Merge pull request #5636 from freqtrade/new_release

New release 2021.9

.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 1>
-- <change log #2>
+- <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

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9.6-slim-buster as base
+FROM python:3.9.7-slim-buster 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

@@ -30,6 +30,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
 - [X] [Bittrex](https://bittrex.com/)
 - [X] [Kraken](https://kraken.com/)
 - [X] [FTX](https://ftx.com)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
 - [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
 ### Community tested
@@ -78,22 +79,22 @@ 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 +108,10 @@ 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

build_helpers/install_ta-lib.sh

@@ -12,9 +12,12 @@ if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
   && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub \
   && ./configure --prefix=${INSTALL_LOC}/ \
   && make -j$(nproc) \
-  && which sudo && sudo make install || make install \
+  && which sudo && sudo make install || make install
-  && cd ..
+  if [ -x "$(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_full.example.json

@@ -149,7 +149,9 @@
             },
             "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

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
+!!! Note "`*args` and `**kwargs`"
-    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
+    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/backtesting.md

@@ -18,6 +18,7 @@ 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]
 
@@ -55,6 +56,9 @@ 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
@@ -425,7 +429,12 @@ 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
+### 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 our [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:
 
@@ -456,10 +465,30 @@ 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).
+One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).
-You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
+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

@@ -444,8 +444,8 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
 ```
 
 !!! Warning
-    This is ongoing work. For now, it is supported only for binance.
+    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.
+    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.
 
 ### Exchange configuration
 

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/docker_quickstart.md

@@ -149,6 +149,24 @@ You'll then also need to modify the `docker-compose.yml` file and uncomment the
 
 You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
 
+### 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.
+    ```
+    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.
+
 ## 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.

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

@@ -4,6 +4,8 @@ This page combines common gotchas and informations which are exchange-specific a
 
 ## 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 +58,12 @@ 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.
@@ -113,8 +121,12 @@ 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.
@@ -158,6 +170,8 @@ 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

@@ -167,7 +167,7 @@ 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

@@ -44,9 +44,8 @@ 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]
+                          [-p PAIRS [PAIRS ...]] [--hyperopt-path PATH]
-                          [--hyperopt-path PATH] [--eps] [--dmmp]
+                          [--eps] [--dmmp] [--enable-protections]
-                          [--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]
@@ -73,10 +72,8 @@ 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
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt Loss
-                        bot.
+                        functions.
-  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
-                        Hyperopt Loss functions.
   --eps, --enable-position-stacking
                         Allow buying the same pair multiple times (position
                         stacking).
@@ -456,7 +453,7 @@ class MyAwesomeStrategy(IStrategy):
                 "only_per_pair": False
             })
 
-        return protection
+        return prot
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # ...
@@ -558,7 +555,7 @@ 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 +677,11 @@ 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 +723,7 @@ 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 +761,10 @@ 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 +824,8 @@ 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

@@ -82,6 +82,8 @@ 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 +107,24 @@ 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 +165,7 @@ Example to remove the first 10 pairs from the pairlist:
 
 ```json
 "pairlists": [
+    // ...
     {
         "method": "OffsetFilter",
         "offset": 10
@@ -170,6 +191,19 @@ Sorts pairs by past trade performance, as follows:
 
 Trade count is used as a tie breaker.
 
+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.
+
+```json
+"pairlists": [
+    // ...
+    {
+        "method": "PerformanceFilter",
+        "minutes": 1440  // rolling 24h
+    }
+],
+```
+
 !!! Note
     `PerformanceFilter` does not support backtesting mode.
 

docs/index.md

@@ -40,6 +40,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
 - [X] [Bittrex](https://bittrex.com/)
 - [X] [FTX](https://ftx.com)
 - [X] [Kraken](https://kraken.com/)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
 - [ ] [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

docs/requirements-docs.txt

@@ -1,4 +1,4 @@
 mkdocs==1.2.2
-mkdocs-material==7.2.4
+mkdocs-material==7.3.0
 mdx_truly_sane_lists==1.2
 pymdown-extensions==8.2

docs/strategy-advanced.md

@@ -288,6 +288,12 @@ Stoploss values returned from `custom_stoploss()` always specify a percentage re
 
 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()`.
 
+### Calculating 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()`.
+
 #### Stepped stoploss
 
 Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
@@ -695,3 +701,33 @@ 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-customization.md

@@ -639,6 +639,167 @@ 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)
+
+    ```
+
+### *@informative()*
+
+``` 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.
+    """
+```
+
+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.
+
+??? 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 (Wallets)
 
@@ -781,6 +942,8 @@ 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.
 

docs/telegram-usage.md

@@ -93,7 +93,9 @@ 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 +105,7 @@ 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.

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.
+Use the `list-strategies` subcommand to see all strategies in one particular directory.
-The file will be named inline with your class name, and will not overwrite existing files.
 
