/'
     # Get base UI Repo path
 
@@ -186,8 +136,16 @@ def get_ui_download_url() -> Tuple[str, str]:
     resp.raise_for_status()
     r = resp.json()
 
-    latest_version = r[0]['name']
-    assets = r[0].get('assets', [])
+    if version:
+        tmp = [x for x in r if x['name'] == version]
+        if tmp:
+            latest_version = tmp[0]['name']
+            assets = tmp[0].get('assets', [])
+        else:
+            raise ValueError("UI-Version not found.")
+    else:
+        latest_version = r[0]['name']
+        assets = r[0].get('assets', [])
     dl_url = ''
     if assets and len(assets) > 0:
         dl_url = assets[0]['browser_download_url']
@@ -206,7 +164,7 @@ def start_install_ui(args: Dict[str, Any]) -> None:
 
     dest_folder = Path(__file__).parents[1] / 'rpc/api_server/ui/installed/'
     # First make sure the assets are removed.
-    dl_url, latest_version = get_ui_download_url()
+    dl_url, latest_version = get_ui_download_url(args.get('ui_version'))
 
     curr_version = read_ui_version(dest_folder)
     if curr_version == latest_version and not args.get('erase_ui_only'):

freqtrade/commands/hyperopt_commands.py

@@ -53,7 +53,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
 
     if epochs and export_csv:
         HyperoptTools.export_csv_file(
-            config, epochs, total_epochs, not config.get('hyperopt_list_best', False), export_csv
+            config, epochs, export_csv
         )
 
 
@@ -96,7 +96,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
         if 'strategy_name' in metrics:
             strategy_name = metrics['strategy_name']
             show_backtest_result(strategy_name, metrics,
-                                 metrics['stake_currency'])
+                                 metrics['stake_currency'], config.get('backtest_breakdown', []))
 
             HyperoptTools.try_export_params(config, strategy_name, val)
 

freqtrade/commands/list_commands.py

@@ -10,7 +10,7 @@ from colorama import init as colorama_init
 from tabulate import tabulate
 
 from freqtrade.configuration import setup_utils_configuration
-from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
+from freqtrade.constants import USERPATH_STRATEGIES
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import market_is_active, validate_exchanges
@@ -92,25 +92,6 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
         _print_objs_tabular(strategy_objs, config.get('print_colorized', False))
 
 
-def start_list_hyperopts(args: Dict[str, Any]) -> None:
-    """
-    Print files with HyperOpt custom classes available in the directory
-    """
-    from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
-
-    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
-
-    directory = Path(config.get('hyperopt_path', config['user_data_dir'] / USERPATH_HYPEROPTS))
-    hyperopt_objs = HyperOptResolver.search_all_objects(directory, not args['print_one_column'])
-    # Sort alphabetically
-    hyperopt_objs = sorted(hyperopt_objs, key=lambda x: x['name'])
-
-    if args['print_one_column']:
-        print('\n'.join([s['name'] for s in hyperopt_objs]))
-    else:
-        _print_objs_tabular(hyperopt_objs, config.get('print_colorized', False))
-
-
 def start_list_timeframes(args: Dict[str, Any]) -> None:
     """
     Print timeframes available on Exchange

freqtrade/commands/optimize_commands.py

@@ -54,6 +54,22 @@ def start_backtesting(args: Dict[str, Any]) -> None:
     backtesting.start()
 
 
+def start_backtesting_show(args: Dict[str, Any]) -> None:
+    """
+    Show previous backtest result
+    """
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    from freqtrade.data.btanalysis import load_backtest_stats
+    from freqtrade.optimize.optimize_reports import show_backtest_results, show_sorted_pairlist
+
+    results = load_backtest_stats(config['exportfilename'])
+
+    show_backtest_results(config, results)
+    show_sorted_pairlist(config, results)
+
+
 def start_hyperopt(args: Dict[str, Any]) -> None:
     """
     Start hyperopt script

freqtrade/configuration/PeriodicCache.py

@@ -0,0 +1,19 @@
+from datetime import datetime, timezone
+
+from cachetools.ttl import TTLCache
+
+
+class PeriodicCache(TTLCache):
+    """
+    Special cache that expires at "straight" times
+    A timer with ttl of 3600 (1h) will expire at every full hour (:00).
+    """
+
+    def __init__(self, maxsize, ttl, getsizeof=None):
+        def local_timer():
+            ts = datetime.now(timezone.utc).timestamp()
+            offset = (ts % ttl)
+            return ts - offset
+
+        # Init with smlight offset
+        super().__init__(maxsize=maxsize, ttl=ttl-1e-5, timer=local_timer, getsizeof=getsizeof)

freqtrade/configuration/__init__.py

@@ -1,7 +1,8 @@
 # flake8: noqa: F401
 
-from freqtrade.configuration.check_exchange import check_exchange, remove_credentials
+from freqtrade.configuration.check_exchange import check_exchange
 from freqtrade.configuration.config_setup import setup_utils_configuration
 from freqtrade.configuration.config_validation import validate_config_consistency
 from freqtrade.configuration.configuration import Configuration
+from freqtrade.configuration.PeriodicCache import PeriodicCache
 from freqtrade.configuration.timerange import TimeRange

freqtrade/configuration/check_exchange.py

@@ -10,19 +10,6 @@ from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
 logger = logging.getLogger(__name__)
 
 
-def remove_credentials(config: Dict[str, Any]) -> None:
-    """
-    Removes exchange keys from the configuration and specifies dry-run
-    Used for backtesting / hyperopt / edge and utils.
-    Modifies the input dict!
-    """
-    config['exchange']['key'] = ''
-    config['exchange']['secret'] = ''
-    config['exchange']['password'] = ''
-    config['exchange']['uid'] = ''
-    config['dry_run'] = True
-
-
 def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
     """
     Check if the exchange name in the config file is supported by Freqtrade

freqtrade/configuration/config_setup.py

@@ -3,7 +3,6 @@ from typing import Any, Dict
 
 from freqtrade.enums import RunMode
 
-from .check_exchange import remove_credentials
 from .config_validation import validate_config_consistency
 from .configuration import Configuration
 
@@ -21,8 +20,8 @@ def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str
     configuration = Configuration(args, method)
     config = configuration.get_config()
 
-    # Ensure we do not use Exchange credentials
-    remove_credentials(config)
+    # Ensure these modes are using Dry-run
+    config['dry_run'] = True
     validate_config_consistency(config)
 
     return config

freqtrade/configuration/configuration.py

@@ -242,6 +242,13 @@ class Configuration:
             except ValueError:
                 pass
 
+        self._args_to_config(config, argname='timeframe_detail',
+                             logstring='Parameter --timeframe-detail detected, '
+                             'using {} for intra-candle backtesting ...')
+
+        self._args_to_config(config, argname='backtest_show_pair_list',
+                             logstring='Parameter --show-pair-list detected.')
+
         self._args_to_config(config, argname='stake_amount',
                              logstring='Parameter --stake-amount detected, '
                              'overriding stake_amount to: {} ...')
@@ -266,8 +273,12 @@ class Configuration:
         self._args_to_config(config, argname='export',
                              logstring='Parameter --export detected: {} ...')
 
+        self._args_to_config(config, argname='backtest_breakdown',
+                             logstring='Parameter --breakdown detected ...')
+
         self._args_to_config(config, argname='disableparamexport',
                              logstring='Parameter --disableparamexport detected: {} ...')
+
         # Edge section:
         if 'stoploss_range' in self.args and self.args["stoploss_range"]:
             txt_range = eval(self.args["stoploss_range"])
@@ -366,6 +377,9 @@ class Configuration:
         self._args_to_config(config, argname='hyperopt_show_no_header',
                              logstring='Parameter --no-header detected: {}')
 
+        self._args_to_config(config, argname="hyperopt_ignore_missing_space",
+                             logstring="Paramter --ignore-missing-space detected: {}")
+
     def _process_plot_options(self, config: Dict[str, Any]) -> None:
 
         self._args_to_config(config, argname='pairs',
@@ -401,6 +415,9 @@ class Configuration:
         self._args_to_config(config, argname='days',
                              logstring='Detected --days: {}')
 
+        self._args_to_config(config, argname='include_inactive',
+                             logstring='Detected --include-inactive-pairs: {}')
+
         self._args_to_config(config, argname='download_trades',
                              logstring='Detected --dl-trades: {}')
 

freqtrade/configuration/environment_vars.py

@@ -32,6 +32,7 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
     :param prefix: Prefix to consider (usually FREQTRADE__)
     :return: Nested dict based on available and relevant variables.
     """
+    no_convert = ['CHAT_ID']
     relevant_vars: Dict[str, Any] = {}
 
     for env_var, val in sorted(env_dict.items()):
@@ -39,9 +40,9 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
             logger.info(f"Loading variable '{env_var}'")
             key = env_var.replace(prefix, '')
             for k in reversed(key.split('__')):
-                val = {k.lower(): get_var_typed(val) if type(val) != dict else val}
+                val = {k.lower(): get_var_typed(val)
+                       if type(val) != dict and k not in no_convert else val}
             relevant_vars = deep_merge_dicts(val, relevant_vars)
-
     return relevant_vars
 
 

freqtrade/constants.py

@@ -24,13 +24,16 @@ ORDERTYPE_POSSIBILITIES = ['limit', 'market']
 ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
 HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
-                         'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily']
+                         'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
+                         'CalmarHyperOptLoss',
+                         'MaxDrawDownHyperOptLoss']
 AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
                        'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
                        'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
                        'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
 AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
 AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
+BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
 DRY_RUN_WALLET = 1000
 DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
@@ -51,7 +54,6 @@ ENV_VAR_PREFIX = 'FREQTRADE__'
 
 NON_OPEN_EXCHANGE_STATES = ('cancelled', 'canceled', 'closed', 'expired')
 
-
 # Define decimals per coin for outputs
 # Only used for outputs.
 DECIMAL_PER_COIN_FALLBACK = 3  # Should be low to avoid listing all possible FIAT's
@@ -65,13 +67,10 @@ DUST_PER_COIN = {
     'ETH': 0.01
 }
 
-
 # Source files with destination directories within user-directory
 USER_DATA_FILES = {
     'sample_strategy.py': USERPATH_STRATEGIES,
-    'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
     'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
-    'sample_hyperopt.py': USERPATH_HYPEROPTS,
     'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS,
 }
 
@@ -112,7 +111,7 @@ CONF_SCHEMA = {
         },
         'tradable_balance_ratio': {
             'type': 'number',
-            'minimum': 0.1,
+            'minimum': 0.0,
             'maximum': 1,
             'default': 0.99
         },
@@ -147,12 +146,17 @@ CONF_SCHEMA = {
         'sell_profit_offset': {'type': 'number'},
         'ignore_roi_if_buy_signal': {'type': 'boolean'},
         'ignore_buying_expired_candle_after': {'type': 'number'},
+        'backtest_breakdown': {
+            'type': 'array',
+            'items': {'type': 'string', 'enum': BACKTEST_BREAKDOWNS}
+        },
         'bot_name': {'type': 'string'},
         'unfilledtimeout': {
             'type': 'object',
             'properties': {
                 'buy': {'type': 'number', 'minimum': 1},
                 'sell': {'type': 'number', 'minimum': 1},
+                'exit_timeout_count': {'type': 'number', 'minimum': 0, 'default': 0},
                 'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
             }
         },
@@ -194,7 +198,7 @@ CONF_SCHEMA = {
             'required': ['price_side']
         },
         'custom_price_max_distance_ratio': {
-           'type': 'number', 'minimum': 0.0
+            'type': 'number', 'minimum': 0.0
         },
         'order_types': {
             'type': 'object',
@@ -203,7 +207,10 @@ CONF_SCHEMA = {
                 'sell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
                 'forcesell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
                 'forcebuy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
-                'emergencysell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
+                'emergencysell': {
+                    'type': 'string',
+                    'enum': ORDERTYPE_POSSIBILITIES,
+                    'default': 'market'},
                 'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
                 'stoploss_on_exchange': {'type': 'boolean'},
                 'stoploss_on_exchange_interval': {'type': 'number'},
@@ -286,6 +293,15 @@ CONF_SCHEMA = {
                             'enum': TELEGRAM_SETTING_OPTIONS,
                             'default': 'off'
                         },
+                        'protection_trigger': {
+                            'type': 'string',
+                            'enum': TELEGRAM_SETTING_OPTIONS,
+                            'default': 'off'
+                        },
+                        'protection_trigger_global': {
+                            'type': 'string',
+                            'enum': TELEGRAM_SETTING_OPTIONS,
+                        },
                     }
                 },
                 'reload': {'type': 'boolean'},
@@ -338,13 +354,13 @@ CONF_SCHEMA = {
         },
         'dataformat_ohlcv': {
             'type': 'string',
-                    'enum': AVAILABLE_DATAHANDLERS,
-                    'default': 'json'
+            'enum': AVAILABLE_DATAHANDLERS,
+            'default': 'json'
         },
         'dataformat_trades': {
             'type': 'string',
-                    'enum': AVAILABLE_DATAHANDLERS,
-                    'default': 'jsongz'
+            'enum': AVAILABLE_DATAHANDLERS,
+            'default': 'jsongz'
         }
     },
     'definitions': {

freqtrade/data/converter.py

@@ -113,7 +113,7 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
     pct_missing = (len_after - len_before) / len_before if len_before > 0 else 0
     if len_before != len_after:
         message = (f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}"
-                   f" - {round(pct_missing * 100, 2)}%")
+                   f" - {pct_missing:.2%}")
         if pct_missing > 0.01:
             logger.info(message)
         else:

freqtrade/data/dataprovider.py

@@ -149,6 +149,8 @@ class DataProvider:
         Clear pair dataframe cache.
         """
         self.__cached_pairs = {}
+        self.__cached_pairs_backtesting = {}
+        self.__slice_index = 0
 
     # Exchange functions
 

freqtrade/data/history/history_utils.py

@@ -197,7 +197,8 @@ def _download_pair_history(pair: str, *,
                                                timeframe=timeframe,
                                                since_ms=since_ms if since_ms else
                                                arrow.utcnow().shift(
-                                                   days=-new_pairs_days).int_timestamp * 1000
+                                                   days=-new_pairs_days).int_timestamp * 1000,
+                                               is_new_pair=data.empty
                                                )
         # TODO: Maybe move parsing to exchange class (?)
         new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,

freqtrade/edge/edge_positioning.py

@@ -119,7 +119,7 @@ class Edge:
             )
             # Download informative pairs too
             res = defaultdict(list)
-            for p, t in self.strategy.informative_pairs():
+            for p, t in self.strategy.gather_informative_pairs():
                 res[t].append(p)
             for timeframe, inf_pairs in res.items():
                 timerange_startup = deepcopy(self._timerange)

freqtrade/enums/rpcmessagetype.py

@@ -11,6 +11,8 @@ class RPCMessageType(Enum):
     SELL = 'sell'
     SELL_FILL = 'sell_fill'
     SELL_CANCEL = 'sell_cancel'
+    PROTECTION_TRIGGER = 'protection_trigger'
+    PROTECTION_TRIGGER_GLOBAL = 'protection_trigger_global'
 
     def __repr__(self):
         return self.value

freqtrade/enums/signaltype.py

@@ -14,3 +14,4 @@ class SignalTagType(Enum):
     Enum for signal columns
     """
     BUY_TAG = "buy_tag"
+    EXIT_TAG = "exit_tag"

freqtrade/exceptions.py

@@ -1,5 +1,3 @@
-
-
 class FreqtradeException(Exception):
     """
     Freqtrade base exception. Handled at the outermost level.