-Results will be located in `user_data/hyperopts/<classname>.py`.
+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).
-
-``` 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).
 
 ```
 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.

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2021.8'
+__version__ = '2021.9'
 
 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

@@ -11,11 +11,11 @@ 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.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,
+from freqtrade.commands.list_commands import (start_list_exchanges, start_list_markets,
-                                              start_list_markets, start_list_strategies,
+                                              start_list_strategies, start_list_timeframes,
-                                              start_list_timeframes, start_show_trades)
+                                              start_show_trades)
 from freqtrade.commands.optimize_commands import start_backtesting, 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

freqtrade/commands/arguments.py

@@ -22,7 +22,7 @@ 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",
+                                        "enable_protections", "dry_run_wallet", "timeframe_detail",
                                         "strategy_list", "export", "exportfilename"]
 
 ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
@@ -55,8 +55,6 @@ 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"]
 
@@ -92,10 +90,10 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
 
 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",
+                    "hyperopt-list", "hyperopt-show",
                     "plot-dataframe", "plot-profit", "show-trades"]
 
-NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
+NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
 
 
 class Arguments:
@@ -174,12 +172,11 @@ class Arguments:
         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,
+                                        start_list_data, start_list_exchanges, start_list_markets,
-                                        start_list_markets, start_list_strategies,
+                                        start_list_strategies, start_list_timeframes,
-                                        start_list_timeframes, start_new_config, start_new_hyperopt,
+                                        start_new_config, start_new_strategy, start_plot_dataframe,
-                                        start_new_strategy, start_plot_dataframe, start_plot_profit,
+                                        start_plot_profit, start_show_trades, start_test_pairlist,
-                                        start_show_trades, start_test_pairlist, start_trading,
+                                        start_trading, start_webserver)
-                                        start_webserver)
 
         subparsers = self.parser.add_subparsers(dest='command',
                                                 # Use custom message when no subhandler is added
@@ -206,12 +203,6 @@ 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")
@@ -300,15 +291,6 @@ 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

@@ -61,21 +61,27 @@ 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:",
+            "message": f"Please insert your stake amount (Number or '{UNLIMITED_STAKE_AMOUNT}'):",
-            "default": "0.01",
+            "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": "text",
@@ -99,6 +105,8 @@ def ask_user_config() -> Dict[str, Any]:
                 "bittrex",
                 "kraken",
                 "ftx",
+                "kucoin",
+                "gateio",
                 Separator(),
                 "other",
             ],
@@ -122,6 +130,12 @@ 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'] == 'kucoin'
+        },
         {
             "type": "confirm",
             "name": "telegram",

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).',
@@ -199,13 +203,13 @@ 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(

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.")

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
         )
 
 

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/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
+    # Ensure these modes are using Dry-run
-    remove_credentials(config)
+    config['dry_run'] = True
     validate_config_consistency(config)
 
     return config

freqtrade/configuration/configuration.py

@@ -242,6 +242,9 @@ 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='stake_amount',
                              logstring='Parameter --stake-amount detected, '
                              'overriding stake_amount to: {} ...')

freqtrade/constants.py

@@ -69,9 +69,7 @@ DUST_PER_COIN = {
 # 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 +110,7 @@ CONF_SCHEMA = {
         },
         'tradable_balance_ratio': {
             'type': 'number',
-            'minimum': 0.1,
+            'minimum': 0.0,
             'maximum': 1,
             'default': 0.99
         },
@@ -286,6 +284,15 @@ 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'},

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/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

freqtrade/exchange/binance.py

@@ -1,7 +1,8 @@
 """ Binance exchange subclass """
 import logging
-from typing import Dict
+from typing import Dict, List
 
+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 +91,20 @@ 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
+                                        ) -> 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)

freqtrade/exchange/common.py

@@ -51,6 +51,19 @@ 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

freqtrade/exchange/exchange.py

@@ -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,
+                                       EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
-                                       retrier_async)
+                                       remove_credentials, retrier, retrier_async)
-from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
+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')
@@ -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(
@@ -709,7 +725,8 @@ 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
@@ -1178,7 +1195,7 @@ 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.
@@ -1190,7 +1207,7 @@ class Exchange:
         """
         return 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))
 
     def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
                                  since_ms: int) -> DataFrame:
@@ -1205,11 +1222,12 @@ 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,
+    async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
-                                        timeframe: str,
+                                        since_ms: int, is_new_pair: bool
-                                        since_ms: int) -> List:
+                                        ) -> 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)
@@ -1222,21 +1240,22 @@ 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:
+        # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
-            if isinstance(res, Exception):
+        for input_coro in chunks(input_coroutines, 100):
-                logger.warning("Async code raised an exception: %s", res.__class__.__name__)
+
-                continue
+            results = await asyncio.gather(*input_coro, return_exceptions=True)
-            # Deconstruct tuple if it's not an exception
+            for res in results:
-            p, _, new_data = res
+                if isinstance(res, Exception):
-            if p == pair:
+                    logger.warning("Async code raised an exception: %s", res.__class__.__name__)
-                data.extend(new_data)
+                    continue
+                # 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))
+        logger.info(f"Downloaded data for {pair} with length {len(data)}.")
         return data
 
     def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,

freqtrade/exchange/gateio.py

@@ -21,3 +21,5 @@ class Gateio(Exchange):
     _ft_has: Dict = {
         "ohlcv_candle_limit": 1000,
     }
+
+    _headers = {'X-Gate-Channel-Id': 'freqtrade'}

freqtrade/exchange/kucoin.py

@@ -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/freqtradebot.py

@@ -83,10 +83,10 @@ class FreqtradeBot(LoggingMixin):
 
         self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
 
-        # Attach Dataprovider to Strategy baseclass
+        # Attach Dataprovider to strategy instance
-        IStrategy.dp = self.dataprovider
+        self.strategy.dp = self.dataprovider
-        # Attach Wallets to Strategy baseclass
+        # Attach Wallets to strategy instance
-        IStrategy.wallets = self.wallets
+        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:
@@ -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)
@@ -296,9 +296,9 @@ 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.
@@ -476,21 +476,21 @@ 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:
@@ -498,7 +498,7 @@ 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)
 
@@ -508,27 +508,27 @@ 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 +551,13 @@ 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 +569,8 @@ class FreqtradeBot(LoggingMixin):
             amount_requested=amount_requested,
             fee_open=fee,
             fee_close=fee,
-            open_rate=buy_limit_filled_price,
+            open_rate=enter_limit_filled_price,
-            open_rate_requested=buy_limit_requested,
+            open_rate_requested=enter_limit_requested,
             open_date=datetime.utcnow(),
             exchange=self.exchange.id,
             open_order_id=order_id,
@@ -590,11 +590,11 @@ 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 +617,7 @@ 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 +643,7 @@ 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,
@@ -713,8 +713,8 @@ class FreqtradeBot(LoggingMixin):
             )
 
         logger.debug('checking sell')
-        sell_rate = self.exchange.get_rate(trade.pair, refresh=True, side="sell")
+        exit_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, exit_rate, buy, sell):
             return True
 
         logger.debug('Found no sell signal for %s.', trade)
@@ -744,7 +744,7 @@ 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 +782,7 @@ 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 +851,19 @@ 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,
+    def _check_and_execute_exit(self, trade: Trade, exit_rate: float,
                                 buy: bool, sell: bool) -> 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)
+            self.execute_trade_exit(trade, exit_rate, should_sell)
             return True
         return False
 
@@ -906,7 +906,7 @@ 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 +915,7 @@ 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'])
 
     def cancel_all_open_orders(self) -> None:
         """
@@ -931,13 +931,13 @@ 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 +994,11 @@ 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'],
+        self._notify_enter_cancel(trade, order_type=self.strategy.order_types['buy'],
-                                reason=reason)
+                                  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 +1032,14 @@ 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.
@@ -1111,7 +1111,7 @@ 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)(