freqtrade/exchange/__init__.py

@@ -1,6 +1,6 @@
 # flake8: noqa: F401
 # isort: off
-from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS
+from freqtrade.exchange.common import remove_credentials, MAP_EXCHANGE_CHILDCLASS
 from freqtrade.exchange.exchange import Exchange
 # isort: on
 from freqtrade.exchange.bibox import Bibox
@@ -19,3 +19,4 @@ from freqtrade.exchange.gateio import Gateio
 from freqtrade.exchange.hitbtc import Hitbtc
 from freqtrade.exchange.kraken import Kraken
 from freqtrade.exchange.kucoin import Kucoin
+from freqtrade.exchange.okex import Okex

freqtrade/exchange/binance.py

@@ -1,7 +1,8 @@
 """ Binance exchange subclass """
 import logging
-from typing import Dict
+from typing import Dict, List, Tuple
 
+import arrow
 import ccxt
 
 from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, InvalidOrderException,
@@ -18,6 +19,7 @@ class Binance(Exchange):
     _ft_has: Dict = {
         "stoploss_on_exchange": True,
         "order_time_in_force": ['gtc', 'fok', 'ioc'],
+        "time_in_force_parameter": "timeInForce",
         "ohlcv_candle_limit": 1000,
         "trades_pagination": "id",
         "trades_pagination_arg": "fromId",
@@ -89,3 +91,22 @@ class Binance(Exchange):
                 f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
+
+    async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
+                                        since_ms: int, is_new_pair: bool = False,
+                                        raise_: bool = False
+                                        ) -> Tuple[str, str, List]:
+        """
+        Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
+        Does not work for other exchanges, which don't return the earliest data when called with "0"
+        """
+        if is_new_pair:
+            x = await self._async_get_candle_history(pair, timeframe, 0)
+            if x and x[2] and x[2][0] and x[2][0][0] > since_ms:
+                # Set starting date to first available candle.
+                since_ms = x[2][0][0]
+                logger.info(f"Candle-data for {pair} available starting with "
+                            f"{arrow.get(since_ms // 1000).isoformat()}.")
+        return await super()._async_get_historic_ohlcv(
+            pair=pair, timeframe=timeframe, since_ms=since_ms, is_new_pair=is_new_pair,
+            raise_=raise_)

freqtrade/exchange/common.py

@@ -16,8 +16,6 @@ API_FETCH_ORDER_RETRY_COUNT = 5
 
 BAD_EXCHANGES = {
     "bitmex": "Various reasons.",
-    "bitstamp": "Does not provide history. "
-                "Details in https://github.com/freqtrade/freqtrade/issues/1983",
     "phemex": "Does not provide history. ",
     "poloniex": "Does not provide fetch_order endpoint to fetch both open and closed orders.",
 }
@@ -51,6 +49,19 @@ EXCHANGE_HAS_OPTIONAL = [
 ]
 
 
+def remove_credentials(config) -> None:
+    """
+    Removes exchange keys from the configuration and specifies dry-run
+    Used for backtesting / hyperopt / edge and utils.
+    Modifies the input dict!
+    """
+    if config.get('dry_run', False):
+        config['exchange']['key'] = ''
+        config['exchange']['secret'] = ''
+        config['exchange']['password'] = ''
+        config['exchange']['uid'] = ''
+
+
 def calculate_backoff(retrycount, max_retries):
     """
     Calculate backoff
@@ -70,9 +81,16 @@ def retrier_async(f):
                 count -= 1
                 kwargs.update({'count': count})
                 if isinstance(ex, DDosProtection):
-                    backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
-                    logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
-                    await asyncio.sleep(backoff_delay)
+                    if "kucoin" in str(ex) and "429000" in str(ex):
+                        # Temporary fix for 429000 error on kucoin
+                        # see https://github.com/freqtrade/freqtrade/issues/5700 for details.
+                        logger.warning(
+                            f"Kucoin 429 error, avoid triggering DDosProtection backoff delay. "
+                            f"{count} tries left before giving up")
+                    else:
+                        backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
+                        logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
+                        await asyncio.sleep(backoff_delay)
                 return await wrapper(*args, **kwargs)
             else:
                 logger.warning('Giving up retrying: %s()', f.__name__)

freqtrade/exchange/exchange.py

@@ -7,7 +7,7 @@ import http
 import inspect
 import logging
 from copy import deepcopy
-from datetime import datetime, timezone
+from datetime import datetime, timedelta, timezone
 from math import ceil
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -26,9 +26,9 @@ from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFun
                                   InvalidOrderException, OperationalException, PricingError,
                                   RetryableOrderError, TemporaryError)
 from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
-                                       EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED, retrier,
-                                       retrier_async)
-from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
+                                       EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
+                                       remove_credentials, retrier, retrier_async)
+from freqtrade.misc import chunks, deep_merge_dicts, safe_value_fallback2
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 
 
@@ -54,12 +54,16 @@ class Exchange:
     # Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
     _params: Dict = {}
 
+    # Additional headers - added to the ccxt object
+    _headers: Dict = {}
+
     # Dict to specify which options each exchange implements
     # This defines defaults, which can be selectively overridden by subclasses using _ft_has
     # or by specifying them in the configuration.
     _ft_has_default: Dict = {
         "stoploss_on_exchange": False,
         "order_time_in_force": ["gtc"],
+        "time_in_force_parameter": "timeInForce",
         "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
         "ohlcv_partial_candle": True,
@@ -100,6 +104,7 @@ class Exchange:
 
         # Holds all open sell orders for dry_run
         self._dry_run_open_orders: Dict[str, Any] = {}
+        remove_credentials(config)
 
         if config['dry_run']:
             logger.info('Instance is running with dry_run enabled')
@@ -150,8 +155,8 @@ class Exchange:
                 self.validate_pairs(config['exchange']['pair_whitelist'])
             self.validate_ordertypes(config.get('order_types', {}))
             self.validate_order_time_in_force(config.get('order_time_in_force', {}))
-            self.validate_required_startup_candles(config.get('startup_candle_count', 0),
-                                                   config.get('timeframe', ''))
+            self.required_candle_call_count = self.validate_required_startup_candles(
+                config.get('startup_candle_count', 0), config.get('timeframe', ''))
 
         # Converts the interval provided in minutes in config to seconds
         self.markets_refresh_interval: int = exchange_config.get(
@@ -169,7 +174,7 @@ class Exchange:
             asyncio.get_event_loop().run_until_complete(self._api_async.close())
 
     def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
-                   ccxt_kwargs: dict = None) -> ccxt.Exchange:
+                   ccxt_kwargs: Dict = {}) -> ccxt.Exchange:
         """
         Initialize ccxt with given config and return valid
         ccxt instance.
@@ -188,6 +193,10 @@ class Exchange:
         }
         if ccxt_kwargs:
             logger.info('Applying additional ccxt config: %s', ccxt_kwargs)
+        if self._headers:
+            # Inject static headers after the above output to not confuse users.
+            ccxt_kwargs = deep_merge_dicts({'headers': self._headers}, ccxt_kwargs)
+        if ccxt_kwargs:
             ex_config.update(ccxt_kwargs)
         try:
 
@@ -352,9 +361,16 @@ class Exchange:
     def validate_stakecurrency(self, stake_currency: str) -> None:
         """
         Checks stake-currency against available currencies on the exchange.
+        Only runs on startup. If markets have not been loaded, there's been a problem with
+        the connection to the exchange.
         :param stake_currency: Stake-currency to validate
         :raise: OperationalException if stake-currency is not available.
         """
+        if not self._markets:
+            raise OperationalException(
+                'Could not load markets, therefore cannot start. '
+                'Please investigate the above error for more details.'
+                )
         quote_currencies = self.get_quote_currencies()
         if stake_currency not in quote_currencies:
             raise OperationalException(
@@ -455,16 +471,29 @@ class Exchange:
             raise OperationalException(
                 f'Time in force policies are not supported for {self.name} yet.')
 
-    def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> None:
+    def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> int:
         """
         Checks if required startup_candles is more than ohlcv_candle_limit().
         Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
         """
         candle_limit = self.ohlcv_candle_limit(timeframe)
-        if startup_candles + 5 > candle_limit:
+        # Require one more candle - to account for the still open candle.
+        candle_count = startup_candles + 1
+        # Allow 5 calls to the exchange per pair
+        required_candle_call_count = int(
+            (candle_count / candle_limit) + (0 if candle_count % candle_limit == 0 else 1))
+
+        if required_candle_call_count > 5:
+            # Only allow 5 calls per pair to somewhat limit the impact
             raise OperationalException(
-                f"This strategy requires {startup_candles} candles to start. "
-                f"{self.name} only provides {candle_limit} for {timeframe}.")
+                f"This strategy requires {startup_candles} candles to start, which is more than 5x "
+                f"the amount of candles {self.name} provides for {timeframe}.")
+
+        if required_candle_call_count > 1:
+            logger.warning(f"Using {required_candle_call_count} calls to get OHLCV. "
+                           f"This can result in slower operations for the bot. Please check "
+                           f"if you really need {startup_candles} candles for your strategy")
+        return required_candle_call_count
 
     def exchange_has(self, endpoint: str) -> bool:
         """
@@ -507,7 +536,7 @@ class Exchange:
                 precision = self.markets[pair]['precision']['price']
                 missing = price % precision
                 if missing != 0:
-                    price = price - missing + precision
+                    price = round(price - missing + precision, 10)
             else:
                 symbol_prec = self.markets[pair]['precision']['price']
                 big_price = price * pow(10, symbol_prec)
@@ -709,7 +738,8 @@ class Exchange:
 
         params = self._params.copy()
         if time_in_force != 'gtc' and ordertype != 'market':
-            params.update({'timeInForce': time_in_force})
+            param = self._ft_has.get('time_in_force_parameter', '')
+            params.update({param: time_in_force})
 
         try:
             # Set the precision for amount and price(rate) as accepted by the exchange
@@ -1041,7 +1071,7 @@ class Exchange:
             ticker_rate = ticker[conf_strategy['price_side']]
             if ticker['last'] and ticker_rate:
                 if side == 'buy' and ticker_rate > ticker['last']:
-                    balance = conf_strategy['ask_last_balance']
+                    balance = conf_strategy.get('ask_last_balance', 0.0)
                     ticker_rate = ticker_rate + balance * (ticker['last'] - ticker_rate)
                 elif side == 'sell' and ticker_rate < ticker['last']:
                     balance = conf_strategy.get('bid_last_balance', 0.0)
@@ -1178,7 +1208,7 @@ class Exchange:
     # Historic data
 
     def get_historic_ohlcv(self, pair: str, timeframe: str,
-                           since_ms: int) -> List:
+                           since_ms: int, is_new_pair: bool = False) -> List:
         """
         Get candle history using asyncio and returns the list of candles.
         Handles all async work for this.
@@ -1188,9 +1218,11 @@ class Exchange:
         :param since_ms: Timestamp in milliseconds to get history from
         :return: List with candle (OHLCV) data
         """
-        return asyncio.get_event_loop().run_until_complete(
+        pair, timeframe, data = asyncio.get_event_loop().run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
-                                           since_ms=since_ms))
+                                           since_ms=since_ms, is_new_pair=is_new_pair))
+        logger.info(f"Downloaded data for {pair} with length {len(data)}.")
+        return data
 
     def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
                                  since_ms: int) -> DataFrame:
@@ -1205,11 +1237,13 @@ class Exchange:
         return ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
                                   drop_incomplete=self._ohlcv_partial_candle)
 
-    async def _async_get_historic_ohlcv(self, pair: str,
-                                        timeframe: str,
-                                        since_ms: int) -> List:
+    async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
+                                        since_ms: int, is_new_pair: bool = False,
+                                        raise_: bool = False
+                                        ) -> Tuple[str, str, List]:
         """
         Download historic ohlcv
+        :param is_new_pair: used by binance subclass to allow "fast" new pair downloading
         """
 
         one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe)
@@ -1222,22 +1256,25 @@ class Exchange:
             pair, timeframe, since) for since in
             range(since_ms, arrow.utcnow().int_timestamp * 1000, one_call)]
 
-        results = await asyncio.gather(*input_coroutines, return_exceptions=True)
-
-        # Combine gathered results
         data: List = []
-        for res in results:
-            if isinstance(res, Exception):
-                logger.warning("Async code raised an exception: %s", res.__class__.__name__)
-                continue
-            # Deconstruct tuple if it's not an exception
-            p, _, new_data = res
-            if p == pair:
-                data.extend(new_data)
+        # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
+        for input_coro in chunks(input_coroutines, 100):
+
+            results = await asyncio.gather(*input_coro, return_exceptions=True)
+            for res in results:
+                if isinstance(res, Exception):
+                    logger.warning("Async code raised an exception: %s", res.__class__.__name__)
+                    if raise_:
+                        raise
+                    continue
+                else:
+                    # Deconstruct tuple if it's not an exception
+                    p, _, new_data = res
+                    if p == pair:
+                        data.extend(new_data)
         # Sort data again after extending the result - above calls return in "async order"
         data = sorted(data, key=lambda x: x[0])
-        logger.info("Downloaded data for %s with length %s.", pair, len(data))
-        return data
+        return pair, timeframe, data
 
     def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
                              since_ms: Optional[int] = None, cache: bool = True
@@ -1257,10 +1294,22 @@ class Exchange:
         cached_pairs = []
         # Gather coroutines to run
         for pair, timeframe in set(pair_list):
-            if (((pair, timeframe) not in self._klines)
+            if ((pair, timeframe) not in self._klines
                     or self._now_is_time_to_refresh(pair, timeframe)):
-                input_coroutines.append(self._async_get_candle_history(pair, timeframe,
-                                                                       since_ms=since_ms))
+                if not since_ms and self.required_candle_call_count > 1:
+                    # Multiple calls for one pair - to get more history
+                    one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe)
+                    move_to = one_call * self.required_candle_call_count
+                    now = timeframe_to_next_date(timeframe)
+                    since_ms = int((now - timedelta(seconds=move_to // 1000)).timestamp() * 1000)
+
+                if since_ms:
+                    input_coroutines.append(self._async_get_historic_ohlcv(
+                        pair, timeframe, since_ms=since_ms, raise_=True))
+                else:
+                    # One call ... "regular" refresh
+                    input_coroutines.append(self._async_get_candle_history(
+                        pair, timeframe, since_ms=since_ms))
             else:
                 logger.debug(
                     "Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
@@ -1515,7 +1564,7 @@ def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = Non
 
 
 def is_exchange_officially_supported(exchange_name: str) -> bool:
-    return exchange_name in ['bittrex', 'binance', 'kraken']
+    return exchange_name in ['bittrex', 'binance', 'kraken', 'ftx', 'gateio', 'okex']
 
 
 def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:

freqtrade/exchange/gateio.py

@@ -2,6 +2,7 @@
 import logging
 from typing import Dict
 
+from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange
 
 
@@ -21,3 +22,12 @@ class Gateio(Exchange):
     _ft_has: Dict = {
         "ohlcv_candle_limit": 1000,
     }
+
+    _headers = {'X-Gate-Channel-Id': 'freqtrade'}
+
+    def validate_ordertypes(self, order_types: Dict) -> None:
+        super().validate_ordertypes(order_types)
+
+        if any(v == 'market' for k, v in order_types.items()):
+            raise OperationalException(
+                    f'Exchange {self.name} does not support market orders.')

freqtrade/exchange/kucoin.py

@@ -1,4 +1,4 @@
-""" Kucoin exchange subclass """
+"""Kucoin exchange subclass."""
 import logging
 from typing import Dict
 
@@ -9,9 +9,9 @@ logger = logging.getLogger(__name__)
 
 
 class Kucoin(Exchange):
-    """
-    Kucoin exchange class. Contains adjustments needed for Freqtrade to work
-    with this exchange.
+    """Kucoin exchange class.
+
+    Contains adjustments needed for Freqtrade to work with this exchange.
 