@@ -1150,11 +1150,11 @@ 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.
         """
@@ -1196,7 +1196,7 @@ 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.
         """
@@ -1217,7 +1217,7 @@ 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,
@@ -1226,7 +1226,7 @@ class FreqtradeBot(LoggingMixin):
             'profit_ratio': profit_ratio,
             '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,
@@ -1291,16 +1291,28 @@ class FreqtradeBot(LoggingMixin):
         # 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._notify_exit(trade, '', True)
-            self.protections.stop_per_pair(trade.pair)
+            self.handle_protections(trade.pair)
-            self.protections.global_stop()
             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:
         """

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
@@ -61,8 +61,7 @@ class Backtesting:
         self.config = config
         self.results: Optional[Dict[str, Any]] = None
 
-        # Reset keys for backtesting
+        config['dry_run'] = True
-        remove_credentials(self.config)
         self.strategylist: List[IStrategy] = []
         self.all_results: Dict[str, Dict] = {}
 
@@ -86,7 +85,7 @@ 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.")
@@ -109,14 +108,6 @@ 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 +116,7 @@ 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.init_backtest()
-        self.progress = BTProgress()
-        self.abort = False
 
     def __del__(self):
         self.cleanup()
@@ -137,6 +126,28 @@ 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 +155,7 @@ 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 +199,23 @@ 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 +227,8 @@ 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):
         """
@@ -318,7 +347,8 @@ 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],
@@ -346,6 +376,32 @@ class Backtesting:
 
         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)
@@ -592,6 +648,7 @@ 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
+        # Ensure using dry-run
-        remove_credentials(self.config)
+        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
@@ -44,7 +45,7 @@ progressbar.streams.wrap_stdout()
 logger = logging.getLogger(__name__)
 
 
-INITIAL_POINTS = 30
+INITIAL_POINTS = 5
 
 # Keep no more than SKOPT_MODEL_QUEUE_SIZE models
 # in the skopt model queue, to optimize memory consumption
@@ -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)
+            raise OperationalException(
-            self.auto_hyperopt = False
+                "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")
@@ -285,6 +261,15 @@ class Hyperopt:
         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 +281,13 @@ class Hyperopt:
 
         # Apply parameters
         if HyperoptTools.has_space(self.config, 'buy'):
-            self.backtesting.strategy.advise_buy = (  # type: ignore
+            self.assign_params(params_dict, 'buy')
-                self.custom_hyperopt.buy_strategy_generator(params_dict))
 
         if HyperoptTools.has_space(self.config, 'sell'):
-            self.backtesting.strategy.advise_sell = (  # type: ignore
+            self.assign_params(params_dict, 'sell')
-                self.custom_hyperopt.sell_strategy_generator(params_dict))
 
         if HyperoptTools.has_space(self.config, 'protection'):
-            for attr_name, attr in self.backtesting.strategy.enumerate_parameters('protection'):
+            self.assign_params(params_dict, 'protection')
-                if attr.optimize:
-                    # noinspection PyProtectedMember
-                    attr.value = params_dict[attr_name]
 
         if HyperoptTools.has_space(self.config, 'roi'):
             self.backtesting.strategy.minimal_roi = (  # type: ignore
@@ -385,10 +365,20 @@ 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",
+            base_estimator=estimator,
-            acq_optimizer="auto",
+            acq_optimizer=acq_optimizer,
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
             random_state=self.random_state,
@@ -517,11 +507,10 @@ class Hyperopt:
                     f"saved to '{self.results_file}'.")
 
         if self.current_best_epoch:
-            if self.auto_hyperopt:
+            HyperoptTools.try_export_params(
-                HyperoptTools.try_export_params(
+                self.config,
-                    self.config,
+                self.backtesting.strategy.get_strategy_name(),
-                    self.backtesting.strategy.get_strategy_name(),
+                self.current_best_epoch)
-                    self.current_best_epoch)
 
             HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
                                              self.print_json)

freqtrade/optimize/hyperopt_auto.py

@@ -4,15 +4,23 @@ This module implements a convenience auto-hyperopt class, which can be used toge
  that implement IHyperStrategy interface.
 """
 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
+
+
+def _format_exception_message(space: str) -> str:
+    raise OperationalException(
+        f"The '{space}' space is included into the hyperoptimization "
+        f"but no parameter for this space was not found in your Strategy. "
+        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 +30,6 @@ 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 +48,22 @@ 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):
+        # 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)
 
-    def indicator_space(self) -> List['Dimension']:
+    def buy_indicator_space(self) -> List['Dimension']:
-        return self._get_indicator_space('buy', 'indicator_space')
+        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 +79,6 @@ 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__)
 
-
+EstimatorType = Union[RegressorMixin, str]
-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.")
 
 
 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'))
+        return 'ET'
-
-    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'))
 
     def generate_roi_table(self, params: Dict) -> Dict[int, float]:
         """

freqtrade/optimize/hyperopt_tools.py

@@ -7,6 +7,7 @@ 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
@@ -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,
+    def export_csv_file(config: dict, results: list, csv_file: str) -> None:
-                        csv_file: str) -> None:
         """
         Log result to csv-file
         """

freqtrade/optimize/optimize_reports.py

@@ -368,6 +368,7 @@ 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,

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
 
@@ -832,17 +832,21 @@ 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()

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]:

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 +15,13 @@ 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)
+
     @property
     def needstickers(self) -> bool:
         """
@@ -40,7 +47,7 @@ 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)

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 = PairLocks.lock_pair('*', until, reason, now=now)
-                    result = True
         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)
+                lock, until, reason = protection_handler.stop_per_pair(pair, now)
-                if result and until:
+                if lock and until:
                     if not PairLocks.is_pair_locked(pair, until):
-                        PairLocks.lock_pair(pair, until, reason, now=now)
+                        result = PairLocks.lock_pair(pair, until, reason, now=now)
-                    result = True
         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/rpc/api_server/api_backtest.py

@@ -4,6 +4,7 @@ from copy import deepcopy
 
 from fastapi import APIRouter, BackgroundTasks, Depends
 
+from freqtrade.configuration.config_validation import validate_config_consistency
 from freqtrade.enums import BacktestState
 from freqtrade.exceptions import DependencyException
 from freqtrade.rpc.api_server.api_schemas import BacktestRequest, BacktestResponse
@@ -42,35 +43,40 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
             # Reload strategy
             lastconfig = ApiServer._bt_last_config
             strat = StrategyResolver.load_strategy(btconfig)
+            validate_config_consistency(btconfig)
 
             if (
                 not ApiServer._bt
                 or lastconfig.get('timeframe') != strat.timeframe
-                or lastconfig.get('dry_run_wallet') != btconfig.get('dry_run_wallet', 0)
+                or lastconfig.get('timeframe_detail') != btconfig.get('timeframe_detail')
                 or lastconfig.get('timerange') != btconfig['timerange']
             ):
                 from freqtrade.optimize.backtesting import Backtesting
                 ApiServer._bt = Backtesting(btconfig)
-
+                if ApiServer._bt.timeframe_detail:
+                    ApiServer._bt.load_bt_data_detail()
+            else:
+                ApiServer._bt.config = btconfig
+                ApiServer._bt.init_backtest()
             # Only reload data if timeframe changed.
             if (
                 not ApiServer._bt_data
                 or not ApiServer._bt_timerange
-                or lastconfig.get('stake_amount') != btconfig.get('stake_amount')
-                or lastconfig.get('enable_protections') != btconfig.get('enable_protections')
-                or lastconfig.get('protections') != btconfig.get('protections', [])
                 or lastconfig.get('timeframe') != strat.timeframe
+                or lastconfig.get('timerange') != btconfig['timerange']
             ):
-                lastconfig['timerange'] = btconfig['timerange']
-                lastconfig['protections'] = btconfig.get('protections', [])
-                lastconfig['enable_protections'] = btconfig.get('enable_protections')
-                lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
-                lastconfig['timeframe'] = strat.timeframe
                 ApiServer._bt_data, ApiServer._bt_timerange = ApiServer._bt.load_bt_data()
 
+            lastconfig['timerange'] = btconfig['timerange']
+            lastconfig['timeframe'] = strat.timeframe
+            lastconfig['protections'] = btconfig.get('protections', [])
+            lastconfig['enable_protections'] = btconfig.get('enable_protections')
+            lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
+
             ApiServer._bt.abort = False
             min_date, max_date = ApiServer._bt.backtest_one_strategy(
                 strat, ApiServer._bt_data, ApiServer._bt_timerange)
+
             ApiServer._bt.results = generate_backtest_stats(
                 ApiServer._bt_data, ApiServer._bt.all_results,
                 min_date=min_date, max_date=max_date)

freqtrade/rpc/api_server/api_schemas.py

@@ -46,6 +46,12 @@ class Balances(BaseModel):
     value: float
     stake: str
     note: str
+    starting_capital: float
+    starting_capital_ratio: float
+    starting_capital_pct: float
+    starting_capital_fiat: float
+    starting_capital_fiat_ratio: float
+    starting_capital_fiat_pct: float
 
 
 class Count(BaseModel):
@@ -324,6 +330,7 @@ class PairHistory(BaseModel):
 class BacktestRequest(BaseModel):
     strategy: str
     timeframe: Optional[str]
+    timeframe_detail: Optional[str]
     timerange: Optional[str]
     max_open_trades: Optional[int]
     stake_amount: Optional[Union[float, str]]

freqtrade/rpc/api_server/uvicorn_threaded.py

@@ -5,6 +5,20 @@ import time
 import uvicorn
 
 
+def asyncio_setup() -> None:  # pragma: no cover
+    # Set eventloop for win32 setups
+    # Reverts a change done in uvicorn 0.15.0 - which now sets the eventloop
+    # via policy.
+    import sys
+
+    if sys.version_info >= (3, 8) and sys.platform == "win32":
+        import asyncio
+        import selectors
+        selector = selectors.SelectSelector()
+        loop = asyncio.SelectorEventLoop(selector)
+        asyncio.set_event_loop(loop)
+
+
 class UvicornServer(uvicorn.Server):
     """
     Multithreaded server - as found in https://github.com/encode/uvicorn/issues/742
@@ -28,7 +42,7 @@ class UvicornServer(uvicorn.Server):
         try:
             import uvloop  # noqa
         except ImportError:  # pragma: no cover
-            from uvicorn.loops.asyncio import asyncio_setup
+
             asyncio_setup()
         else:
             asyncio.set_event_loop(uvloop.new_event_loop())

freqtrade/rpc/rpc.py

@@ -403,8 +403,11 @@ class RPC:
         # Doing the sum is not right - overall profit needs to be based on initial capital
         profit_all_ratio_sum = sum(profit_all_ratio) if profit_all_ratio else 0.0
         starting_balance = self._freqtrade.wallets.get_starting_balance()
-        profit_closed_ratio_fromstart = profit_closed_coin_sum / starting_balance
+        profit_closed_ratio_fromstart = 0
-        profit_all_ratio_fromstart = profit_all_coin_sum / starting_balance
+        profit_all_ratio_fromstart = 0
+        if starting_balance:
+            profit_closed_ratio_fromstart = profit_closed_coin_sum / starting_balance
+            profit_all_ratio_fromstart = profit_all_coin_sum / starting_balance
 
         profit_all_fiat = self._fiat_converter.convert_amount(
             profit_all_coin_sum,
@@ -455,6 +458,9 @@ class RPC:
             raise RPCException('Error getting current tickers.')
 
         self._freqtrade.wallets.update(require_update=False)
+        starting_capital = self._freqtrade.wallets.get_starting_balance()
+        starting_cap_fiat = self._fiat_converter.convert_amount(
+            starting_capital, stake_currency, fiat_display_currency) if self._fiat_converter else 0
 
         for coin, balance in self._freqtrade.wallets.get_all_balances().items():
             if not balance.total:
@@ -490,15 +496,25 @@ class RPC:
             else:
                 raise RPCException('All balances are zero.')
 
-        symbol = fiat_display_currency
+        value = self._fiat_converter.convert_amount(
-        value = self._fiat_converter.convert_amount(total, stake_currency,
+            total, stake_currency, fiat_display_currency) if self._fiat_converter else 0
-                                                    symbol) if self._fiat_converter else 0
+
+        starting_capital_ratio = 0.0
+        starting_capital_ratio = (total / starting_capital) - 1 if starting_capital else 0.0
+        starting_cap_fiat_ratio = (value / starting_cap_fiat) - 1 if starting_cap_fiat else 0.0
+
         return {
             'currencies': output,
             'total': total,
-            'symbol': symbol,
+            'symbol': fiat_display_currency,
             'value': value,
             'stake': stake_currency,
+            'starting_capital': starting_capital,
+            'starting_capital_ratio': starting_capital_ratio,
+            'starting_capital_pct': round(starting_capital_ratio * 100, 2),
+            'starting_capital_fiat': starting_cap_fiat,
+            'starting_capital_fiat_ratio': starting_cap_fiat_ratio,
+            'starting_capital_fiat_pct': round(starting_cap_fiat_ratio * 100, 2),
             'note': 'Simulated balances' if self._freqtrade.config['dry_run'] else ''
         }
 
@@ -545,12 +561,12 @@ class RPC:
                 order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
 
                 if order['side'] == 'buy':
-                    fully_canceled = self._freqtrade.handle_cancel_buy(
+                    fully_canceled = self._freqtrade.handle_cancel_enter(
                         trade, order, CANCEL_REASON['FORCE_SELL'])
 
                 if order['side'] == 'sell':
                     # Cancel order - so it is placed anew with a fresh price.
-                    self._freqtrade.handle_cancel_sell(trade, order, CANCEL_REASON['FORCE_SELL'])
+                    self._freqtrade.handle_cancel_exit(trade, order, CANCEL_REASON['FORCE_SELL'])
 
             if not fully_canceled:
                 # Get current rate and execute sell
@@ -563,7 +579,7 @@ class RPC:
         if self._freqtrade.state != State.RUNNING:
             raise RPCException('trader is not running')
 
-        with self._freqtrade._sell_lock:
+        with self._freqtrade._exit_lock:
             if trade_id == 'all':
                 # Execute sell for all open orders
                 for trade in Trade.get_open_trades():
@@ -625,7 +641,7 @@ class RPC:
         Handler for delete <id>.
         Delete the given trade and close eventually existing open orders.
         """
-        with self._freqtrade._sell_lock:
+        with self._freqtrade._exit_lock:
             c_count = 0
             trade = Trade.get_trades(trade_filter=[Trade.id == trade_id]).first()
             if not trade:

freqtrade/rpc/telegram.py

@@ -260,6 +260,50 @@ class Telegram(RPCHandler):
 
         return message
 
+    def compose_message(self, msg: Dict[str, Any], msg_type: RPCMessageType) -> str:
+
+        if msg_type == RPCMessageType.BUY:
+            message = self._format_buy_msg(msg)
+
+        elif msg_type in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
+            msg['message_side'] = 'buy' if msg_type == RPCMessageType.BUY_CANCEL else 'sell'
+            message = ("\N{WARNING SIGN} *{exchange}:* "
+                       "Cancelling open {message_side} Order for {pair} (#{trade_id}). "
+                       "Reason: {reason}.".format(**msg))
+
+        elif msg_type == RPCMessageType.BUY_FILL:
+            message = ("\N{LARGE CIRCLE} *{exchange}:* "
+                       "Buy order for {pair} (#{trade_id}) filled "
+                       "for {open_rate}.".format(**msg))
+        elif msg_type == RPCMessageType.SELL_FILL:
+            message = ("\N{LARGE CIRCLE} *{exchange}:* "
+                       "Sell order for {pair} (#{trade_id}) filled "
+                       "for {close_rate}.".format(**msg))
+        elif msg_type == RPCMessageType.SELL:
+            message = self._format_sell_msg(msg)
+        elif msg_type == RPCMessageType.PROTECTION_TRIGGER:
+            message = (
+                "*Protection* triggered due to {reason}. "
+                "`{pair}` will be locked until `{lock_end_time}`."
+            ).format(**msg)
+        elif msg_type == RPCMessageType.PROTECTION_TRIGGER_GLOBAL:
+            message = (
+                "*Protection* triggered due to {reason}. "
+                "*All pairs* will be locked until `{lock_end_time}`."
+            ).format(**msg)
+        elif msg_type == RPCMessageType.STATUS:
+            message = '*Status:* `{status}`'.format(**msg)
+
+        elif msg_type == RPCMessageType.WARNING:
+            message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
+
+        elif msg_type == RPCMessageType.STARTUP:
+            message = '{status}'.format(**msg)
+
+        else:
+            raise NotImplementedError('Unknown message type: {}'.format(msg_type))
+        return message
+
     def send_msg(self, msg: Dict[str, Any]) -> None:
         """ Send a message to telegram channel """
 
@@ -284,37 +328,7 @@ class Telegram(RPCHandler):
             # Notification disabled
             return
 
-        if msg_type == RPCMessageType.BUY:
+        message = self.compose_message(msg, msg_type)
-            message = self._format_buy_msg(msg)
-
-        elif msg_type in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
-            msg['message_side'] = 'buy' if msg_type == RPCMessageType.BUY_CANCEL else 'sell'
-            message = ("\N{WARNING SIGN} *{exchange}:* "
-                       "Cancelling open {message_side} Order for {pair} (#{trade_id}). "
-                       "Reason: {reason}.".format(**msg))
-
-        elif msg_type == RPCMessageType.BUY_FILL:
-            message = ("\N{LARGE CIRCLE} *{exchange}:* "
-                       "Buy order for {pair} (#{trade_id}) filled "
-                       "for {open_rate}.".format(**msg))
-        elif msg_type == RPCMessageType.SELL_FILL:
-            message = ("\N{LARGE CIRCLE} *{exchange}:* "
-                       "Sell order for {pair} (#{trade_id}) filled "
-                       "for {close_rate}.".format(**msg))
-        elif msg_type == RPCMessageType.SELL:
-            message = self._format_sell_msg(msg)
-
-        elif msg_type == RPCMessageType.STATUS:
-            message = '*Status:* `{status}`'.format(**msg)
-
-        elif msg_type == RPCMessageType.WARNING:
-            message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
-
-        elif msg_type == RPCMessageType.STARTUP:
-            message = '{status}'.format(**msg)
-
-        else:
-            raise NotImplementedError('Unknown message type: {}'.format(msg_type))
 
         self._send_msg(message, disable_notification=(noti == 'silent'))
 
@@ -603,12 +617,15 @@ class Telegram(RPCHandler):
 
             output = ''
             if self._config['dry_run']:
-                output += (
+                output += "*Warning:* Simulated balances in Dry Mode.\n"
-                    f"*Warning:* Simulated balances in Dry Mode.\n"
+
-                    "This mode is still experimental!\n"
+            output += ("Starting capital: "
-                    "Starting capital: "
+                       f"`{result['starting_capital']}` {self._config['stake_currency']}"
-                    f"`{self._config['dry_run_wallet']}` {self._config['stake_currency']}.\n"
+                       )
-                )
+            output += (f" `{result['starting_capital_fiat']}` "
+                       f"{self._config['fiat_display_currency']}.\n"
+                       ) if result['starting_capital_fiat'] > 0 else '.\n'
+
             total_dust_balance = 0
             total_dust_currencies = 0
             for curr in result['currencies']:
@@ -641,9 +658,12 @@ class Telegram(RPCHandler):
                     f"{round_coin_value(total_dust_balance, result['stake'], False)}`\n")
 
             output += ("\n*Estimated Value*:\n"
-                       f"\t`{result['stake']}: {result['total']: .8f}`\n"
+                       f"\t`{result['stake']}: "
+                       f"{round_coin_value(result['total'], result['stake'], False)}`"
+                       f" `({result['starting_capital_pct']}%)`\n"
                        f"\t`{result['symbol']}: "
-                       f"{round_coin_value(result['value'], result['symbol'], False)}`\n")
+                       f"{round_coin_value(result['value'], result['symbol'], False)}`"
+                       f" `({result['starting_capital_fiat_pct']}%)`\n")
             self._send_msg(output, reload_able=True, callback_path="update_balance",
                            query=update.callback_query)
         except RPCException as e:

freqtrade/strategy/__init__.py

@@ -3,5 +3,7 @@ from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_msecs, timefr
                                 timeframe_to_prev_date, timeframe_to_seconds)
 from freqtrade.strategy.hyper import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                       IntParameter, RealParameter)
+from freqtrade.strategy.informative_decorator import informative
 from freqtrade.strategy.interface import IStrategy