     Please note that this exchange is not included in the list of exchanges
     officially supported by the Freqtrade development team. So some features
@@ -21,4 +21,6 @@ class Kucoin(Exchange):
     _ft_has: Dict = {
         "l2_limit_range": [20, 100],
         "l2_limit_range_required": False,
+        "order_time_in_force": ['gtc', 'fok', 'ioc'],
+        "time_in_force_parameter": "timeInForce",
     }

freqtrade/exchange/okex.py

@@ -0,0 +1,18 @@
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Okex(Exchange):
+    """Okex exchange class.
+
+    Contains adjustments needed for Freqtrade to work with this exchange.
+    """
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 100,
+    }

freqtrade/freqtradebot.py

@@ -83,10 +83,10 @@ class FreqtradeBot(LoggingMixin):
 
         self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
 
-        # Attach Dataprovider to Strategy baseclass
-        IStrategy.dp = self.dataprovider
-        # Attach Wallets to Strategy baseclass
-        IStrategy.wallets = self.wallets
+        # Attach Dataprovider to strategy instance
+        self.strategy.dp = self.dataprovider
+        # Attach Wallets to strategy instance
+        self.strategy.wallets = self.wallets
 
         # Initializing Edge only if enabled
         self.edge = Edge(self.config, self.exchange, self.strategy) if \
@@ -99,7 +99,7 @@ class FreqtradeBot(LoggingMixin):
         self.state = State[initial_state.upper()] if initial_state else State.STOPPED
 
         # Protect sell-logic from forcesell and vice versa
-        self._sell_lock = Lock()
+        self._exit_lock = Lock()
         LoggingMixin.__init__(self, logger, timeframe_to_seconds(self.strategy.timeframe))
 
     def notify_status(self, msg: str) -> None:
@@ -139,7 +139,7 @@ class FreqtradeBot(LoggingMixin):
 
         # Only update open orders on startup
         # This will update the database after the initial migration
-        self.update_open_orders()
+        self.startup_update_open_orders()
 
     def process(self) -> None:
         """
@@ -160,20 +160,20 @@ class FreqtradeBot(LoggingMixin):
 
         # Refreshing candles
         self.dataprovider.refresh(self.pairlists.create_pair_list(self.active_pair_whitelist),
-                                  self.strategy.informative_pairs())
+                                  self.strategy.gather_informative_pairs())
 
         strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
 
         self.strategy.analyze(self.active_pair_whitelist)
 
-        with self._sell_lock:
+        with self._exit_lock:
             # Check and handle any timed out open orders
             self.check_handle_timedout()
 
         # Protect from collisions with forcesell.
         # Without this, freqtrade my try to recreate stoploss_on_exchange orders
         # while selling is in process, since telegram messages arrive in an different thread.
-        with self._sell_lock:
+        with self._exit_lock:
             trades = Trade.get_open_trades()
             # First process current opened trades (positions)
             self.exit_positions(trades)
@@ -193,19 +193,20 @@ class FreqtradeBot(LoggingMixin):
 
     def check_for_open_trades(self):
         """
-        Notify the user when the bot is stopped
+        Notify the user when the bot is stopped (not reloaded)
         and there are still open trades active.
         """
         open_trades = Trade.get_trades([Trade.is_open.is_(True)]).all()
 
-        if len(open_trades) != 0:
+        if len(open_trades) != 0 and self.state != State.RELOAD_CONFIG:
             msg = {
                 'type': RPCMessageType.WARNING,
-                'status':  f"{len(open_trades)} open trades active.\n\n"
-                           f"Handle these trades manually on {self.exchange.name}, "
-                           f"or '/start' the bot again and use '/stopbuy' "
-                           f"to handle open trades gracefully. \n"
-                           f"{'Trades are simulated.' if self.config['dry_run'] else ''}",
+                'status':
+                    f"{len(open_trades)} open trades active.\n\n"
+                    f"Handle these trades manually on {self.exchange.name}, "
+                    f"or '/start' the bot again and use '/stopbuy' "
+                    f"to handle open trades gracefully. \n"
+                    f"{'Note: Trades are simulated (dry run).' if self.config['dry_run'] else ''}",
             }
             self.rpc.send_msg(msg)
 
@@ -237,7 +238,7 @@ class FreqtradeBot(LoggingMixin):
         open_trades = len(Trade.get_open_trades())
         return max(0, self.config['max_open_trades'] - open_trades)
 
-    def update_open_orders(self):
+    def startup_update_open_orders(self):
         """
         Updates open orders based on order list kept in the database.
         Mainly updates the state of orders - but may also close trades
@@ -296,9 +297,9 @@ class FreqtradeBot(LoggingMixin):
         if sell_order:
             self.refind_lost_order(trade)
         else:
-            self.reupdate_buy_order_fees(trade)
+            self.reupdate_enter_order_fees(trade)
 
-    def reupdate_buy_order_fees(self, trade: Trade):
+    def reupdate_enter_order_fees(self, trade: Trade):
         """
         Get buy order from database, and try to reupdate.
         Handles trades where the initial fee-update did not work.
@@ -420,7 +421,7 @@ class FreqtradeBot(LoggingMixin):
             return False
 
         # running get_signal on historical data fetched
-        (buy, sell, buy_tag) = self.strategy.get_signal(
+        (buy, sell, buy_tag, _) = self.strategy.get_signal(
             pair,
             self.strategy.timeframe,
             analyzed_df
@@ -476,21 +477,21 @@ class FreqtradeBot(LoggingMixin):
         time_in_force = self.strategy.order_time_in_force['buy']
 
         if price:
-            buy_limit_requested = price
+            enter_limit_requested = price
         else:
             # Calculate price
-            proposed_buy_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
+            proposed_enter_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
             custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
-                                                       default_retval=proposed_buy_rate)(
+                                                       default_retval=proposed_enter_rate)(
                 pair=pair, current_time=datetime.now(timezone.utc),
-                proposed_rate=proposed_buy_rate)
+                proposed_rate=proposed_enter_rate)
 
-            buy_limit_requested = self.get_valid_price(custom_entry_price, proposed_buy_rate)
+            enter_limit_requested = self.get_valid_price(custom_entry_price, proposed_enter_rate)
 
-        if not buy_limit_requested:
+        if not enter_limit_requested:
             raise PricingError('Could not determine buy price.')
 
-        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, buy_limit_requested,
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, enter_limit_requested,
                                                                    self.strategy.stoploss)
 
         if not self.edge:
@@ -498,9 +499,9 @@ class FreqtradeBot(LoggingMixin):
             stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
                                                  default_retval=stake_amount)(
                 pair=pair, current_time=datetime.now(timezone.utc),
-                current_rate=buy_limit_requested, proposed_stake=stake_amount,
+                current_rate=enter_limit_requested, proposed_stake=stake_amount,
                 min_stake=min_stake_amount, max_stake=max_stake_amount)
-        stake_amount = self.wallets._validate_stake_amount(pair, stake_amount, min_stake_amount)
+        stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
 
         if not stake_amount:
             return False
@@ -508,27 +509,27 @@ class FreqtradeBot(LoggingMixin):
         logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
                     f"{stake_amount} ...")
 
-        amount = stake_amount / buy_limit_requested
+        amount = stake_amount / enter_limit_requested
         order_type = self.strategy.order_types['buy']
         if forcebuy:
             # Forcebuy can define a different ordertype
             order_type = self.strategy.order_types.get('forcebuy', order_type)
 
         if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
-                pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested,
+                pair=pair, order_type=order_type, amount=amount, rate=enter_limit_requested,
                 time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
             logger.info(f"User requested abortion of buying {pair}")
             return False
         amount = self.exchange.amount_to_precision(pair, amount)
         order = self.exchange.create_order(pair=pair, ordertype=order_type, side="buy",
-                                           amount=amount, rate=buy_limit_requested,
+                                           amount=amount, rate=enter_limit_requested,
                                            time_in_force=time_in_force)
         order_obj = Order.parse_from_ccxt_object(order, pair, 'buy')
         order_id = order['id']
         order_status = order.get('status', None)
 
         # we assume the order is executed at the price requested
-        buy_limit_filled_price = buy_limit_requested
+        enter_limit_filled_price = enter_limit_requested
         amount_requested = amount
 
         if order_status == 'expired' or order_status == 'rejected':
@@ -551,13 +552,13 @@ class FreqtradeBot(LoggingMixin):
                                )
                 stake_amount = order['cost']
                 amount = safe_value_fallback(order, 'filled', 'amount')
-                buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
+                enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
 
         # in case of FOK the order may be filled immediately and fully
         elif order_status == 'closed':
             stake_amount = order['cost']
             amount = safe_value_fallback(order, 'filled', 'amount')
-            buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
+            enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
 
         # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
         fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
@@ -569,8 +570,8 @@ class FreqtradeBot(LoggingMixin):
             amount_requested=amount_requested,
             fee_open=fee,
             fee_close=fee,
-            open_rate=buy_limit_filled_price,
-            open_rate_requested=buy_limit_requested,
+            open_rate=enter_limit_filled_price,
+            open_rate_requested=enter_limit_requested,
             open_date=datetime.utcnow(),
             exchange=self.exchange.id,
             open_order_id=order_id,
@@ -590,11 +591,11 @@ class FreqtradeBot(LoggingMixin):
         # Updating wallets
         self.wallets.update()
 
-        self._notify_buy(trade, order_type)
+        self._notify_enter(trade, order_type)
 
         return True
 
-    def _notify_buy(self, trade: Trade, order_type: str) -> None:
+    def _notify_enter(self, trade: Trade, order_type: str) -> None:
         """
         Sends rpc notification when a buy occurred.
         """
@@ -617,7 +618,7 @@ class FreqtradeBot(LoggingMixin):
         # Send the message
         self.rpc.send_msg(msg)
 
-    def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
+    def _notify_enter_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
         """
         Sends rpc notification when a buy cancel occurred.
         """
@@ -643,7 +644,7 @@ class FreqtradeBot(LoggingMixin):
         # Send the message
         self.rpc.send_msg(msg)
 
-    def _notify_buy_fill(self, trade: Trade) -> None:
+    def _notify_enter_fill(self, trade: Trade) -> None:
         msg = {
             'trade_id': trade.id,
             'type': RPCMessageType.BUY_FILL,
@@ -700,13 +701,14 @@ class FreqtradeBot(LoggingMixin):
         logger.debug('Handling %s ...', trade)
 
         (buy, sell) = (False, False)
+        exit_tag = None
 
         if (self.config.get('use_sell_signal', True) or
                 self.config.get('ignore_roi_if_buy_signal', False)):
             analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
                                                                       self.strategy.timeframe)
 
-            (buy, sell, _) = self.strategy.get_signal(
+            (buy, sell, _, exit_tag) = self.strategy.get_signal(
                 trade.pair,
                 self.strategy.timeframe,
                 analyzed_df
@@ -714,7 +716,7 @@ class FreqtradeBot(LoggingMixin):
 
         logger.debug('checking sell')
         sell_rate = self.exchange.get_rate(trade.pair, refresh=True, side="sell")
-        if self._check_and_execute_sell(trade, sell_rate, buy, sell):
+        if self._check_and_execute_exit(trade, sell_rate, buy, sell, exit_tag):
             return True
 
         logger.debug('Found no sell signal for %s.', trade)
@@ -744,7 +746,7 @@ class FreqtradeBot(LoggingMixin):
         except InvalidOrderException as e:
             trade.stoploss_order_id = None
             logger.error(f'Unable to place a stoploss order on exchange. {e}')
-            logger.warning('Selling the trade forcefully')
+            logger.warning('Exiting the trade forcefully')
             self.execute_trade_exit(trade, trade.stop_loss, sell_reason=SellCheckTuple(
                 sell_type=SellType.EMERGENCY_SELL))
 
@@ -782,7 +784,7 @@ class FreqtradeBot(LoggingMixin):
             # Lock pair for one candle to prevent immediate rebuys
             self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
                                     reason='Auto lock')
-            self._notify_sell(trade, "stoploss")
+            self._notify_exit(trade, "stoploss")
             return True
 
         if trade.open_order_id or not trade.is_open:
@@ -851,19 +853,22 @@ class FreqtradeBot(LoggingMixin):
                     logger.warning(f"Could not create trailing stoploss order "
                                    f"for pair {trade.pair}.")
 