-from freqtrade.strategy.strategy_helper import merge_informative_pair, stoploss_from_open
+from freqtrade.strategy.strategy_helper import (merge_informative_pair, stoploss_from_absolute,
+                                                stoploss_from_open)

freqtrade/strategy/informative_decorator.py

@@ -0,0 +1,128 @@
+from typing import Any, Callable, NamedTuple, Optional, Union
+
+from pandas import DataFrame
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.strategy.strategy_helper import merge_informative_pair
+
+
+PopulateIndicators = Callable[[Any, DataFrame, dict], DataFrame]
+
+
+class InformativeData(NamedTuple):
+    asset: Optional[str]
+    timeframe: str
+    fmt: Union[str, Callable[[Any], str], None]
+    ffill: bool
+
+
+def informative(timeframe: str, asset: str = '',
+                fmt: Optional[Union[str, Callable[[Any], 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.
+    """
+    _asset = asset
+    _timeframe = timeframe
+    _fmt = fmt
+    _ffill = ffill
+
+    def decorator(fn: PopulateIndicators):
+        informative_pairs = getattr(fn, '_ft_informative', [])
+        informative_pairs.append(InformativeData(_asset, _timeframe, _fmt, _ffill))
+        setattr(fn, '_ft_informative', informative_pairs)
+        return fn
+    return decorator
+
+
+def _format_pair_name(config, pair: str) -> str:
+    return pair.format(stake_currency=config['stake_currency'],
+                       stake=config['stake_currency']).upper()
+
+
+def _create_and_merge_informative_pair(strategy, dataframe: DataFrame, metadata: dict,
+                                       inf_data: InformativeData,
+                                       populate_indicators: PopulateIndicators):
+    asset = inf_data.asset or ''
+    timeframe = inf_data.timeframe
+    fmt = inf_data.fmt
+    config = strategy.config
+
+    if asset:
+        # Insert stake currency if needed.
+        asset = _format_pair_name(config, asset)
+    else:
+        # Not specifying an asset will define informative dataframe for current pair.
+        asset = metadata['pair']
+
+    if '/' in asset:
+        base, quote = asset.split('/')
+    else:
+        # When futures are supported this may need reevaluation.
+        # base, quote = asset, ''
+        raise OperationalException('Not implemented.')
+
+    # Default format. This optimizes for the common case: informative pairs using same stake
+    # currency. When quote currency matches stake currency, column name will omit base currency.
+    # This allows easily reconfiguring strategy to use different base currency. In a rare case
+    # where it is desired to keep quote currency in column name at all times user should specify
+    # fmt='{base}_{quote}_{column}_{timeframe}' format or similar.
+    if not fmt:
+        fmt = '{column}_{timeframe}'                # Informatives of current pair
+        if inf_data.asset:
+            fmt = '{base}_{quote}_' + fmt           # Informatives of other pairs
+
+    inf_metadata = {'pair': asset, 'timeframe': timeframe}
+    inf_dataframe = strategy.dp.get_pair_dataframe(asset, timeframe)
+    inf_dataframe = populate_indicators(strategy, inf_dataframe, inf_metadata)
+
+    formatter: Any = None
+    if callable(fmt):
+        formatter = fmt             # A custom user-specified formatter function.
+    else:
+        formatter = fmt.format      # A default string formatter.
+
+    fmt_args = {
+        'BASE': base.upper(),
+        'QUOTE': quote.upper(),
+        'base': base.lower(),
+        'quote': quote.lower(),
+        'asset': asset,
+        'timeframe': timeframe,
+    }
+    inf_dataframe.rename(columns=lambda column: formatter(column=column, **fmt_args),
+                         inplace=True)
+
+    date_column = formatter(column='date', **fmt_args)
+    if date_column in dataframe.columns:
+        raise OperationalException(f'Duplicate column name {date_column} exists in '
+                                   f'dataframe! Ensure column names are unique!')
+    dataframe = merge_informative_pair(dataframe, inf_dataframe, strategy.timeframe, timeframe,
+                                       ffill=inf_data.ffill, append_timeframe=False,
+                                       date_column=date_column)
+    return dataframe

freqtrade/strategy/interface.py

@@ -19,6 +19,9 @@ from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
 from freqtrade.exchange.exchange import timeframe_to_next_date
 from freqtrade.persistence import PairLocks, Trade
 from freqtrade.strategy.hyper import HyperStrategyMixin
+from freqtrade.strategy.informative_decorator import (InformativeData, PopulateIndicators,
+                                                      _create_and_merge_informative_pair,
+                                                      _format_pair_name)
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from freqtrade.wallets import Wallets
 
@@ -118,8 +121,10 @@ class IStrategy(ABC, HyperStrategyMixin):
     # Class level variables (intentional) containing
     # the dataprovider (dp) (access to other candles, historic data, ...)
     # and wallets - access to the current balance.
-    dp: Optional[DataProvider] = None
+    dp: Optional[DataProvider]
     wallets: Optional[Wallets] = None
+    # Filled from configuration
+    stake_currency: str
     # container variable for strategy source code
     __source__: str = ''
 
@@ -132,6 +137,24 @@ class IStrategy(ABC, HyperStrategyMixin):
         self._last_candle_seen_per_pair: Dict[str, datetime] = {}
         super().__init__(config)
 
+        # Gather informative pairs from @informative-decorated methods.
+        self._ft_informative: List[Tuple[InformativeData, PopulateIndicators]] = []
+        for attr_name in dir(self.__class__):
+            cls_method = getattr(self.__class__, attr_name)
+            if not callable(cls_method):
+                continue
+            informative_data_list = getattr(cls_method, '_ft_informative', None)
+            if not isinstance(informative_data_list, list):
+                # Type check is required because mocker would return a mock object that evaluates to
+                # True, confusing this code.
+                continue
+            strategy_timeframe_minutes = timeframe_to_minutes(self.timeframe)
+            for informative_data in informative_data_list:
+                if timeframe_to_minutes(informative_data.timeframe) < strategy_timeframe_minutes:
+                    raise OperationalException('Informative timeframe must be equal or higher than '
+                                               'strategy timeframe!')
+                self._ft_informative.append((informative_data, cls_method))
+
     @abstractmethod
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
@@ -375,6 +398,23 @@ class IStrategy(ABC, HyperStrategyMixin):
 # END - Intended to be overridden by strategy
 ###
 
+    def gather_informative_pairs(self) -> ListPairsWithTimeframes:
+        """
+        Internal method which gathers all informative pairs (user or automatically defined).
+        """
+        informative_pairs = self.informative_pairs()
+        for inf_data, _ in self._ft_informative:
+            if inf_data.asset:
+                pair_tf = (_format_pair_name(self.config, inf_data.asset), inf_data.timeframe)
+                informative_pairs.append(pair_tf)
+            else:
+                if not self.dp:
+                    raise OperationalException('@informative decorator with unspecified asset '
+                                               'requires DataProvider instance.')
+                for pair in self.dp.current_whitelist():
+                    informative_pairs.append((pair, inf_data.timeframe))
+        return list(set(informative_pairs))
+
     def get_strategy_name(self) -> str:
         """
         Returns strategy class name
@@ -775,10 +815,11 @@ class IStrategy(ABC, HyperStrategyMixin):
         Does not run advise_buy or advise_sell!
         Used by optimize operations only, not during dry / live runs.
         Using .copy() to get a fresh copy of the dataframe for every strategy run.
+        Also copy on output to avoid PerformanceWarnings pandas 1.3.0 started to show.
         Has positive effects on memory usage for whatever reason - also when
         using only one strategy.
         """
-        return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair})
+        return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
                 for pair, pair_data in data.items()}
 
     def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -790,6 +831,12 @@ class IStrategy(ABC, HyperStrategyMixin):
         :return: a Dataframe with all mandatory indicators for the strategies
         """
         logger.debug(f"Populating indicators for pair {metadata.get('pair')}.")
+
+        # call populate_indicators_Nm() which were tagged with @informative decorator.
+        for inf_data, populate_fn in self._ft_informative:
+            dataframe = _create_and_merge_informative_pair(
+                self, dataframe, metadata, inf_data, populate_fn)
+
         if self._populate_fun_len == 2:
             warnings.warn("deprecated - check out the Sample strategy to see "
                           "the current function headers!", DeprecationWarning)

freqtrade/strategy/strategy_helper.py

@@ -4,7 +4,9 @@ from freqtrade.exchange import timeframe_to_minutes
 
 
 def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
-                           timeframe: str, timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
+                           timeframe: str, timeframe_inf: str, ffill: bool = True,
+                           append_timeframe: bool = True,
+                           date_column: str = 'date') -> pd.DataFrame:
     """
     Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
 
@@ -24,6 +26,8 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     :param timeframe: Timeframe of the original pair sample.
     :param timeframe_inf: Timeframe of the informative pair sample.
     :param ffill: Forwardfill missing values - optional but usually required
+    :param append_timeframe: Rename columns by appending timeframe.
+    :param date_column: A custom date column name.
     :return: Merged dataframe
     :raise: ValueError if the secondary timeframe is shorter than the dataframe timeframe
     """
@@ -32,25 +36,29 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     minutes = timeframe_to_minutes(timeframe)
     if minutes == minutes_inf:
         # No need to forwardshift if the timeframes are identical
-        informative['date_merge'] = informative["date"]
+        informative['date_merge'] = informative[date_column]
     elif minutes < minutes_inf:
         # Subtract "small" timeframe so merging is not delayed by 1 small candle
         # Detailed explanation in https://github.com/freqtrade/freqtrade/issues/4073
         informative['date_merge'] = (
-            informative["date"] + pd.to_timedelta(minutes_inf, 'm') - pd.to_timedelta(minutes, 'm')
+            informative[date_column] + pd.to_timedelta(minutes_inf, 'm') -
+            pd.to_timedelta(minutes, 'm')
         )
     else:
         raise ValueError("Tried to merge a faster timeframe to a slower timeframe."
                          "This would create new rows, and can throw off your regular indicators.")
 
     # Rename columns to be unique
-    informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
+    date_merge = 'date_merge'
+    if append_timeframe:
+        date_merge = f'date_merge_{timeframe_inf}'
+        informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
 