-    def _check_and_execute_sell(self, trade: Trade, sell_rate: float,
-                                buy: bool, sell: bool) -> bool:
+    def _check_and_execute_exit(self, trade: Trade, exit_rate: float,
+                                buy: bool, sell: bool, exit_tag: Optional[str]) -> bool:
         """
-        Check and execute sell
+        Check and execute exit
         """
+
         should_sell = self.strategy.should_sell(
-            trade, sell_rate, datetime.now(timezone.utc), buy, sell,
+            trade, exit_rate, datetime.now(timezone.utc), buy, sell,
             force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
         )
 
         if should_sell.sell_flag:
-            logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
-            self.execute_trade_exit(trade, sell_rate, should_sell)
+            logger.info(
+                f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}. '
+                f'Tag: {exit_tag if exit_tag is not None else "None"}')
+            self.execute_trade_exit(trade, exit_rate, should_sell, exit_tag)
             return True
         return False
 
@@ -906,7 +911,7 @@ class FreqtradeBot(LoggingMixin):
                                              default_retval=False)(pair=trade.pair,
                                                                    trade=trade,
                                                                    order=order))):
-                self.handle_cancel_buy(trade, order, constants.CANCEL_REASON['TIMEOUT'])
+                self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])
 
             elif (order['side'] == 'sell' and (order['status'] == 'open' or fully_cancelled) and (
                   fully_cancelled
@@ -915,7 +920,14 @@ class FreqtradeBot(LoggingMixin):
                                            default_retval=False)(pair=trade.pair,
                                                                  trade=trade,
                                                                  order=order))):
-                self.handle_cancel_sell(trade, order, constants.CANCEL_REASON['TIMEOUT'])
+                self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['TIMEOUT'])
+                canceled_count = trade.get_exit_order_count()
+                max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
+                if max_timeouts > 0 and canceled_count >= max_timeouts:
+                    logger.warning(f'Emergencyselling trade {trade}, as the sell order '
+                                   f'timed out {max_timeouts} times.')
+                    self.execute_trade_exit(trade, order.get('price'), sell_reason=SellCheckTuple(
+                        sell_type=SellType.EMERGENCY_SELL))
 
     def cancel_all_open_orders(self) -> None:
         """
@@ -931,13 +943,13 @@ class FreqtradeBot(LoggingMixin):
                 continue
 
             if order['side'] == 'buy':
-                self.handle_cancel_buy(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
+                self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
 
             elif order['side'] == 'sell':
-                self.handle_cancel_sell(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
+                self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
         Trade.commit()
 
-    def handle_cancel_buy(self, trade: Trade, order: Dict, reason: str) -> bool:
+    def handle_cancel_enter(self, trade: Trade, order: Dict, reason: str) -> bool:
         """
         Buy cancel - cancel order
         :return: True if order was fully cancelled
@@ -994,11 +1006,11 @@ class FreqtradeBot(LoggingMixin):
             reason += f", {constants.CANCEL_REASON['PARTIALLY_FILLED']}"
 
         self.wallets.update()
-        self._notify_buy_cancel(trade, order_type=self.strategy.order_types['buy'],
-                                reason=reason)
+        self._notify_enter_cancel(trade, order_type=self.strategy.order_types['buy'],
+                                  reason=reason)
         return was_trade_fully_canceled
 
-    def handle_cancel_sell(self, trade: Trade, order: Dict, reason: str) -> str:
+    def handle_cancel_exit(self, trade: Trade, order: Dict, reason: str) -> str:
         """
         Sell cancel - cancel order and update trade
         :return: Reason for cancel
@@ -1032,14 +1044,14 @@ class FreqtradeBot(LoggingMixin):
             reason = constants.CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
 
         self.wallets.update()
-        self._notify_sell_cancel(
+        self._notify_exit_cancel(
             trade,
             order_type=self.strategy.order_types['sell'],
             reason=reason
         )
         return reason
 
-    def _safe_sell_amount(self, pair: str, amount: float) -> float:
+    def _safe_exit_amount(self, pair: str, amount: float) -> float:
         """
         Get sellable amount.
         Should be trade.amount - but will fall back to the available amount if necessary.
@@ -1064,7 +1076,12 @@ class FreqtradeBot(LoggingMixin):
             raise DependencyException(
                 f"Not enough amount to sell. Trade-amount: {amount}, Wallet: {wallet_amount}")
 
-    def execute_trade_exit(self, trade: Trade, limit: float, sell_reason: SellCheckTuple) -> bool:
+    def execute_trade_exit(
+            self,
+            trade: Trade,
+            limit: float,
+            sell_reason: SellCheckTuple,
+            exit_tag: Optional[str] = None) -> bool:
         """
         Executes a trade exit for the given trade and limit
         :param trade: Trade instance
@@ -1111,7 +1128,7 @@ class FreqtradeBot(LoggingMixin):
             # but we allow this value to be changed)
             order_type = self.strategy.order_types.get("forcesell", order_type)
 
-        amount = self._safe_sell_amount(trade.pair, trade.amount)
+        amount = self._safe_exit_amount(trade.pair, trade.amount)
         time_in_force = self.strategy.order_time_in_force['sell']
 
         if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
@@ -1140,7 +1157,7 @@ class FreqtradeBot(LoggingMixin):
         trade.open_order_id = order['id']
         trade.sell_order_status = ''
         trade.close_rate_requested = limit
-        trade.sell_reason = sell_reason.sell_reason
+        trade.sell_reason = exit_tag or sell_reason.sell_reason
         # In case of market sell orders the order can be closed immediately
         if order.get('status', 'unknown') in ('closed', 'expired'):
             self.update_trade_state(trade, trade.open_order_id, order)
@@ -1150,11 +1167,11 @@ class FreqtradeBot(LoggingMixin):
         self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
                                 reason='Auto lock')
 
-        self._notify_sell(trade, order_type)
+        self._notify_exit(trade, order_type)
 
         return True
 
-    def _notify_sell(self, trade: Trade, order_type: str, fill: bool = False) -> None:
+    def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False) -> None:
         """
         Sends rpc notification when a sell occurred.
         """
@@ -1181,6 +1198,7 @@ class FreqtradeBot(LoggingMixin):
             'current_rate': current_rate,
             'profit_amount': profit_trade,
             'profit_ratio': profit_ratio,
+            'buy_tag': trade.buy_tag,
             'sell_reason': trade.sell_reason,
             'open_date': trade.open_date,
             'close_date': trade.close_date or datetime.utcnow(),
@@ -1196,7 +1214,7 @@ class FreqtradeBot(LoggingMixin):
         # Send the message
         self.rpc.send_msg(msg)
 
-    def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
+    def _notify_exit_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
         """
         Sends rpc notification when a sell cancel occurred.
         """
@@ -1217,16 +1235,17 @@ class FreqtradeBot(LoggingMixin):
             'exchange': trade.exchange.capitalize(),
             'pair': trade.pair,
             'gain': gain,
-            'limit': profit_rate,
+            'limit': profit_rate or 0,
             'order_type': order_type,
             'amount': trade.amount,
             'open_rate': trade.open_rate,
             'current_rate': current_rate,
             'profit_amount': profit_trade,
             'profit_ratio': profit_ratio,
+            'buy_tag': trade.buy_tag,
             'sell_reason': trade.sell_reason,
             'open_date': trade.open_date,
-            'close_date': trade.close_date,
+            'close_date': trade.close_date or datetime.now(timezone.utc),
             'stake_currency': self.config['stake_currency'],
             'fiat_currency': self.config.get('fiat_display_currency', None),
             'reason': reason,
@@ -1270,6 +1289,11 @@ class FreqtradeBot(LoggingMixin):
 
         trade.update_order(order)
 
+        if self.exchange.check_order_canceled_empty(order):
+            # Trade has been cancelled on exchange
+            # Handling of this will happen in check_handle_timedout.
+            return True
+
         # Try update amount (binance-fix)
         try:
             new_amount = self.get_real_amount(trade, order)
@@ -1281,26 +1305,34 @@ class FreqtradeBot(LoggingMixin):
         except DependencyException as exception:
             logger.warning("Could not update trade amount: %s", exception)
 
-        if self.exchange.check_order_canceled_empty(order):
-            # Trade has been cancelled on exchange
-            # Handling of this will happen in check_handle_timeout.
-            return True
         trade.update(order)
         Trade.commit()
 
         # Updating wallets when order is closed
         if not trade.is_open:
             if not stoploss_order and not trade.open_order_id:
-                self._notify_sell(trade, '', True)
-            self.protections.stop_per_pair(trade.pair)
-            self.protections.global_stop()
+                self._notify_exit(trade, '', True)
+            self.handle_protections(trade.pair)
             self.wallets.update()
         elif not trade.open_order_id:
             # Buy fill
-            self._notify_buy_fill(trade)
+            self._notify_enter_fill(trade)
 
         return False
 
+    def handle_protections(self, pair: str) -> None:
+        prot_trig = self.protections.stop_per_pair(pair)
+        if prot_trig:
+            msg = {'type': RPCMessageType.PROTECTION_TRIGGER, }
+            msg.update(prot_trig.to_json())
+            self.rpc.send_msg(msg)
+
+        prot_trig_glb = self.protections.global_stop()
+        if prot_trig_glb:
+            msg = {'type': RPCMessageType.PROTECTION_TRIGGER_GLOBAL, }
+            msg.update(prot_trig_glb.to_json())
+            self.rpc.send_msg(msg)
+
     def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
                               amount: float, fee_abs: float) -> float:
         """
@@ -1349,14 +1381,17 @@ class FreqtradeBot(LoggingMixin):
                     return self.apply_fee_conditional(trade, trade_base_currency,
                                                       amount=order_amount, fee_abs=fee_cost)
                 return order_amount
-        return self.fee_detection_from_trades(trade, order, order_amount)
+        return self.fee_detection_from_trades(trade, order, order_amount, order.get('trades', []))
 
-    def fee_detection_from_trades(self, trade: Trade, order: Dict, order_amount: float) -> float:
+    def fee_detection_from_trades(self, trade: Trade, order: Dict, order_amount: float,
+                                  trades: List) -> float:
         """
-        fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
+        fee-detection fallback to Trades.
+        Either uses provided trades list or the result of fetch_my_trades to get correct fee.
         """
-        trades = self.exchange.get_trades_for_order(self.exchange.get_order_id_conditional(order),
-                                                    trade.pair, trade.open_date)
+        if not trades:
+            trades = self.exchange.get_trades_for_order(
+                self.exchange.get_order_id_conditional(order), trade.pair, trade.open_date)
 
         if len(trades) == 0:
             logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)

freqtrade/loggers.py

@@ -87,7 +87,7 @@ def setup_logging(config: Dict[str, Any]) -> None:
             # syslog config. The messages should be equal for this.
             handler_sl.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
             logging.root.addHandler(handler_sl)
-        elif s[0] == 'journald':
+        elif s[0] == 'journald':  # pragma: no cover
             try:
                 from systemd.journal import JournaldLogHandler
             except ImportError:

freqtrade/main.py

@@ -9,7 +9,7 @@ from typing import Any, List
 
 
 # check min. python version
-if sys.version_info < (3, 7):
+if sys.version_info < (3, 7):  # pragma: no cover
     sys.exit("Freqtrade requires Python version >= 3.7")
 
 from freqtrade.commands import Arguments
@@ -46,7 +46,7 @@ def main(sysargv: List[str] = None) -> None:
                 "`freqtrade --help` or `freqtrade <command> --help`."
             )
 
-    except SystemExit as e:
+    except SystemExit as e:  # pragma: no cover
         return_code = e
     except KeyboardInterrupt:
         logger.info('SIGINT received, aborting ...')
@@ -60,5 +60,5 @@ def main(sysargv: List[str] = None) -> None:
         sys.exit(return_code)
 
 
-if __name__ == '__main__':
+if __name__ == '__main__':  # pragma: no cover
     main()

freqtrade/optimize/backtesting.py

@@ -11,7 +11,7 @@ from typing import Any, Dict, List, Optional, Tuple
 
 from pandas import DataFrame
 
-from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
+from freqtrade.configuration import TimeRange, validate_config_consistency
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.data import history
 from freqtrade.data.btanalysis import trade_list_to_dataframe
@@ -44,6 +44,7 @@ SELL_IDX = 4
 LOW_IDX = 5
 HIGH_IDX = 6
 BUY_TAG_IDX = 7
+EXIT_TAG_IDX = 8
 
 
 class Backtesting:
@@ -61,13 +62,12 @@ class Backtesting:
         self.config = config
         self.results: Optional[Dict[str, Any]] = None
 
-        # Reset keys for backtesting
-        remove_credentials(self.config)
+        config['dry_run'] = True
         self.strategylist: List[IStrategy] = []
         self.all_results: Dict[str, Dict] = {}
 
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
-        self.dataprovider = DataProvider(self.config, None)
+        self.dataprovider = DataProvider(self.config, self.exchange)
 
         if self.config.get('strategy_list', None):
             for strat in list(self.config['strategy_list']):
@@ -86,10 +86,11 @@ class Backtesting:
                                        "configuration or as cli argument `--timeframe 5m`")
         self.timeframe = str(self.config.get('timeframe'))
         self.timeframe_min = timeframe_to_minutes(self.timeframe)
-
+        self.init_backtest_detail()
         self.pairlists = PairListManager(self.exchange, self.config)
         if 'VolumePairList' in self.pairlists.name_list:
-            raise OperationalException("VolumePairList not allowed for backtesting.")
+            raise OperationalException("VolumePairList not allowed for backtesting. "
+                                       "Please use StaticPairlist instead.")
         if 'PerformanceFilter' in self.pairlists.name_list:
             raise OperationalException("PerformanceFilter not allowed for backtesting.")
 
@@ -109,14 +110,6 @@ class Backtesting:
         else:
             self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
 
-        Trade.use_db = False
-        Trade.reset_trades()
-        PairLocks.timeframe = self.config['timeframe']
-        PairLocks.use_db = False
-        PairLocks.reset_locks()
-
-        self.wallets = Wallets(self.config, self.exchange, log=False)
-
         self.timerange = TimeRange.parse_timerange(
             None if self.config.get('timerange') is None else str(self.config.get('timerange')))
 
@@ -125,9 +118,7 @@ class Backtesting:
         # Add maximum startup candle count to configuration for informative pairs support
         self.config['startup_candle_count'] = self.required_startup
         self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
-
-        self.progress = BTProgress()
-        self.abort = False
+        self.init_backtest()
 
     def __del__(self):
         self.cleanup()
@@ -137,6 +128,28 @@ class Backtesting:
         PairLocks.use_db = True
         Trade.use_db = True
 
+    def init_backtest_detail(self):
+        # Load detail timeframe if specified
+        self.timeframe_detail = str(self.config.get('timeframe_detail', ''))
+        if self.timeframe_detail:
+            self.timeframe_detail_min = timeframe_to_minutes(self.timeframe_detail)
+            if self.timeframe_min <= self.timeframe_detail_min:
+                raise OperationalException(
+                    "Detail timeframe must be smaller than strategy timeframe.")
+
+        else:
+            self.timeframe_detail_min = 0
+        self.detail_data: Dict[str, DataFrame] = {}
+
+    def init_backtest(self):
+
+        self.prepare_backtest(False)
+
+        self.wallets = Wallets(self.config, self.exchange, log=False)
+
+        self.progress = BTProgress()
+        self.abort = False
+
     def _set_strategy(self, strategy: IStrategy):
         """
         Load strategy into backtesting
@@ -144,7 +157,7 @@ class Backtesting:
         self.strategy: IStrategy = strategy
         strategy.dp = self.dataprovider
         # Attach Wallets to Strategy baseclass
-        IStrategy.wallets = self.wallets
+        strategy.wallets = self.wallets
         # Set stoploss_on_exchange to false for backtesting,
         # since a "perfect" stoploss-sell is assumed anyway
         # And the regular "stoploss" function would not apply to that case
@@ -188,6 +201,23 @@ class Backtesting:
         self.progress.set_new_value(1)
         return data, self.timerange
 