     # Combine the 2 dataframes
     # all indicators on the informative sample MUST be calculated before this point
     dataframe = pd.merge(dataframe, informative, left_on='date',
-                         right_on=f'date_merge_{timeframe_inf}', how='left')
+                         right_on=date_merge, how='left')
-    dataframe = dataframe.drop(f'date_merge_{timeframe_inf}', axis=1)
+    dataframe = dataframe.drop(date_merge, axis=1)
 
     if ffill:
         dataframe = dataframe.ffill()
@@ -83,3 +91,28 @@ def stoploss_from_open(open_relative_stop: float, current_profit: float) -> floa
 
     # negative stoploss values indicate the requested stop price is higher than the current price
     return max(stoploss, 0.0)
+
+
+def stoploss_from_absolute(stop_rate: float, current_rate: float) -> float:
+    """
+    Given current price and desired stop price, return a stop loss value that is relative to current
+    price.
+
+    The requested stop can be positive for a stop above the open price, or negative for
+    a stop below the open price. The return value is always >= 0.
+
+    Returns 0 if the resulting stop price would be above the current price.
+
+    :param stop_rate: Stop loss price.
+    :param current_rate: Current asset price.
+    :return: Positive stop loss value relative to current price
+    """
+
+    # formula is undefined for current_rate 0, return maximum value
+    if current_rate == 0:
+        return 1
+
+    stoploss = 1 - (stop_rate / current_rate)
+
+    # negative stoploss values indicate the requested stop price is higher than the current price
+    return max(stoploss, 0.0)

freqtrade/templates/base_config.json.j2

@@ -1,3 +1,10 @@
+{%set volume_pairlist = '{
+            "method": "VolumePairList",
+            "number_assets": 20,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }' %}
 {
     "max_open_trades": {{ max_open_trades }},
     "stake_currency": "{{ stake_currency }}",
@@ -29,7 +36,7 @@
     },
     {{ exchange | indent(4) }},
     "pairlists": [
-        {"method": "StaticPairList"}
+        {{ '{"method": "StaticPairList"}' if exchange_name == 'bittrex' else  volume_pairlist }}
     ],
     "edge": {
         "enabled": false,

freqtrade/templates/base_hyperopt.py.j2

@@ -1,137 +0,0 @@
-# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
-
-# --- Do not remove these libs ---
-from functools import reduce
-from typing import Any, Callable, Dict, List
-
-import numpy as np  # noqa
-import pandas as pd  # noqa
-from pandas import DataFrame
-from skopt.space import Categorical, Dimension, Integer, Real  # noqa
-
-from freqtrade.optimize.hyperopt_interface import IHyperOpt
-
-# --------------------------------
-# Add your lib to import here
-import talib.abstract as ta  # noqa
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-
-
-class {{ hyperopt }}(IHyperOpt):
-    """
-    This is a Hyperopt template to get you started.
-
-    More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
-
-    You should:
-    - Add any lib you need to build your hyperopt.
-
-    You must keep:
-    - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
-
-    The methods roi_space, generate_roi_table and stoploss_space are not required
-    and are provided by default.
-    However, you may override them if you need 'roi' and 'stoploss' spaces that
-    differ from the defaults offered by Freqtrade.
-    Sample implementation of these methods will be copied to `user_data/hyperopts` when
-    creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
-    or is available online under the following URL:
-    https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
-    """
-
-    @staticmethod
-    def indicator_space() -> List[Dimension]:
-        """
-        Define your Hyperopt space for searching buy strategy parameters.
-        """
-        return [
-            {{ buy_space | indent(12) }}
-        ]
-
-    @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:
-            """
-            Buy strategy Hyperopt will build and use.
-            """
-            conditions = []
-
-            # GUARDS AND TRENDS
-            {{ buy_guards | indent(12) }}
-
-            # 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']
-                    ))
-                if params['trigger'] == 'sar_reversal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['close'], dataframe['sar']
-                    ))
-
-            # Check that the candle had volume
-            conditions.append(dataframe['volume'] > 0)
-
-            if conditions:
-                dataframe.loc[
-                    reduce(lambda x, y: x & y, conditions),
-                    'buy'] = 1
-
-            return dataframe
-
-        return populate_buy_trend
-
-    @staticmethod
-    def sell_indicator_space() -> List[Dimension]:
-        """
-        Define your Hyperopt space for searching sell strategy parameters.
-        """
-        return [
-            {{ sell_space | indent(12) }}
-        ]
-
-    @staticmethod
-    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
-        """
-        Define the sell strategy parameters to be used by Hyperopt.
-        """
-        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
-            """
-            Sell strategy Hyperopt will build and use.
-            """
-            conditions = []
-
-            # GUARDS AND TRENDS
-            {{ sell_guards | indent(12) }}
-
-            # TRIGGERS
-            if 'sell-trigger' in params:
-                if params['sell-trigger'] == 'sell-bb_upper':
-                    conditions.append(dataframe['close'] > dataframe['bb_upperband'])
-                if params['sell-trigger'] == 'sell-macd_cross_signal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['macdsignal'], dataframe['macd']
-                    ))
-                if params['sell-trigger'] == 'sell-sar_reversal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['sar'], dataframe['close']
-                    ))
-
-            # Check that the candle had volume
-            conditions.append(dataframe['volume'] > 0)
-
-            if conditions:
-                dataframe.loc[
-                    reduce(lambda x, y: x & y, conditions),
-                    'sell'] = 1
-
-            return dataframe
-
-        return populate_sell_trend
-

freqtrade/templates/sample_hyperopt.py

@@ -1,174 +0,0 @@
-# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
-# isort: skip_file
-
-# --- Do not remove these libs ---
-from functools import reduce
-from typing import Any, Callable, Dict, List
-
-import numpy as np  # noqa
-import pandas as pd  # noqa
-from pandas import DataFrame
-from skopt.space import Categorical, Dimension, Integer, Real  # noqa
-
-from freqtrade.optimize.hyperopt_interface import IHyperOpt
-
-# --------------------------------
-# Add your lib to import here
-import talib.abstract as ta  # noqa
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-
-
-class SampleHyperOpt(IHyperOpt):
-    """
-    This is a sample Hyperopt to inspire you.
-
-    More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
-
-    You should:
-    - Rename the class name to some unique name.
-    - Add any methods you want to build your hyperopt.
-    - Add any lib you need to build your hyperopt.
-
-    An easier way to get a new hyperopt file is by using
-    `freqtrade new-hyperopt --hyperopt MyCoolHyperopt`.
-
-    You must keep:
-    - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
-
-    The methods roi_space, generate_roi_table and stoploss_space are not required
-    and are provided by default.
-    However, you may override them if you need 'roi' and 'stoploss' spaces that
-    differ from the defaults offered by Freqtrade.
-    Sample implementation of these methods will be copied to `user_data/hyperopts` when
-    creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
-    or is available online under the following URL:
-    https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
-    """
-
-    @staticmethod
-    def indicator_space() -> List[Dimension]:
-        """
-        Define your Hyperopt space for searching buy strategy parameters.
-        """
-        return [
-            Integer(10, 25, name='mfi-value'),
-            Integer(15, 45, name='fastd-value'),
-            Integer(20, 50, name='adx-value'),
-            Integer(20, 40, name='rsi-value'),
-            Categorical([True, False], name='mfi-enabled'),
-            Categorical([True, False], name='fastd-enabled'),
-            Categorical([True, False], name='adx-enabled'),
-            Categorical([True, False], name='rsi-enabled'),
-            Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
-        ]
-
-    @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:
-            """
-            Buy strategy Hyperopt will build and use.
-            """
-            conditions = []
-
-            # GUARDS AND TRENDS
-            if 'mfi-enabled' in params and params['mfi-enabled']:
-                conditions.append(dataframe['mfi'] < params['mfi-value'])
-            if 'fastd-enabled' in params and params['fastd-enabled']:
-                conditions.append(dataframe['fastd'] < params['fastd-value'])
-            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']
-                    ))
-                if params['trigger'] == 'sar_reversal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['close'], dataframe['sar']
-                    ))
-
-            # 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
-
-    @staticmethod
-    def sell_indicator_space() -> List[Dimension]:
-        """
-        Define your Hyperopt space for searching sell strategy parameters.
-        """
-        return [
-            Integer(75, 100, name='sell-mfi-value'),
-            Integer(50, 100, name='sell-fastd-value'),
-            Integer(50, 100, name='sell-adx-value'),
-            Integer(60, 100, name='sell-rsi-value'),
-            Categorical([True, False], name='sell-mfi-enabled'),
-            Categorical([True, False], name='sell-fastd-enabled'),
-            Categorical([True, False], name='sell-adx-enabled'),
-            Categorical([True, False], name='sell-rsi-enabled'),
-            Categorical(['sell-bb_upper',
-                         'sell-macd_cross_signal',
-                         'sell-sar_reversal'], name='sell-trigger')
-        ]
-
-    @staticmethod
-    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
-        """
-        Define the sell strategy parameters to be used by Hyperopt.
-        """
-        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
-            """
-            Sell strategy Hyperopt will build and use.
-            """
-            conditions = []
-
-            # GUARDS AND TRENDS
-            if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
-                conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
-            if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
-                conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
-            if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
-                conditions.append(dataframe['adx'] < params['sell-adx-value'])
-            if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
-                conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
-
-            # TRIGGERS
-            if 'sell-trigger' in params:
-                if params['sell-trigger'] == 'sell-bb_upper':
-                    conditions.append(dataframe['close'] > dataframe['bb_upperband'])