+    def load_bt_data_detail(self) -> None:
+        """
+        Loads backtest detail data (smaller timeframe) if necessary.
+        """
+        if self.timeframe_detail:
+            self.detail_data = history.load_data(
+                datadir=self.config['datadir'],
+                pairs=self.pairlists.whitelist,
+                timeframe=self.timeframe_detail,
+                timerange=self.timerange,
+                startup_candles=0,
+                fail_without_data=True,
+                data_format=self.config.get('dataformat_ohlcv', 'json'),
+            )
+        else:
+            self.detail_data = {}
+
     def prepare_backtest(self, enable_protections):
         """
         Backtesting setup method - called once for every call to "backtest()".
@@ -199,7 +229,8 @@ class Backtesting:
         Trade.reset_trades()
         self.rejected_trades = 0
         self.dataprovider.clear_cache()
-        self._load_protections(self.strategy)
+        if enable_protections:
+            self._load_protections(self.strategy)
 
     def check_abort(self):
         """
@@ -218,7 +249,7 @@ class Backtesting:
         """
         # Every change to this headers list must evaluate further usages of the resulting tuple
         # and eventually change the constants for indexes at the top
-        headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag']
+        headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
         data: Dict = {}
         self.progress.init_step(BacktestState.CONVERT, len(processed))
 
@@ -230,6 +261,7 @@ class Backtesting:
                 pair_data.loc[:, 'buy'] = 0  # cleanup if buy_signal is exist
                 pair_data.loc[:, 'sell'] = 0  # cleanup if sell_signal is exist
                 pair_data.loc[:, 'buy_tag'] = None  # cleanup if buy_tag is exist
+                pair_data.loc[:, 'exit_tag'] = None  # cleanup if exit_tag is exist
 
             df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
@@ -241,6 +273,7 @@ class Backtesting:
             df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
             df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
             df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
+            df_analyzed.loc[:, 'exit_tag'] = df_analyzed.loc[:, 'exit_tag'].shift(1)
 
             # Update dataprovider cache
             self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
@@ -283,7 +316,9 @@ class Backtesting:
                     # Worst case: price ticks tiny bit above open and dives down.
                     stop_rate = sell_row[OPEN_IDX] * (1 - abs(trade.stop_loss_pct))
                     assert stop_rate < sell_row[HIGH_IDX]
-                return stop_rate
+                # Limit lower-end to candle low to avoid sells below the low.
+                # This still remains "worst case" - but "worst realistic case".
+                return max(sell_row[LOW_IDX], stop_rate)
 
             # Set close_rate to stoploss
             return trade.stop_loss
@@ -318,7 +353,8 @@ class Backtesting:
         else:
             return sell_row[OPEN_IDX]
 
-    def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
+    def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
+                                         sell_row: Tuple) -> Optional[LocalTrade]:
         sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
         sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX],  # type: ignore
                                          sell_candle_time, sell_row[BUY_IDX],
@@ -327,7 +363,7 @@ class Backtesting:
 
         if sell.sell_flag:
             trade.close_date = sell_candle_time
-            trade.sell_reason = sell.sell_reason
+
             trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
             closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
 
@@ -341,11 +377,48 @@ class Backtesting:
                     current_time=sell_candle_time):
                 return None
 
+            trade.sell_reason = sell.sell_reason
+
+            # Checks and adds an exit tag, after checking that the length of the
+            # sell_row has the length for an exit tag column
+            if(
+                len(sell_row) > EXIT_TAG_IDX
+                and sell_row[EXIT_TAG_IDX] is not None
+                and len(sell_row[EXIT_TAG_IDX]) > 0
+            ):
+                trade.sell_reason = sell_row[EXIT_TAG_IDX]
+
             trade.close(closerate, show_msg=False)
             return trade
 
         return None
 
+    def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
+        if self.timeframe_detail and trade.pair in self.detail_data:
+            sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
+            sell_candle_end = sell_candle_time + timedelta(minutes=self.timeframe_min)
+
+            detail_data = self.detail_data[trade.pair]
+            detail_data = detail_data.loc[
+                (detail_data['date'] >= sell_candle_time) &
+                (detail_data['date'] < sell_candle_end)
+            ].copy()
+            if len(detail_data) == 0:
+                # Fall back to "regular" data if no detail data was found for this candle
+                return self._get_sell_trade_entry_for_candle(trade, sell_row)
+            detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
+            detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
+            headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
+            for det_row in detail_data[headers].values.tolist():
+                res = self._get_sell_trade_entry_for_candle(trade, det_row)
+                if res:
+                    return res
+
+            return None
+
+        else:
+            return self._get_sell_trade_entry_for_candle(trade, sell_row)
+
     def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
         try:
             stake_amount = self.wallets.get_trade_stake_amount(pair, None)
@@ -359,7 +432,7 @@ class Backtesting:
                                              default_retval=stake_amount)(
             pair=pair, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
             proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount)
-        stake_amount = self.wallets._validate_stake_amount(pair, stake_amount, min_stake_amount)
+        stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
 
         if not stake_amount:
             return None
@@ -592,6 +665,7 @@ class Backtesting:
         data: Dict[str, Any] = {}
 
         data, timerange = self.load_bt_data()
+        self.load_bt_data_detail()
         logger.info("Dataload complete. Calculating indicators")
 
         for strat in self.strategylist:

freqtrade/optimize/edge_cli.py

@@ -7,7 +7,8 @@ import logging
 from typing import Any, Dict
 
 from freqtrade import constants
-from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
+from freqtrade.configuration import TimeRange, validate_config_consistency
+from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
 from freqtrade.optimize.optimize_reports import generate_edge_table
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@@ -28,11 +29,12 @@ class EdgeCli:
     def __init__(self, config: Dict[str, Any]) -> None:
         self.config = config
 
-        # Reset keys for edge
-        remove_credentials(self.config)
+        # Ensure using dry-run
+        self.config['dry_run'] = True
         self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
         self.strategy = StrategyResolver.load_strategy(self.config)
+        self.strategy.dp = DataProvider(config, None)
 
         validate_config_consistency(self.config)
 

freqtrade/optimize/hyperopt.py

@@ -22,6 +22,7 @@ from pandas import DataFrame
 from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
 from freqtrade.data.converter import trim_dataframes
 from freqtrade.data.history import get_timerange
+from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
@@ -30,7 +31,7 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F401
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F401
 from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
 from freqtrade.optimize.optimize_reports import generate_strategy_stats
-from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
+from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
 
 
 # Suppress scikit-learn FutureWarnings from skopt
@@ -78,10 +79,10 @@ class Hyperopt:
 
         if not self.config.get('hyperopt'):
             self.custom_hyperopt = HyperOptAuto(self.config)
-            self.auto_hyperopt = True
         else:
-            self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
-            self.auto_hyperopt = False
+            raise OperationalException(
+                "Using separate Hyperopt files has been removed in 2021.9. Please convert "
+                "your existing Hyperopt file to the new Hyperoptable strategy interface")
 
         self.backtesting._set_strategy(self.backtesting.strategylist[0])
         self.custom_hyperopt.strategy = self.backtesting.strategy
@@ -103,31 +104,6 @@ class Hyperopt:
         self.num_epochs_saved = 0
         self.current_best_epoch: Optional[Dict[str, Any]] = None
 
-        if not self.auto_hyperopt:
-            # Populate "fallback" functions here
-            # (hasattr is slow so should not be run during "regular" operations)
-            if hasattr(self.custom_hyperopt, 'populate_indicators'):
-                logger.warning(
-                    "DEPRECATED: Using `populate_indicators()` in the hyperopt file is deprecated. "
-                    "Please move these methods to your strategy."
-                    )
-                self.backtesting.strategy.populate_indicators = (  # type: ignore
-                    self.custom_hyperopt.populate_indicators)  # type: ignore
-            if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
-                logger.warning(
-                    "DEPRECATED: Using `populate_buy_trend()` in the hyperopt file is deprecated. "
-                    "Please move these methods to your strategy."
-                )
-                self.backtesting.strategy.populate_buy_trend = (  # type: ignore
-                    self.custom_hyperopt.populate_buy_trend)  # type: ignore
-            if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
-                logger.warning(
-                    "DEPRECATED: Using `populate_sell_trend()` in the hyperopt file is deprecated. "
-                    "Please move these methods to your strategy."
-                )
-                self.backtesting.strategy.populate_sell_trend = (  # type: ignore
-                    self.custom_hyperopt.populate_sell_trend)  # type: ignore
-
         # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
         if self.config.get('use_max_market_positions', True):
             self.max_open_trades = self.config['max_open_trades']
@@ -256,7 +232,7 @@ class Hyperopt:
         """
         Assign the dimensions in the hyperoptimization space.
         """
-        if self.auto_hyperopt and HyperoptTools.has_space(self.config, 'protection'):
+        if HyperoptTools.has_space(self.config, 'protection'):
             # Protections can only be optimized when using the Parameter interface
             logger.debug("Hyperopt has 'protection' space")
             # Enable Protections if protection space is selected.
@@ -265,7 +241,7 @@ class Hyperopt:
 
         if HyperoptTools.has_space(self.config, 'buy'):
             logger.debug("Hyperopt has 'buy' space")
-            self.buy_space = self.custom_hyperopt.indicator_space()
+            self.buy_space = self.custom_hyperopt.buy_indicator_space()
 
         if HyperoptTools.has_space(self.config, 'sell'):
             logger.debug("Hyperopt has 'sell' space")
@@ -282,9 +258,19 @@ class Hyperopt:
         if HyperoptTools.has_space(self.config, 'trailing'):
             logger.debug("Hyperopt has 'trailing' space")
             self.trailing_space = self.custom_hyperopt.trailing_space()
+
         self.dimensions = (self.buy_space + self.sell_space + self.protection_space
                            + self.roi_space + self.stoploss_space + self.trailing_space)
 
+    def assign_params(self, params_dict: Dict, category: str) -> None:
+        """
+        Assign hyperoptable parameters
+        """
+        for attr_name, attr in self.backtesting.strategy.enumerate_parameters(category):
+            if attr.optimize:
+                # noinspection PyProtectedMember
+                attr.value = params_dict[attr_name]
+
     def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
         """
         Used Optimize function.
@@ -296,18 +282,13 @@ class Hyperopt:
 
         # Apply parameters
         if HyperoptTools.has_space(self.config, 'buy'):
-            self.backtesting.strategy.advise_buy = (  # type: ignore
-                self.custom_hyperopt.buy_strategy_generator(params_dict))
+            self.assign_params(params_dict, 'buy')
 
         if HyperoptTools.has_space(self.config, 'sell'):
-            self.backtesting.strategy.advise_sell = (  # type: ignore
-                self.custom_hyperopt.sell_strategy_generator(params_dict))
+            self.assign_params(params_dict, 'sell')
 
         if HyperoptTools.has_space(self.config, 'protection'):
-            for attr_name, attr in self.backtesting.strategy.enumerate_parameters('protection'):
-                if attr.optimize:
-                    # noinspection PyProtectedMember
-                    attr.value = params_dict[attr_name]
+            self.assign_params(params_dict, 'protection')
 
         if HyperoptTools.has_space(self.config, 'roi'):
             self.backtesting.strategy.minimal_roi = (  # type: ignore
@@ -385,10 +366,20 @@ class Hyperopt:
         }
 
     def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
+        estimator = self.custom_hyperopt.generate_estimator()
+
+        acq_optimizer = "sampling"
+        if isinstance(estimator, str):
+            if estimator not in ("GP", "RF", "ET", "GBRT"):
+                raise OperationalException(f"Estimator {estimator} not supported.")
+            else:
+                acq_optimizer = "auto"
+
+        logger.info(f"Using estimator {estimator}.")
         return Optimizer(
             dimensions,
-            base_estimator="ET",
-            acq_optimizer="auto",
+            base_estimator=estimator,
+            acq_optimizer=acq_optimizer,
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
             random_state=self.random_state,
@@ -517,11 +508,10 @@ class Hyperopt:
                     f"saved to '{self.results_file}'.")
 
         if self.current_best_epoch:
-            if self.auto_hyperopt:
-                HyperoptTools.try_export_params(
-                    self.config,
-                    self.backtesting.strategy.get_strategy_name(),
-                    self.current_best_epoch)
+            HyperoptTools.try_export_params(
+                self.config,
+                self.backtesting.strategy.get_strategy_name(),
+                self.current_best_epoch)
 
             HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
                                              self.print_json)

freqtrade/optimize/hyperopt_auto.py

@@ -3,16 +3,32 @@ HyperOptAuto class.
 This module implements a convenience auto-hyperopt class, which can be used together with strategies
  that implement IHyperStrategy interface.
 """
+import logging
 from contextlib import suppress
-from typing import Any, Callable, Dict, List
+from typing import Callable, Dict, List
 
-from pandas import DataFrame
+from freqtrade.exceptions import OperationalException
 
 
 with suppress(ImportError):
     from skopt.space import Dimension
 
-from freqtrade.optimize.hyperopt_interface import IHyperOpt
+from freqtrade.optimize.hyperopt_interface import EstimatorType, IHyperOpt
+
+
+logger = logging.getLogger(__name__)
+
+
+def _format_exception_message(space: str, ignore_missing_space: bool) -> None:
+    msg = (f"The '{space}' space is included into the hyperoptimization "
+           f"but no parameter for this space was not found in your Strategy. "
+           )
+    if ignore_missing_space:
+        logger.warning(msg + "This space will be ignored.")
+    else:
+        raise OperationalException(
+            msg + f"Please make sure to have parameters for this space enabled for optimization "
+            f"or remove the '{space}' space from hyperoptimization.")
 
 
 class HyperOptAuto(IHyperOpt):
@@ -22,26 +38,6 @@ class HyperOptAuto(IHyperOpt):
      sell_indicator_space methods, but other hyperopt methods can be overridden as well.
     """
 
-    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
-        def populate_buy_trend(dataframe: DataFrame, metadata: dict):
-            for attr_name, attr in self.strategy.enumerate_parameters('buy'):
-                if attr.optimize:
-                    # noinspection PyProtectedMember
-                    attr.value = params[attr_name]
-            return self.strategy.populate_buy_trend(dataframe, metadata)
-
-        return populate_buy_trend
-
-    def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
-        def populate_sell_trend(dataframe: DataFrame, metadata: dict):
-            for attr_name, attr in self.strategy.enumerate_parameters('sell'):
-                if attr.optimize:
-                    # noinspection PyProtectedMember
-                    attr.value = params[attr_name]
-            return self.strategy.populate_sell_trend(dataframe, metadata)
-
-        return populate_sell_trend
-
     def _get_func(self, name) -> Callable:
         """
         Return a function defined in Strategy.HyperOpt class, or one defined in super() class.
@@ -60,21 +56,25 @@ class HyperOptAuto(IHyperOpt):
             if attr.optimize:
                 yield attr.get_space(attr_name)
 
-    def _get_indicator_space(self, category, fallback_method_name):
+    def _get_indicator_space(self, category) -> List:
+        # TODO: is this necessary, or can we call "generate_space" directly?
         indicator_space = list(self._generate_indicator_space(category))
         if len(indicator_space) > 0:
             return indicator_space
         else:
-            return self._get_func(fallback_method_name)()
+            _format_exception_message(
+                category,
+                self.config.get("hyperopt_ignore_missing_space", False))
+            return []
 
-    def indicator_space(self) -> List['Dimension']:
-        return self._get_indicator_space('buy', 'indicator_space')
+    def buy_indicator_space(self) -> List['Dimension']:
+        return self._get_indicator_space('buy')
 
     def sell_indicator_space(self) -> List['Dimension']:
-        return self._get_indicator_space('sell', 'sell_indicator_space')
+        return self._get_indicator_space('sell')
 
     def protection_space(self) -> List['Dimension']:
-        return self._get_indicator_space('protection', 'protection_space')
+        return self._get_indicator_space('protection')
 
     def generate_roi_table(self, params: Dict) -> Dict[int, float]:
         return self._get_func('generate_roi_table')(params)
@@ -90,3 +90,6 @@ class HyperOptAuto(IHyperOpt):
 
     def trailing_space(self) -> List['Dimension']:
         return self._get_func('trailing_space')()
+
+    def generate_estimator(self) -> EstimatorType:
+        return self._get_func('generate_estimator')()

freqtrade/optimize/hyperopt_interface.py

@@ -5,11 +5,11 @@ This module defines the interface to apply for hyperopt
 import logging
 import math
 from abc import ABC
-from typing import Any, Callable, Dict, List
+from typing import Dict, List, Union
 
+from sklearn.base import RegressorMixin
 from skopt.space import Categorical, Dimension, Integer
 
-from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.misc import round_dict
 from freqtrade.optimize.space import SKDecimal
@@ -18,12 +18,7 @@ from freqtrade.strategy import IStrategy
 
 logger = logging.getLogger(__name__)
 
-
-def _format_exception_message(method: str, space: str) -> str:
-    return (f"The '{space}' space is included into the hyperoptimization "
-            f"but {method}() method is not found in your "
-            f"custom Hyperopt class. You should either implement this "
-            f"method or remove the '{space}' space from hyperoptimization.")
+EstimatorType = Union[RegressorMixin, str]
 
 
 class IHyperOpt(ABC):
@@ -45,36 +40,13 @@ class IHyperOpt(ABC):
         IHyperOpt.ticker_interval = str(config['timeframe'])  # DEPRECATED
         IHyperOpt.timeframe = str(config['timeframe'])
 
-    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+    def generate_estimator(self) -> EstimatorType:
         """
-        Create a buy strategy generator.
+        Return base_estimator.
+        Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
+        inheriting from RegressorMixin (from sklearn).
         """
-        raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy'))
-
-    def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
-        """
-        Create a sell strategy generator.
-        """
-        raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))
-
-    def protection_space(self) -> List[Dimension]:
-        """
-        Create a protection space.
-        Only supported by the Parameter interface.
-        """
-        raise OperationalException(_format_exception_message('indicator_space', 'protection'))
-
-    def indicator_space(self) -> List[Dimension]:
-        """
-        Create an indicator space.
-        """
-        raise OperationalException(_format_exception_message('indicator_space', 'buy'))
-
-    def sell_indicator_space(self) -> List[Dimension]:
-        """
-        Create a sell indicator space.
-        """
-        raise OperationalException(_format_exception_message('sell_indicator_space', 'sell'))
+        return 'ET'
 
     def generate_roi_table(self, params: Dict) -> Dict[int, float]:
         """

freqtrade/optimize/hyperopt_loss_calmar.py

@@ -0,0 +1,64 @@
+"""
+CalmarHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+from datetime import datetime
+from math import sqrt as msqrt
+from typing import Any, Dict
+
+from pandas import DataFrame
+
+from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class CalmarHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Calmar Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        config: Dict,
+        processed: Dict[str, DataFrame],
+        backtest_stats: Dict[str, Any],
+        *args,
+        **kwargs
+    ) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Calmar Ratio calculation.
+        """
+        total_profit = backtest_stats["profit_total"]
+        days_period = (max_date - min_date).days
+
+        # adding slippage of 0.1% per trade
+        total_profit = total_profit - 0.0005
+        expected_returns_mean = total_profit.sum() / days_period * 100
+
+        # calculate max drawdown
+        try:
+            _, _, _, high_val, low_val = calculate_max_drawdown(
+                results, value_col="profit_abs"
+            )
+            max_drawdown = (high_val - low_val) / high_val
+        except ValueError:
+            max_drawdown = 0
+
+        if max_drawdown != 0:
+            calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
+        else:
+            # Define high (negative) calmar ratio to be clear that this is NOT optimal.
+            calmar_ratio = -20.0
+
+        # print(expected_returns_mean, max_drawdown, calmar_ratio)
+        return -calmar_ratio

freqtrade/optimize/hyperopt_loss_max_drawdown.py

@@ -0,0 +1,41 @@
+"""
+MaxDrawDownHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+from datetime import datetime
+
+from pandas import DataFrame
+
+from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class MaxDrawDownHyperOptLoss(IHyperOptLoss):
+
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation optimizes for max draw down and profit
+    Less max drawdown more profit -> Lower return value
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+
+        """
+        Objective function.
+
+        Uses profit ratio weighted max_drawdown when drawdown is available.
+        Otherwise directly optimizes profit ratio.
+        """
+        total_profit = results['profit_abs'].sum()
+        try:
+            max_drawdown = calculate_max_drawdown(results, value_col='profit_abs')
+        except ValueError:
+            # No losing trade, therefore no drawdown.
+            return -total_profit
+        return -total_profit / max_drawdown[0]

freqtrade/optimize/hyperopt_tools.py

@@ -1,4 +1,3 @@
-
 import io
 import logging
 from copy import deepcopy
@@ -7,6 +6,7 @@ from pathlib import Path
 from typing import Any, Dict, Iterator, List, Optional, Tuple
 
 import numpy as np
+import pandas as pd
 import rapidjson
 import tabulate
 from colorama import Fore, Style
@@ -63,10 +63,11 @@ class HyperoptTools():
             'export_time': datetime.now(timezone.utc),
         }
         logger.info(f"Dumping parameters to {filename}")
-        rapidjson.dump(final_params, filename.open('w'), indent=2,
-                       default=hyperopt_serializer,
-                       number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
-                       )
+        with filename.open('w') as f:
+            rapidjson.dump(final_params, f, indent=2,
+                           default=hyperopt_serializer,
+                           number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
+                           )
 
     @staticmethod
     def try_export_params(config: Dict[str, Any], strategy_name: str, params: Dict):
@@ -283,10 +284,10 @@ class HyperoptTools():
         return (f"{results_metrics['total_trades']:6d} trades. "
                 f"{results_metrics['wins']}/{results_metrics['draws']}"
                 f"/{results_metrics['losses']} Wins/Draws/Losses. "
-                f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
-                f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
-                f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
-                f"({results_metrics['profit_total'] * 100: 7.2f}%). "
+                f"Avg profit {results_metrics['profit_mean']:7.2%}. "
+                f"Median profit {results_metrics['profit_median']:7.2%}. "
+                f"Total profit {results_metrics['profit_total_abs']:11.8f} {stake_currency} "
+                f"({results_metrics['profit_total']:8.2%}). "
                 f"Avg duration {results_metrics['holding_avg']} min."
                 )
 
@@ -298,8 +299,8 @@ class HyperoptTools():
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def prepare_trials_columns(trials, legacy_mode: bool, has_drawdown: bool) -> str:
-
+    def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
+                               has_drawdown: bool) -> pd.DataFrame:
         trials['Best'] = ''
 
         if 'results_metrics.winsdrawslosses' not in trials.columns:
@@ -435,8 +436,7 @@ class HyperoptTools():
         return table
 
     @staticmethod
-    def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
-                        csv_file: str) -> None:
+    def export_csv_file(config: dict, results: list, csv_file: str) -> None:
         """
         Log result to csv-file
         """

freqtrade/optimize/optimize_reports.py

@@ -4,7 +4,7 @@ from pathlib import Path
 from typing import Any, Dict, List, Union
 
 from numpy import int64
-from pandas import DataFrame
+from pandas import DataFrame, to_datetime
 from tabulate import tabulate
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
@@ -46,11 +46,11 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
             '.2f', 'd', 's', 's']
 
 
-def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
+def _get_line_header(first_column: str, stake_currency: str, direction: str = 'Buys') -> List[str]:
     """
     Generate header lines (goes in line with _generate_result_line())
     """
-    return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
+    return [first_column, direction, 'Avg Profit %', 'Cum Profit %',
             f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
             'Win  Draw  Loss  Win%']
 
@@ -127,6 +127,38 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_b
     return tabular_data
 
 
+def generate_tag_metrics(tag_type: str,
+                         starting_balance: int,
+                         results: DataFrame,
+                         skip_nan: bool = False) -> List[Dict]:
+    """
+    Generates and returns a list of metrics for the given tag trades and the results dataframe
+    :param starting_balance: Starting balance
+    :param results: Dataframe containing the backtest results
+    :param skip_nan: Print "left open" open trades
+    :return: List of Dicts containing the metrics per pair
+    """
+
+    tabular_data = []
+
+    if tag_type in results.columns:
+        for tag, count in results[tag_type].value_counts().iteritems():
+            result = results[results[tag_type] == tag]
+            if skip_nan and result['profit_abs'].isnull().all():
+                continue
+
+            tabular_data.append(_generate_result_line(result, starting_balance, tag))
+
+        # Sort by total profit %:
+        tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)
+
+        # Append Total
+        tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
+        return tabular_data
+    else:
+        return []
+
+
 def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
     """
     Generate small table outlining Backtest results
@@ -189,7 +221,6 @@ def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
 
 
 def generate_edge_table(results: dict) -> str:
-
     floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
     tabular_data = []
     headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
@@ -214,6 +245,41 @@ def generate_edge_table(results: dict) -> str:
                     floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
 
 
+def _get_resample_from_period(period: str) -> str:
+    if period == 'day':
+        return '1d'
+    if period == 'week':
+        return '1w'
+    if period == 'month':
+        return '1M'
+    raise ValueError(f"Period {period} is not supported.")
+
+
+def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dict[str, Any]]:
+    results = DataFrame.from_records(trade_list)
+    if len(results) == 0:
+        return []
+    results['close_date'] = to_datetime(results['close_date'], utc=True)
+    resample_period = _get_resample_from_period(period)
+    resampled = results.resample(resample_period, on='close_date')
+    stats = []
+    for name, day in resampled:
+        profit_abs = day['profit_abs'].sum().round(10)
+        wins = sum(day['profit_abs'] > 0)
+        draws = sum(day['profit_abs'] == 0)
+        loses = sum(day['profit_abs'] < 0)
+        stats.append(
+            {
+                'date': name.strftime('%d/%m/%Y'),
+                'profit_abs': profit_abs,
+                'wins': wins,
+                'draws': draws,
+                'loses': loses
+            }
+        )
+    return stats
+
+
 def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
     """ Generate overall trade statistics """
     if len(results) == 0:
@@ -313,6 +379,10 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
     pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
                                          starting_balance=starting_balance,
                                          results=results, skip_nan=False)
+
+    buy_tag_results = generate_tag_metrics("buy_tag", starting_balance=starting_balance,
+                                           results=results, skip_nan=False)
+
     sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
                                                    results=results)
     left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
@@ -329,15 +399,18 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
         results['open_timestamp'] = results['open_date'].view(int64) // 1e6
         results['close_timestamp'] = results['close_date'].view(int64) // 1e6
 
-    backtest_days = (max_date - min_date).days
+    backtest_days = (max_date - min_date).days or 1
     strat_stats = {
         'trades': results.to_dict(orient='records'),
         'locks': [lock.to_json() for lock in content['locks']],
         'best_pair': best_pair,
         'worst_pair': worst_pair,
         'results_per_pair': pair_results,
+        'results_per_buy_tag': buy_tag_results,
         'sell_reason_summary': sell_reason_stats,
         'left_open_trades': left_open_results,
+        # 'days_breakdown_stats': days_breakdown_stats,
+
         'total_trades': len(results),
         'total_volume': float(results['stake_amount'].sum()),
         'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
@@ -354,7 +427,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
         'backtest_run_start_ts': content['backtest_start_time'],
         'backtest_run_end_ts': content['backtest_end_time'],
 
-        'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
+        'trades_per_day': round(len(results) / backtest_days, 2),
         'market_change': market_change,
         'pairlist': list(btdata.keys()),
         'stake_amount': config['stake_amount'],
@@ -368,6 +441,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
         'max_open_trades_setting': (config['max_open_trades']
                                     if config['max_open_trades'] != float('inf') else -1),
         'timeframe': config['timeframe'],
+        'timeframe_detail': config.get('timeframe_detail', ''),
         'timerange': config.get('timerange', ''),
         'enable_protections': config.get('enable_protections', False),
         'strategy_name': strategy,
@@ -505,6 +579,59 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
     return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
 
 
+def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str:
+    """
+    Generates and returns a text table for the given backtest data and the results dataframe
+    :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
+    :param stake_currency: stake-currency - used to correctly name headers
+    :return: pretty printed table with tabulate as string
+    """
+    if(tag_type == "buy_tag"):
+        headers = _get_line_header("TAG", stake_currency)
+    else:
+        headers = _get_line_header("TAG", stake_currency, 'Sells')
+    floatfmt = _get_line_floatfmt(stake_currency)
+    output = [
+        [
+            t['key'] if t['key'] is not None and len(
+                t['key']) > 0 else "OTHER",
+            t['trades'],
+            t['profit_mean_pct'],
+            t['profit_sum_pct'],
+            t['profit_total_abs'],
+            t['profit_total_pct'],
+            t['duration_avg'],
+            _generate_wins_draws_losses(
+                t['wins'],
+                t['draws'],
+                t['losses'])] for t in tag_results]
+    # Ignore type as floatfmt does allow tuples but mypy does not know that
+    return tabulate(output, headers=headers,
+                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
+
+
+def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]],
+                                  stake_currency: str, period: str) -> str:
+    """
+    Generate small table with Backtest results by days
+    :param days_breakdown_stats: Days breakdown metrics
+    :param stake_currency: Stakecurrency used
+    :return: pretty printed table with tabulate as string
+    """
+    headers = [
+        period.capitalize(),
+        f'Tot Profit {stake_currency}',
+        'Wins',
+        'Draws',
+        'Losses',
+    ]
+    output = [[
+        d['date'], round_coin_value(d['profit_abs'], stake_currency, False),
+        d['wins'], d['draws'], d['loses'],
+    ] for d in days_breakdown_stats]
+    return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
+
+
 def text_table_strategy(strategy_results, stake_currency: str) -> str:
     """
     Generate summary table per strategy
@@ -556,19 +683,22 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                                strat_results['stake_currency'])),
             ('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
                                                   strat_results['stake_currency'])),
-            ('Total profit %', f"{round(strat_results['profit_total'] * 100, 2):}%"),
+            ('Total profit %', f"{strat_results['profit_total']:.2%}"),
+            ('Trades per day', strat_results['trades_per_day']),
+            ('Avg. daily profit %',
+             f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
             ('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
                                                    strat_results['stake_currency'])),
             ('Total trade volume', round_coin_value(strat_results['total_volume'],
                                                     strat_results['stake_currency'])),
             ('', ''),  # Empty line to improve readability
             ('Best Pair', f"{strat_results['best_pair']['key']} "
-                          f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
+                          f"{strat_results['best_pair']['profit_sum']:.2%}"),
             ('Worst Pair', f"{strat_results['worst_pair']['key']} "
-                           f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
-            ('Best trade', f"{best_trade['pair']} {round(best_trade['profit_ratio'] * 100, 2)}%"),
+                           f"{strat_results['worst_pair']['profit_sum']:.2%}"),
+            ('Best trade', f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"),
             ('Worst trade', f"{worst_trade['pair']} "
-                            f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
+                            f"{worst_trade['profit_ratio']:.2%}"),
 