-                if params['sell-trigger'] == 'sell-macd_cross_signal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['macdsignal'], dataframe['macd']
-                    ))
-                if params['sell-trigger'] == 'sell-sar_reversal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['sar'], dataframe['close']
-                    ))
-
-            # Check that volume is not 0
-            conditions.append(dataframe['volume'] > 0)
-
-            if conditions:
-                dataframe.loc[
-                    reduce(lambda x, y: x & y, conditions),
-                    'sell'] = 1
-
-            return dataframe
-
-        return populate_sell_trend

freqtrade/templates/sample_hyperopt_advanced.py

@@ -1,269 +0,0 @@
-# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
-# isort: skip_file
-# --- Do not remove these libs ---
-from functools import reduce
-from typing import Any, Callable, Dict, List
-
-import numpy as np  # noqa
-import pandas as pd  # noqa
-from pandas import DataFrame
-from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real  # noqa
-
-from freqtrade.optimize.hyperopt_interface import IHyperOpt
-
-# --------------------------------
-# Add your lib to import here
-import talib.abstract as ta  # noqa
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-
-
-class AdvancedSampleHyperOpt(IHyperOpt):
-    """
-    This is a sample hyperopt to inspire you.
-    Feel free to customize it.
-
-    More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
-
-    You should:
-    - Rename the class name to some unique name.
-    - Add any methods you want to build your hyperopt.
-    - Add any lib you need to build your hyperopt.
-
-    You must keep:
-    - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
-
-    The methods roi_space, generate_roi_table and stoploss_space are not required
-    and are provided by default.
-    However, you may override them if you need the
-    'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
-
-    This sample illustrates how to override these methods.
-    """
-    @staticmethod
-    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
-        This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
-        """
-        dataframe['adx'] = ta.ADX(dataframe)
-        macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['macdsignal'] = macd['macdsignal']
-        dataframe['mfi'] = ta.MFI(dataframe)
-        dataframe['rsi'] = ta.RSI(dataframe)
-        stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-        # Bollinger bands
-        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-        dataframe['bb_lowerband'] = bollinger['lower']
-        dataframe['bb_upperband'] = bollinger['upper']
-        dataframe['sar'] = ta.SAR(dataframe)
-        return dataframe
-
-    @staticmethod
-    def indicator_space() -> List[Dimension]:
-        """
-        Define your Hyperopt space for searching buy strategy parameters.
-        """
-        return [
-            Integer(10, 25, name='mfi-value'),
-            Integer(15, 45, name='fastd-value'),
-            Integer(20, 50, name='adx-value'),
-            Integer(20, 40, name='rsi-value'),
-            Categorical([True, False], name='mfi-enabled'),
-            Categorical([True, False], name='fastd-enabled'),
-            Categorical([True, False], name='adx-enabled'),
-            Categorical([True, False], name='rsi-enabled'),
-            Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
-        ]
-
-    @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:
-            """
-            Buy strategy Hyperopt will build and use
-            """
-            conditions = []
-            # GUARDS AND TRENDS
-            if 'mfi-enabled' in params and params['mfi-enabled']:
-                conditions.append(dataframe['mfi'] < params['mfi-value'])
-            if 'fastd-enabled' in params and params['fastd-enabled']:
-                conditions.append(dataframe['fastd'] < params['fastd-value'])
-            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']
-                    ))
-                if params['trigger'] == 'sar_reversal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['close'], dataframe['sar']
-                    ))
-
-            # 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
-
-    @staticmethod
-    def sell_indicator_space() -> List[Dimension]:
-        """
-        Define your Hyperopt space for searching sell strategy parameters.
-        """
-        return [
-            Integer(75, 100, name='sell-mfi-value'),
-            Integer(50, 100, name='sell-fastd-value'),
-            Integer(50, 100, name='sell-adx-value'),
-            Integer(60, 100, name='sell-rsi-value'),
-            Categorical([True, False], name='sell-mfi-enabled'),
-            Categorical([True, False], name='sell-fastd-enabled'),
-            Categorical([True, False], name='sell-adx-enabled'),
-            Categorical([True, False], name='sell-rsi-enabled'),
-            Categorical(['sell-bb_upper',
-                         'sell-macd_cross_signal',
-                         'sell-sar_reversal'], name='sell-trigger')
-        ]
-
-    @staticmethod
-    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
-        """
-        Define the sell strategy parameters to be used by hyperopt
-        """
-        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
-            """
-            Sell strategy Hyperopt will build and use
-            """
-            # print(params)
-            conditions = []
-            # GUARDS AND TRENDS
-            if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
-                conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
-            if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
-                conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
-            if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
-                conditions.append(dataframe['adx'] < params['sell-adx-value'])
-            if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
-                conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
-
-            # TRIGGERS
-            if 'sell-trigger' in params:
-                if params['sell-trigger'] == 'sell-bb_upper':
-                    conditions.append(dataframe['close'] > dataframe['bb_upperband'])
-                if params['sell-trigger'] == 'sell-macd_cross_signal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['macdsignal'], dataframe['macd']
-                    ))
-                if params['sell-trigger'] == 'sell-sar_reversal':
-                    conditions.append(qtpylib.crossed_above(
-                        dataframe['sar'], dataframe['close']
-                    ))
-
-            # Check that volume is not 0
-            conditions.append(dataframe['volume'] > 0)
-
-            if conditions:
-                dataframe.loc[
-                    reduce(lambda x, y: x & y, conditions),
-                    'sell'] = 1
-
-            return dataframe
-
-        return populate_sell_trend
-
-    @staticmethod
-    def generate_roi_table(params: Dict) -> Dict[int, float]:
-        """
-        Generate the ROI table that will be used by Hyperopt
-
-        This implementation generates the default legacy Freqtrade ROI tables.
-
-        Change it if you need different number of steps in the generated
-        ROI tables or other structure of the ROI tables.
-
-        Please keep it aligned with parameters in the 'roi' optimization
-        hyperspace defined by the roi_space method.
-        """
-        roi_table = {}
-        roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
-        roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
-        roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
-        roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
-
-        return roi_table
-
-    @staticmethod
-    def roi_space() -> List[Dimension]:
-        """
-        Values to search for each ROI steps
-
-        Override it if you need some different ranges for the parameters in the
-        'roi' optimization hyperspace.
-
-        Please keep it aligned with the implementation of the
-        generate_roi_table method.
-        """
-        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'),
-        ]
-
-    @staticmethod
-    def stoploss_space() -> List[Dimension]:
-        """
-        Stoploss Value to search
-
-        Override it if you need some different range for the parameter in the
-        'stoploss' optimization hyperspace.
-        """
-        return [
-            SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
-        ]
-
-    @staticmethod
-    def trailing_space() -> List[Dimension]:
-        """
-        Create a trailing stoploss space.
-
-        You may override it in your custom Hyperopt class.
-        """
-        return [
-            # It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
-            # is used. Otherwise hyperopt will vary other parameters that won't have effect if
-            # trailing_stop is set False.
-            # This parameter is included into the hyperspace dimensions rather than assigning
-            # it explicitly in the code in order to have it printed in the results along with
-            # other 'trailing' hyperspace parameters.
-            Categorical([True], name='trailing_stop'),
-
-            SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
-
-            # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
-            # so this intermediate parameter is used as the value of the difference between
-            # them. The value of the 'trailing_stop_positive_offset' is constructed in the
-            # generate_trailing_params() method.
-            # This is similar to the hyperspace dimensions used for constructing the ROI tables.
-            SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
-
-            Categorical([True, False], name='trailing_only_offset_is_reached'),
-        ]

freqtrade/templates/subtemplates/exchange_binance.j2

@@ -8,34 +8,8 @@
         "rateLimit": 200
     },
     "pair_whitelist": [
-        "ALGO/BTC",
-        "ATOM/BTC",
-        "BAT/BTC",
-        "BCH/BTC",
-        "BRD/BTC",
-        "EOS/BTC",
-        "ETH/BTC",
-        "IOTA/BTC",
-        "LINK/BTC",
-        "LTC/BTC",
-        "NEO/BTC",
-        "NXS/BTC",
-        "XMR/BTC",
-        "XRP/BTC",
-        "XTZ/BTC"
     ],
     "pair_blacklist": [
-        "BNB/BTC",
+        "BNB/.*"
-        "BNB/BUSD",
-        "BNB/ETH",
-        "BNB/EUR",
-        "BNB/NGN",
-        "BNB/PAX",
-        "BNB/RUB",
-        "BNB/TRY",
-        "BNB/TUSD",
-        "BNB/USDC",
-        "BNB/USDS",
-        "BNB/USDT",
     ]
 }

freqtrade/templates/subtemplates/exchange_bittrex.j2

@@ -15,16 +15,6 @@
         "rateLimit": 500
     },
     "pair_whitelist": [
-        "ETH/BTC",
-        "LTC/BTC",
-        "ETC/BTC",
-        "DASH/BTC",
-        "ZEC/BTC",
-        "XLM/BTC",
-        "XRP/BTC",
-        "TRX/BTC",
-        "ADA/BTC",
-        "XMR/BTC"
     ],
     "pair_blacklist": [
     ]

freqtrade/templates/subtemplates/exchange_kraken.j2

@@ -7,28 +7,10 @@
     "ccxt_async_config": {
         "enableRateLimit": true,
         "rateLimit": 1000
+        // Enable the below for downoading data.
+        //"rateLimit": 3100
     },
     "pair_whitelist": [
-        "ADA/EUR",
-        "ATOM/EUR",
-        "BAT/EUR",
-        "BCH/EUR",
-        "BTC/EUR",
-        "DAI/EUR",
-        "DASH/EUR",
-        "EOS/EUR",
-        "ETC/EUR",
-        "ETH/EUR",
-        "LINK/EUR",
-        "LTC/EUR",
-        "QTUM/EUR",
-        "REP/EUR",
-        "WAVES/EUR",
-        "XLM/EUR",
-        "XMR/EUR",
-        "XRP/EUR",
-        "XTZ/EUR",
-        "ZEC/EUR"
     ],
     "pair_blacklist": [
 

freqtrade/templates/subtemplates/exchange_kucoin.j2

@@ -0,0 +1,18 @@
+"exchange": {
+    "name": "{{ exchange_name | lower }}",
+    "key": "{{ exchange_key }}",
+    "secret": "{{ exchange_secret }}",
+    "password": "{{ exchange_key_password }}",
+    "ccxt_config": {
+        "enableRateLimit": true
+        "rateLimit": 200
+    },
+    "ccxt_async_config": {
+        "enableRateLimit": true,
+        "rateLimit": 200
+    },
+    "pair_whitelist": [
+    ],
+    "pair_blacklist": [
+    ]
+}

freqtrade/templates/subtemplates/hyperopt_buy_guards_full.j2

@@ -1,8 +0,0 @@
-if params.get('mfi-enabled'):
-    conditions.append(dataframe['mfi'] < params['mfi-value'])
-if params.get('fastd-enabled'):
-    conditions.append(dataframe['fastd'] < params['fastd-value'])
-if params.get('adx-enabled'):
-    conditions.append(dataframe['adx'] > params['adx-value'])
-if params.get('rsi-enabled'):
-    conditions.append(dataframe['rsi'] < params['rsi-value'])

freqtrade/templates/subtemplates/hyperopt_buy_guards_minimal.j2

@@ -1,2 +0,0 @@
-if params.get('rsi-enabled'):
-    conditions.append(dataframe['rsi'] < params['rsi-value'])

freqtrade/templates/subtemplates/hyperopt_buy_space_full.j2

@@ -1,9 +0,0 @@
-Integer(10, 25, name='mfi-value'),
-Integer(15, 45, name='fastd-value'),
-Integer(20, 50, name='adx-value'),
-Integer(20, 40, name='rsi-value'),
-Categorical([True, False], name='mfi-enabled'),
-Categorical([True, False], name='fastd-enabled'),
-Categorical([True, False], name='adx-enabled'),
-Categorical([True, False], name='rsi-enabled'),
-Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

freqtrade/templates/subtemplates/hyperopt_buy_space_minimal.j2

@@ -1,3 +0,0 @@
-Integer(20, 40, name='rsi-value'),
-Categorical([True, False], name='rsi-enabled'),
-Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

freqtrade/templates/subtemplates/hyperopt_sell_guards_full.j2

@@ -1,8 +0,0 @@
-if params.get('sell-mfi-enabled'):
-    conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
-if params.get('sell-fastd-enabled'):
-    conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
-if params.get('sell-adx-enabled'):
-    conditions.append(dataframe['adx'] < params['sell-adx-value'])
-if params.get('sell-rsi-enabled'):
-    conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

freqtrade/templates/subtemplates/hyperopt_sell_guards_minimal.j2

@@ -1,2 +0,0 @@
-if params.get('sell-rsi-enabled'):
-    conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

freqtrade/templates/subtemplates/hyperopt_sell_space_full.j2

@@ -1,11 +0,0 @@
-Integer(75, 100, name='sell-mfi-value'),
-Integer(50, 100, name='sell-fastd-value'),
-Integer(50, 100, name='sell-adx-value'),
-Integer(60, 100, name='sell-rsi-value'),
-Categorical([True, False], name='sell-mfi-enabled'),
-Categorical([True, False], name='sell-fastd-enabled'),
-Categorical([True, False], name='sell-adx-enabled'),
-Categorical([True, False], name='sell-rsi-enabled'),
-Categorical(['sell-bb_upper',
-             'sell-macd_cross_signal',
-             'sell-sar_reversal'], name='sell-trigger')

freqtrade/templates/subtemplates/hyperopt_sell_space_minimal.j2

@@ -1,5 +0,0 @@
-Integer(60, 100, name='sell-rsi-value'),
-Categorical([True, False], name='sell-rsi-enabled'),
-Categorical(['sell-bb_upper',
-             'sell-macd_cross_signal',
-             'sell-sar_reversal'], name='sell-trigger')

mkdocs.yml

@@ -23,10 +23,10 @@ nav:
     - Hyperopt: hyperopt.md
     - Utility Sub-commands: utils.md
     - Plotting: plotting.md
+    - Exchange-specific Notes: exchanges.md
     - Data Analysis:
         - Jupyter Notebooks: data-analysis.md
         - Strategy analysis: strategy_analysis_example.md
-    - Exchange-specific Notes: exchanges.md
     - Advanced Topics:
         - Advanced Post-installation Tasks: advanced-setup.md
         - Edge Positioning: edge.md

requirements-dev.txt

@@ -8,18 +8,20 @@ flake8==3.9.2
 flake8-type-annotations==0.1.0
 flake8-tidy-imports==4.4.1
 mypy==0.910
-pytest==6.2.4
+pytest==6.2.5
 pytest-asyncio==0.15.1
 pytest-cov==2.12.1
 pytest-mock==3.6.1
 pytest-random-order==1.0.4
 isort==5.9.3
+# For datetime mocking
+time-machine==2.4.0
 
 # Convert jupyter notebooks to markdown documents
-nbconvert==6.1.0
+nbconvert==6.2.0
 
 # mypy types
 types-cachetools==4.2.0
 types-filelock==0.1.5
-types-requests==2.25.6
+types-requests==2.25.9
 types-tabulate==0.8.2

requirements-hyperopt.txt

@@ -8,4 +8,4 @@ scikit-optimize==0.8.1
 filelock==3.0.12
 joblib==1.0.1
 psutil==5.8.0
-progressbar2==3.53.1
+progressbar2==3.53.3

requirements-plot.txt

@@ -1,5 +1,5 @@
 # Include all requirements to run the bot.
 -r requirements.txt
 
-plotly==5.2.1
+plotly==5.3.1
 

requirements.txt

@@ -1,16 +1,17 @@
 numpy==1.21.2
-pandas==1.3.2
+pandas==1.3.3
+pandas-ta==0.3.14b
 
-ccxt==1.55.28
+ccxt==1.57.3
 # Pin cryptography for now due to rust build errors with piwheels
-cryptography==3.4.7
+cryptography==3.4.8
 aiohttp==3.7.4.post0
-SQLAlchemy==1.4.23
+SQLAlchemy==1.4.25
 python-telegram-bot==13.7
 arrow==1.1.1
 cachetools==4.2.2
 requests==2.26.0
-urllib3==1.26.6
+urllib3==1.26.7
 wrapt==1.12.1
 jsonschema==3.2.0
 TA-Lib==0.4.21
@@ -31,7 +32,7 @@ python-rapidjson==1.4
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.68.0
+fastapi==0.68.1
 uvicorn==0.15.0
 pyjwt==2.1.0
 aiofiles==0.7.0

setup.py

@@ -54,6 +54,7 @@ setup(
         'wrapt',
         'jsonschema',
         'TA-Lib',
+        'pandas-ta',
         'technical',
         'tabulate',
         'pycoingecko',

setup.sh

@@ -62,7 +62,7 @@ function updateenv() {
     then
         REQUIREMENTS_PLOT="-r requirements-plot.txt"
     fi
-    if [ "${SYS_ARCH}" == "armv7l" ]; then
+    if [ "${SYS_ARCH}" == "armv7l" ] || [ "${SYS_ARCH}" == "armv6l" ]; then
         echo "Detected Raspberry, installing cython, skipping hyperopt installation."
         ${PYTHON} -m pip install --upgrade cython
     else
@@ -95,19 +95,7 @@ function install_talib() {
         return
     fi
 
-    cd build_helpers
+    cd build_helpers && ./install_ta-lib.sh && cd ..
-    tar zxvf ta-lib-0.4.0-src.tar.gz
-    cd ta-lib
-    sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h
-    ./configure --prefix=/usr/local
-    make
-    sudo make install
-    if [ -x "$(command -v apt-get)" ]; then
-        echo "Updating library path using ldconfig"
-        sudo ldconfig
-    fi
-    cd .. && rm -rf ./ta-lib/
-    cd ..
 }
 
 function install_mac_newer_python_dependencies() {    
@@ -119,6 +107,7 @@ function install_mac_newer_python_dependencies() {
         echo "-------------------------"
         brew install hdf5
     fi
+    export HDF5_DIR=$(brew --prefix)
 
     if [ ! $(brew --prefix --installed c-blosc 2>/dev/null) ]
     then
@@ -127,6 +116,7 @@ function install_mac_newer_python_dependencies() {
         echo "-------------------------"
         brew install c-blosc
     fi    
+    export CBLOSC_DIR=$(brew --prefix)
 }
 
 # Install bot MacOS

tests/commands/test_commands.py

@@ -10,10 +10,10 @@ import pytest
 
 from freqtrade.commands import (start_convert_data, start_create_userdir, start_download_data,
                                 start_hyperopt_list, start_hyperopt_show, start_install_ui,
-                                start_list_data, start_list_exchanges, start_list_hyperopts,
+                                start_list_data, start_list_exchanges, start_list_markets,
-                                start_list_markets, start_list_strategies, start_list_timeframes,
+                                start_list_strategies, start_list_timeframes, start_new_strategy,
-                                start_new_hyperopt, start_new_strategy, start_show_trades,
+                                start_show_trades, start_test_pairlist, start_trading,
-                                start_test_pairlist, start_trading, start_webserver)
+                                start_webserver)
 from freqtrade.commands.deploy_commands import (clean_ui_subdir, download_and_install_ui,
                                                 get_ui_download_url, read_ui_version)
 from freqtrade.configuration import setup_utils_configuration
@@ -32,8 +32,6 @@ def test_setup_utils_configuration():
     config = setup_utils_configuration(get_args(args), RunMode.OTHER)
     assert "exchange" in config
     assert config['dry_run'] is True
-    assert config['exchange']['key'] == ''
-    assert config['exchange']['secret'] == ''
 
 
 def test_start_trading_fail(mocker, caplog):
@@ -519,37 +517,6 @@ def test_start_new_strategy_no_arg(mocker, caplog):
         start_new_strategy(get_args(args))
 
 
-def test_start_new_hyperopt(mocker, caplog):
-    wt_mock = mocker.patch.object(Path, "write_text", MagicMock())
-    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
-
-    args = [
-        "new-hyperopt",
-        "--hyperopt",
-        "CoolNewhyperopt"
-    ]
-    start_new_hyperopt(get_args(args))
-
-    assert wt_mock.call_count == 1
-    assert "CoolNewhyperopt" in wt_mock.call_args_list[0][0][0]
-    assert log_has_re("Writing hyperopt to .*", caplog)
-
-    mocker.patch('freqtrade.commands.deploy_commands.setup_utils_configuration')
-    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
-    with pytest.raises(OperationalException,
-                       match=r".* already exists. Please choose another Hyperopt Name\."):
-        start_new_hyperopt(get_args(args))
-
-
-def test_start_new_hyperopt_no_arg(mocker):
-    args = [
-        "new-hyperopt",
-    ]
-    with pytest.raises(OperationalException,
-                       match="`new-hyperopt` requires --hyperopt to be set."):
-        start_new_hyperopt(get_args(args))
-
-
 def test_start_install_ui(mocker):
     clean_mock = mocker.patch('freqtrade.commands.deploy_commands.clean_ui_subdir')
     get_url_mock = mocker.patch('freqtrade.commands.deploy_commands.get_ui_download_url',
@@ -824,37 +791,20 @@ def test_start_list_strategies(mocker, caplog, capsys):
     assert "legacy_strategy_v1.py" in captured.out
     assert "StrategyTestV2" in captured.out
 
-
+    # Test color output
-def test_start_list_hyperopts(mocker, caplog, capsys):
-
     args = [
-        "list-hyperopts",
+        "list-strategies",
-        "--hyperopt-path",
+        "--strategy-path",
-        str(Path(__file__).parent.parent / "optimize" / "hyperopts"),
+        str(Path(__file__).parent.parent / "strategy" / "strats"),
-        "-1"
     ]
     pargs = get_args(args)
     # pargs['config'] = None
-    start_list_hyperopts(pargs)
+    start_list_strategies(pargs)
     captured = capsys.readouterr()
-    assert "TestHyperoptLegacy" not in captured.out
+    assert "TestStrategyLegacyV1" in captured.out
-    assert "legacy_hyperopt.py" not in captured.out
+    assert "legacy_strategy_v1.py" in captured.out
-    assert "HyperoptTestSepFile" in captured.out
+    assert "StrategyTestV2" in captured.out
-    assert "test_hyperopt.py" not in captured.out
+    assert "LOAD FAILED" in captured.out
-
-    # Test regular output
-    args = [
-        "list-hyperopts",
-        "--hyperopt-path",
-        str(Path(__file__).parent.parent / "optimize" / "hyperopts"),
-    ]
-    pargs = get_args(args)
-    # pargs['config'] = None
-    start_list_hyperopts(pargs)
-    captured = capsys.readouterr()
-    assert "TestHyperoptLegacy" not in captured.out
-    assert "legacy_hyperopt.py" not in captured.out
-    assert "HyperoptTestSepFile" in captured.out
 
 
 def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):