             ('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
                                           strat_results['stake_currency'])),
@@ -586,7 +716,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
             ('Max balance', round_coin_value(strat_results['csum_max'],
                                              strat_results['stake_currency'])),
 
-            ('Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
+            ('Drawdown', f"{strat_results['max_drawdown']:.2%}"),
             ('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
                                           strat_results['stake_currency'])),
             ('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
@@ -595,7 +725,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                               strat_results['stake_currency'])),
             ('Drawdown Start', strat_results['drawdown_start']),
             ('Drawdown End', strat_results['drawdown_end']),
-            ('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
+            ('Market change', f"{strat_results['market_change']:.2%}"),
         ]
 
         return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
@@ -613,7 +743,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
         return message
 
 
-def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str):
+def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str,
+                         backtest_breakdown=[]):
     """
     Print results for one strategy
     """
@@ -624,6 +755,16 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
         print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
     print(table)
 
+    if results.get('results_per_buy_tag') is not None:
+        table = text_table_tags(
+            "buy_tag",
+            results['results_per_buy_tag'],
+            stake_currency=stake_currency)
+
+        if isinstance(table, str) and len(table) > 0:
+            print(' BUY TAG STATS '.center(len(table.splitlines()[0]), '='))
+        print(table)
+
     table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
                                    stake_currency=stake_currency)
     if isinstance(table, str) and len(table) > 0:
@@ -635,6 +776,15 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
         print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
     print(table)
 
+    for period in backtest_breakdown:
+        days_breakdown_stats = generate_periodic_breakdown_stats(
+            trade_list=results['trades'], period=period)
+        table = text_table_periodic_breakdown(days_breakdown_stats=days_breakdown_stats,
+                                              stake_currency=stake_currency, period=period)
+        if isinstance(table, str) and len(table) > 0:
+            print(f' {period.upper()} BREAKDOWN '.center(len(table.splitlines()[0]), '='))
+        print(table)
+
     table = text_table_add_metrics(results)
     if isinstance(table, str) and len(table) > 0:
         print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
@@ -642,6 +792,7 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
 
     if isinstance(table, str) and len(table) > 0:
         print('=' * len(table.splitlines()[0]))
+
     print()
 
 
@@ -649,7 +800,9 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
     stake_currency = config['stake_currency']
 
     for strategy, results in backtest_stats['strategy'].items():
-        show_backtest_result(strategy, results, stake_currency)
+        show_backtest_result(
+            strategy, results, stake_currency,
+            config.get('backtest_breakdown', []))
 
     if len(backtest_stats['strategy']) > 1:
         # Print Strategy summary table
@@ -661,3 +814,13 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
         print(table)
         print('=' * len(table.splitlines()[0]))
         print('\nFor more details, please look at the detail tables above')
+
+
+def show_sorted_pairlist(config: Dict, backtest_stats: Dict):
+    if config.get('backtest_show_pair_list', False):
+        for strategy, results in backtest_stats['strategy'].items():
+            print(f"Pairs for Strategy {strategy}: \n[")
+            for result in results['results_per_pair']:
+                if result["key"] != 'TOTAL':
+                    print(f'"{result["key"]}",  // {result["profit_mean"]:.2%}')
+            print("]")

freqtrade/optimize/space/decimalspace.py

@@ -7,11 +7,15 @@ class SKDecimal(Integer):
     def __init__(self, low, high, decimals=3, prior="uniform", base=10, transform=None,
                  name=None, dtype=np.int64):
         self.decimals = decimals
-        _low = int(low * pow(10, self.decimals))
-        _high = int(high * pow(10, self.decimals))
+
+        self.pow_dot_one = pow(0.1, self.decimals)
+        self.pow_ten = pow(10, self.decimals)
+
+        _low = int(low * self.pow_ten)
+        _high = int(high * self.pow_ten)
         # trunc to precision to avoid points out of space
-        self.low_orig = round(_low * pow(0.1, self.decimals), self.decimals)
-        self.high_orig = round(_high * pow(0.1, self.decimals), self.decimals)
+        self.low_orig = round(_low * self.pow_dot_one, self.decimals)
+        self.high_orig = round(_high * self.pow_dot_one, self.decimals)
 
         super().__init__(_low, _high, prior, base, transform, name, dtype)
 
@@ -25,9 +29,9 @@ class SKDecimal(Integer):
         return self.low_orig <= point <= self.high_orig
 
     def transform(self, Xt):
-        aa = [int(x * pow(10, self.decimals)) for x in Xt]
-        return super().transform(aa)
+        return super().transform([int(v * self.pow_ten) for v in Xt])
 
     def inverse_transform(self, Xt):
         res = super().inverse_transform(Xt)
-        return [round(x * pow(0.1, self.decimals), self.decimals) for x in res]
+        # equivalent to [round(x * pow(0.1, self.decimals), self.decimals) for x in res]
+        return [int(v) / self.pow_ten for v in res]

freqtrade/persistence/models.py

@@ -2,7 +2,7 @@
 This module contains the class to persist trades into SQLite
 """
 import logging
-from datetime import datetime, timezone
+from datetime import datetime, timedelta, timezone
 from decimal import Decimal
 from typing import Any, Dict, List, Optional
 
@@ -195,6 +195,8 @@ class Order(_DECL_BASE):
     @staticmethod
     def get_open_orders() -> List['Order']:
         """
+        Retrieve open orders from the database
+        :return: List of open orders
         """
         return Order.query.filter(Order.ft_is_open.is_(True)).all()
 
@@ -491,6 +493,13 @@ class LocalTrade():
     def update_order(self, order: Dict) -> None:
         Order.update_orders(self.orders, order)
 
+    def get_exit_order_count(self) -> int:
+        """
+        Get amount of failed exiting orders
+        assumes full exits.
+        """
+        return len([o for o in self.orders if o.ft_order_side == 'sell'])
+
     def _calc_open_trade_value(self) -> float:
         """
         Calculate the open_rate including open_fee.
@@ -775,7 +784,7 @@ class Trade(_DECL_BASE, LocalTrade):
             return Trade.query
 
     @staticmethod
-    def get_open_order_trades():
+    def get_open_order_trades() -> List['Trade']:
         """
         Returns all open trades
         NOTE: Not supported in Backtesting.
@@ -832,30 +841,153 @@ class Trade(_DECL_BASE, LocalTrade):
         return total_open_stake_amount or 0
 
     @staticmethod
-    def get_overall_performance() -> List[Dict[str, Any]]:
+    def get_overall_performance(minutes=None) -> List[Dict[str, Any]]:
         """
         Returns List of dicts containing all Trades, including profit and trade count
         NOTE: Not supported in Backtesting.
         """
+        filters = [Trade.is_open.is_(False)]
+        if minutes:
+            start_date = datetime.now(timezone.utc) - timedelta(minutes=minutes)
+            filters.append(Trade.close_date >= start_date)
         pair_rates = Trade.query.with_entities(
             Trade.pair,
             func.sum(Trade.close_profit).label('profit_sum'),
             func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
             func.count(Trade.pair).label('count')
-        ).filter(Trade.is_open.is_(False))\
+        ).filter(*filters)\
             .group_by(Trade.pair) \
             .order_by(desc('profit_sum_abs')) \
             .all()
         return [
             {
                 'pair': pair,
-                'profit': profit,
+                'profit_ratio': profit,
+                'profit': round(profit * 100, 2),  # Compatibility mode
+                'profit_pct': round(profit * 100, 2),
                 'profit_abs': profit_abs,
                 'count': count
             }
             for pair, profit, profit_abs, count in pair_rates
         ]
 
+    @staticmethod
+    def get_buy_tag_performance(pair: Optional[str]) -> List[Dict[str, Any]]:
+        """
+        Returns List of dicts containing all Trades, based on buy tag performance
+        Can either be average for all pairs or a specific pair provided
+        NOTE: Not supported in Backtesting.
+        """
+
+        filters = [Trade.is_open.is_(False)]
+        if(pair is not None):
+            filters.append(Trade.pair == pair)
+
+        buy_tag_perf = Trade.query.with_entities(
+            Trade.buy_tag,
+            func.sum(Trade.close_profit).label('profit_sum'),
+            func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
+            func.count(Trade.pair).label('count')
+        ).filter(*filters)\
+            .group_by(Trade.buy_tag) \
+            .order_by(desc('profit_sum_abs')) \
+            .all()
+
+        return [
+            {
+                'buy_tag': buy_tag if buy_tag is not None else "Other",
+                'profit_ratio': profit,
+                'profit_pct': round(profit * 100, 2),
+                'profit_abs': profit_abs,
+                'count': count
+            }
+            for buy_tag, profit, profit_abs, count in buy_tag_perf
+        ]
+
+    @staticmethod
+    def get_sell_reason_performance(pair: Optional[str]) -> List[Dict[str, Any]]:
+        """
+        Returns List of dicts containing all Trades, based on sell reason performance
+        Can either be average for all pairs or a specific pair provided
+        NOTE: Not supported in Backtesting.
+        """
+
+        filters = [Trade.is_open.is_(False)]
+        if(pair is not None):
+            filters.append(Trade.pair == pair)
+
+        sell_tag_perf = Trade.query.with_entities(
+            Trade.sell_reason,
+            func.sum(Trade.close_profit).label('profit_sum'),
+            func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
+            func.count(Trade.pair).label('count')
+        ).filter(*filters)\
+            .group_by(Trade.sell_reason) \
+            .order_by(desc('profit_sum_abs')) \
+            .all()
+
+        return [
+            {
+                'sell_reason': sell_reason if sell_reason is not None else "Other",
+                'profit_ratio': profit,
+                'profit_pct': round(profit * 100, 2),
+                'profit_abs': profit_abs,
+                'count': count
+            }
+            for sell_reason, profit, profit_abs, count in sell_tag_perf
+        ]
+
+    @staticmethod
+    def get_mix_tag_performance(pair: Optional[str]) -> List[Dict[str, Any]]:
+        """
+        Returns List of dicts containing all Trades, based on buy_tag + sell_reason performance
+        Can either be average for all pairs or a specific pair provided
+        NOTE: Not supported in Backtesting.
+        """
+
+        filters = [Trade.is_open.is_(False)]
+        if(pair is not None):
+            filters.append(Trade.pair == pair)
+
+        mix_tag_perf = Trade.query.with_entities(
+            Trade.id,
+            Trade.buy_tag,
+            Trade.sell_reason,
+            func.sum(Trade.close_profit).label('profit_sum'),
+            func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
+            func.count(Trade.pair).label('count')
+        ).filter(*filters)\
+            .group_by(Trade.id) \
+            .order_by(desc('profit_sum_abs')) \
+            .all()
+
+        return_list: List[Dict] = []
+        for id, buy_tag, sell_reason, profit, profit_abs, count in mix_tag_perf:
+            buy_tag = buy_tag if buy_tag is not None else "Other"
+            sell_reason = sell_reason if sell_reason is not None else "Other"
+
+            if(sell_reason is not None and buy_tag is not None):
+                mix_tag = buy_tag + " " + sell_reason
+                i = 0
+                if not any(item["mix_tag"] == mix_tag for item in return_list):
+                    return_list.append({'mix_tag': mix_tag,
+                                        'profit': profit,
+                                        'profit_pct': round(profit * 100, 2),
+                                        'profit_abs': profit_abs,
+                                        'count': count})
+                else:
+                    while i < len(return_list):
+                        if return_list[i]["mix_tag"] == mix_tag:
+                            return_list[i] = {
+                                'mix_tag': mix_tag,
+                                'profit': profit + return_list[i]["profit"],
+                                'profit_pct': round(profit + return_list[i]["profit"] * 100, 2),
+                                'profit_abs': profit_abs + return_list[i]["profit_abs"],
+                                'count': 1 + return_list[i]["count"]}
+                        i += 1
+
+        return return_list
+
     @staticmethod
     def get_best_pair(start_date: datetime = datetime.fromtimestamp(0)):
         """
@@ -892,7 +1024,7 @@ class PairLock(_DECL_BASE):
         lock_time = self.lock_time.strftime(DATETIME_PRINT_FORMAT)
         lock_end_time = self.lock_end_time.strftime(DATETIME_PRINT_FORMAT)
         return (f'PairLock(id={self.id}, pair={self.pair}, lock_time={lock_time}, '
-                f'lock_end_time={lock_end_time})')
+                f'lock_end_time={lock_end_time}, reason={self.reason}, active={self.active})')
 
     @staticmethod
     def query_pair_locks(pair: Optional[str], now: datetime) -> Query:
@@ -901,7 +1033,6 @@ class PairLock(_DECL_BASE):
         :param pair: Pair to check for. Returns all current locks if pair is empty
         :param now: Datetime object (generated via datetime.now(timezone.utc)).
         """
-
         filters = [PairLock.lock_end_time > now,
                    # Only active locks
                    PairLock.active.is_(True), ]

freqtrade/persistence/pairlock_middleware.py

@@ -30,7 +30,8 @@ class PairLocks():
             PairLocks.locks = []
 
     @staticmethod
-    def lock_pair(pair: str, until: datetime, reason: str = None, *, now: datetime = None) -> None:
+    def lock_pair(pair: str, until: datetime, reason: str = None, *,
+                  now: datetime = None) -> PairLock:
         """
         Create PairLock from now to "until".
         Uses database by default, unless PairLocks.use_db is set to False,
@@ -52,6 +53,7 @@ class PairLocks():
             PairLock.query.session.commit()
         else:
             PairLocks.locks.append(lock)
+        return lock
 
     @staticmethod
     def get_pair_locks(pair: Optional[str], now: Optional[datetime] = None) -> List[PairLock]:
@@ -101,6 +103,36 @@ class PairLocks():
         if PairLocks.use_db:
             PairLock.query.session.commit()
 
+    @staticmethod
+    def unlock_reason(reason: str, now: Optional[datetime] = None) -> None:
+        """
+        Release all locks for this reason.
+        :param reason: Which reason to unlock
+        :param now: Datetime object (generated via datetime.now(timezone.utc)).
+            defaults to datetime.now(timezone.utc)
+        """
+        if not now:
+            now = datetime.now(timezone.utc)
+
+        if PairLocks.use_db:
+            # used in live modes
+            logger.info(f"Releasing all locks with reason '{reason}':")
+            filters = [PairLock.lock_end_time > now,
+                       PairLock.active.is_(True),
+                       PairLock.reason == reason
+                       ]
+            locks = PairLock.query.filter(*filters)
+            for lock in locks:
+                logger.info(f"Releasing lock for {lock.pair} with reason '{reason}'.")
+                lock.active = False
+            PairLock.query.session.commit()
+        else:
+            # used in backtesting mode; don't show log messages for speed
+            locks = PairLocks.get_pair_locks(None)
+            for lock in locks:
+                if lock.reason == reason:
+                    lock.active = False
+
     @staticmethod
     def is_global_lock(now: Optional[datetime] = None) -> bool:
         """
@@ -126,7 +158,9 @@ class PairLocks():
 
     @staticmethod
     def get_all_locks() -> List[PairLock]:
-
+        """
+        Return all locks, also locks with expired end date
+        """
         if PairLocks.use_db:
             return PairLock.query.all()
         else:

freqtrade/plot/plotting.py

@@ -169,8 +169,8 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
                 df_comb.loc[timeframe_to_prev_date(timeframe, lowdate), 'cum_profit'],
             ],
             mode='markers',
-            name=f"Max drawdown {max_drawdown * 100:.2f}%",
-            text=f"Max drawdown {max_drawdown * 100:.2f}%",
+            name=f"Max drawdown {max_drawdown:.2%}",
+            text=f"Max drawdown {max_drawdown:.2%}",
             marker=dict(
                 symbol='square-open',
                 size=9,
@@ -192,7 +192,7 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
     # Trades can be empty
     if trades is not None and len(trades) > 0:
         # Create description for sell summarizing the trade
-        trades['desc'] = trades.apply(lambda row: f"{round(row['profit_ratio'] * 100, 1)}%, "
+        trades['desc'] = trades.apply(lambda row: f"{row['profit_ratio']:.2%}, "
                                                   f"{row['sell_reason']}, "
                                                   f"{row['trade_duration']} min",
                                       axis=1)

freqtrade/plugins/pairlist/AgeFilter.py

@@ -8,6 +8,7 @@ from typing import Any, Dict, List, Optional
 import arrow
 from pandas import DataFrame
 
+from freqtrade.configuration import PeriodicCache
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
@@ -18,14 +19,15 @@ logger = logging.getLogger(__name__)
 
 class AgeFilter(IPairList):
 
-    # Checked symbols cache (dictionary of ticker symbol => timestamp)
-    _symbolsChecked: Dict[str, int] = {}
-
     def __init__(self, exchange, pairlistmanager,
                  config: Dict[str, Any], pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 
+        # Checked symbols cache (dictionary of ticker symbol => timestamp)
+        self._symbolsChecked: Dict[str, int] = {}
+        self._symbolsCheckFailed = PeriodicCache(maxsize=1000, ttl=86_400)
+
         self._min_days_listed = pairlistconfig.get('min_days_listed', 10)
         self._max_days_listed = pairlistconfig.get('max_days_listed', None)
 
@@ -69,9 +71,12 @@ class AgeFilter(IPairList):
         :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new allowlist
         """
-        needed_pairs = [(p, '1d') for p in pairlist if p not in self._symbolsChecked]
+        needed_pairs = [
+            (p, '1d') for p in pairlist
+            if p not in self._symbolsChecked and p not in self._symbolsCheckFailed]
         if not needed_pairs:
-            return pairlist
+            # Remove pairs that have been removed before
+            return [p for p in pairlist if p not in self._symbolsCheckFailed]
 
         since_days = -(
             self._max_days_listed if self._max_days_listed else self._min_days_listed
@@ -118,5 +123,6 @@ class AgeFilter(IPairList):
                     " or more than "
                     f"{self._max_days_listed} {plural(self._max_days_listed, 'day')}"
                 ) if self._max_days_listed else ''), logger.info)
+                self._symbolsCheckFailed[pair] = arrow.utcnow().int_timestamp * 1000
                 return False
         return False

freqtrade/plugins/pairlist/PerformanceFilter.py

@@ -2,7 +2,7 @@
 Performance pair list filter
 """
 import logging
-from typing import Dict, List
+from typing import Any, Dict, List
 
 import pandas as pd
 
@@ -15,6 +15,14 @@ logger = logging.getLogger(__name__)
 
 class PerformanceFilter(IPairList):
 
+    def __init__(self, exchange, pairlistmanager,
+                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 pairlist_pos: int) -> None:
+        super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
+
+        self._minutes = pairlistconfig.get('minutes', 0)
+        self._min_profit = pairlistconfig.get('min_profit', None)
+
     @property
     def needstickers(self) -> bool:
         """
@@ -40,7 +48,7 @@ class PerformanceFilter(IPairList):
         """
         # Get the trading performance for pairs from database
         try:
-            performance = pd.DataFrame(Trade.get_overall_performance())
+            performance = pd.DataFrame(Trade.get_overall_performance(self._minutes))
         except AttributeError:
             # Performancefilter does not work in backtesting.
             self.log_once("PerformanceFilter is not available in this mode.", logger.warning)
@@ -61,6 +69,14 @@ class PerformanceFilter(IPairList):
         sorted_df = list_df.merge(performance, on='pair', how='left')\
             .fillna(0).sort_values(by=['count', 'pair'], ascending=True)\
             .sort_values(by=['profit'], ascending=False)
+        if self._min_profit is not None:
+            removed = sorted_df[sorted_df['profit'] < self._min_profit]
+            for _, row in removed.iterrows():
+                self.log_once(
+                    f"Removing pair {row['pair']} since {row['profit']} is "
+                    f"below {self._min_profit}", logger.info)
+            sorted_df = sorted_df[sorted_df['profit'] >= self._min_profit]
+
         pairlist = sorted_df['pair'].tolist()
 
         return pairlist

freqtrade/plugins/pairlist/PriceFilter.py

@@ -50,7 +50,7 @@ class PriceFilter(IPairList):
         """
         active_price_filters = []
         if self._low_price_ratio != 0:
-            active_price_filters.append(f"below {self._low_price_ratio * 100}%")
+            active_price_filters.append(f"below {self._low_price_ratio:.1%}")
         if self._min_price != 0:
             active_price_filters.append(f"below {self._min_price:.8f}")
         if self._max_price != 0:
@@ -82,7 +82,7 @@ class PriceFilter(IPairList):
             changeperc = compare / ticker['last']
             if changeperc > self._low_price_ratio:
                 self.log_once(f"Removed {pair} from whitelist, "
-                              f"because 1 unit is {changeperc * 100:.3f}%", logger.info)
+                              f"because 1 unit is {changeperc:.3%}", logger.info)
                 return False
 
         # Perform low_amount check

freqtrade/plugins/pairlist/SpreadFilter.py

@@ -34,7 +34,7 @@ class SpreadFilter(IPairList):
         Short whitelist method description - used for startup-messages
         """
         return (f"{self.name} - Filtering pairs with ask/bid diff above "
-                f"{self._max_spread_ratio * 100}%.")
+                f"{self._max_spread_ratio:.2%}.")
 
     def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
         """
@@ -47,7 +47,7 @@ class SpreadFilter(IPairList):
             spread = 1 - ticker['bid'] / ticker['ask']
             if spread > self._max_spread_ratio:
                 self.log_once(f"Removed {pair} from whitelist, because spread "
-                              f"{spread * 100:.3f}% > {self._max_spread_ratio * 100}%",
+                              f"{spread * 100:.3%} > {self._max_spread_ratio:.3%}",
                               logger.info)
                 return False
             else:

freqtrade/plugins/pairlist/StaticPairList.py

@@ -4,9 +4,9 @@ Static Pair List provider
 Provides pair white list as it configured in config
 """
 import logging
+from copy import deepcopy
 from typing import Any, Dict, List
 
-from freqtrade.exceptions import OperationalException
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -20,10 +20,6 @@ class StaticPairList(IPairList):
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 
-        if self._pairlist_pos != 0:
-            raise OperationalException(f"{self.name} can only be used in the first position "
-                                       "in the list of Pairlist Handlers.")
-
         self._allow_inactive = self._pairlistconfig.get('allow_inactive', False)
 
     @property
@@ -64,4 +60,8 @@ class StaticPairList(IPairList):
         :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new whitelist
         """
-        return pairlist
+        pairlist_ = deepcopy(pairlist)
+        for pair in self._config['exchange']['pair_whitelist']:
+            if pair not in pairlist_:
+                pairlist_.append(pair)
+        return pairlist_

freqtrade/plugins/pairlist/VolumePairList.py

@@ -123,7 +123,7 @@ class VolumePairList(IPairList):
             filtered_tickers = [
                 v for k, v in tickers.items()
                 if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
-                    and v[self._sort_key] is not None)]
+                    and (self._use_range or v[self._sort_key] is not None))]
             pairlist = [s['symbol'] for s in filtered_tickers]
 
             pairlist = self.filter_pairlist(pairlist, tickers)

freqtrade/plugins/pairlist/pairlist_helpers.py

@@ -17,7 +17,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
     if keep_invalid:
         for pair_wc in wildcardpl:
             try:
-                comp = re.compile(pair_wc)
+                comp = re.compile(pair_wc, re.IGNORECASE)
                 result_partial = [
                     pair for pair in available_pairs if re.fullmatch(comp, pair)
                 ]
@@ -33,7 +33,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
     else:
         for pair_wc in wildcardpl:
             try:
-                comp = re.compile(pair_wc)
+                comp = re.compile(pair_wc, re.IGNORECASE)
                 result += [
                     pair for pair in available_pairs if re.fullmatch(comp, pair)
                 ]

freqtrade/plugins/protectionmanager.py

@@ -6,6 +6,7 @@ from datetime import datetime, timezone
 from typing import Dict, List, Optional
 
 from freqtrade.persistence import PairLocks
+from freqtrade.persistence.models import PairLock
 from freqtrade.plugins.protections import IProtection
 from freqtrade.resolvers import ProtectionResolver
 
@@ -43,30 +44,28 @@ class ProtectionManager():
         """
         return [{p.name: p.short_desc()} for p in self._protection_handlers]
 
-    def global_stop(self, now: Optional[datetime] = None) -> bool:
+    def global_stop(self, now: Optional[datetime] = None) -> Optional[PairLock]:
         if not now:
             now = datetime.now(timezone.utc)
-        result = False
+        result = None
         for protection_handler in self._protection_handlers:
             if protection_handler.has_global_stop:
-                result, until, reason = protection_handler.global_stop(now)
+                lock, until, reason = protection_handler.global_stop(now)
 
                 # Early stopping - first positive result blocks further trades
-                if result and until:
+                if lock and until:
                     if not PairLocks.is_global_lock(until):
-                        PairLocks.lock_pair('*', until, reason, now=now)
-                    result = True
+                        result = PairLocks.lock_pair('*', until, reason, now=now)
         return result
 
-    def stop_per_pair(self, pair, now: Optional[datetime] = None) -> bool:
+    def stop_per_pair(self, pair, now: Optional[datetime] = None) -> Optional[PairLock]:
         if not now:
             now = datetime.now(timezone.utc)
-        result = False
+        result = None
         for protection_handler in self._protection_handlers:
             if protection_handler.has_local_stop:
-                result, until, reason = protection_handler.stop_per_pair(pair, now)
-                if result and until:
+                lock, until, reason = protection_handler.stop_per_pair(pair, now)
+                if lock and until:
                     if not PairLocks.is_pair_locked(pair, until):
-                        PairLocks.lock_pair(pair, until, reason, now=now)
-                    result = True
+                        result = PairLocks.lock_pair(pair, until, reason, now=now)
         return result

freqtrade/resolvers/hyperopt_resolver.py

@@ -9,7 +9,6 @@ from typing import Dict
 
 from freqtrade.constants import HYPEROPT_LOSS_BUILTIN, USERPATH_HYPEROPTS
 from freqtrade.exceptions import OperationalException
-from freqtrade.optimize.hyperopt_interface import IHyperOpt
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
 from freqtrade.resolvers import IResolver
 
@@ -17,43 +16,6 @@ from freqtrade.resolvers import IResolver
 logger = logging.getLogger(__name__)
 
 
-class HyperOptResolver(IResolver):
-    """
-    This class contains all the logic to load custom hyperopt class
-    """
-    object_type = IHyperOpt
-    object_type_str = "Hyperopt"
-    user_subdir = USERPATH_HYPEROPTS
-    initial_search_path = None
-
-    @staticmethod
-    def load_hyperopt(config: Dict) -> IHyperOpt:
-        """
-        Load the custom hyperopt class from config parameter
-        :param config: configuration dictionary
-        """
-        if not config.get('hyperopt'):
-            raise OperationalException("No Hyperopt set. Please use `--hyperopt` to specify "
-                                       "the Hyperopt class to use.")
-
-        hyperopt_name = config['hyperopt']
-
-        hyperopt = HyperOptResolver.load_object(hyperopt_name, config,
-                                                kwargs={'config': config},
-                                                extra_dir=config.get('hyperopt_path'))
-
-        if not hasattr(hyperopt, 'populate_indicators'):
-            logger.info("Hyperopt class does not provide populate_indicators() method. "
-                        "Using populate_indicators from the strategy.")
-        if not hasattr(hyperopt, 'populate_buy_trend'):
-            logger.info("Hyperopt class does not provide populate_buy_trend() method. "
-                        "Using populate_buy_trend from the strategy.")
-        if not hasattr(hyperopt, 'populate_sell_trend'):
-            logger.info("Hyperopt class does not provide populate_sell_trend() method. "
-                        "Using populate_sell_trend from the strategy.")
-        return hyperopt
-
-
 class HyperOptLossResolver(IResolver):
     """
     This class contains all the logic to load custom hyperopt loss class

freqtrade/resolvers/iresolver.py

@@ -91,7 +91,7 @@ class IResolver:
         logger.debug(f"Searching for {cls.object_type.__name__} {object_name} in '{directory}'")
         for entry in directory.iterdir():
             # Only consider python files
-            if not str(entry).endswith('.py'):
+            if entry.suffix != '.py':
                 logger.debug('Ignoring %s', entry)
                 continue
             if entry.is_symlink() and not entry.is_file():
@@ -169,7 +169,7 @@ class IResolver:
         objects = []
         for entry in directory.iterdir():
             # Only consider python files
-            if not str(entry).endswith('.py'):
+            if entry.suffix != '.py':
                 logger.debug('Ignoring %s', entry)
                 continue
             module_path = entry.resolve()