Merge branch 'develop' into pr/Axel-CH/5347

README.md

@@ -37,7 +37,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
 Exchanges confirmed working by the community:
 
 - [X] [Bitvavo](https://bitvavo.com/)
-- [X] [Kukoin](https://www.kucoin.com/)
+- [X] [Kucoin](https://www.kucoin.com/)
 
 ## Documentation
 

build_helpers/publish_docker_arm64.sh

@@ -74,7 +74,5 @@ fi
 
 docker images
 
-if [ $? -ne 0 ]; then
-    echo "failed building image"
-    return 1
-fi
+# Cleanup old images from arm64 node.
+docker image prune -a --force --filter "until=24h"

docs/bot-basics.md

@@ -35,7 +35,7 @@ By default, loop runs every few seconds (`internals.process_throttle_secs`) and
   * Calls `check_buy_timeout()` strategy callback for open buy orders.
   * Calls `check_sell_timeout()` strategy callback for open sell orders.
 * Verifies existing positions and eventually places sell orders.
-  * Considers stoploss, ROI and sell-signal.
+  * Considers stoploss, ROI and sell-signal, `custom_sell()` and `custom_stoploss()`.
   * Determine sell-price based on `ask_strategy` configuration setting or by using the `custom_exit_price()` callback.
   * Before a sell order is placed, `confirm_trade_exit()` strategy callback is called.
 * Check if trade-slots are still available (if `max_open_trades` is reached).
@@ -53,9 +53,10 @@ This loop will be repeated again and again until the bot is stopped.
 * Load historic data for configured pairlist.
 * Calls `bot_loop_start()` once.
 * Calculate indicators (calls `populate_indicators()` once per pair).
-* Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair)
-* Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy)
+* Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair).
 * Loops per candle simulating entry and exit points.
+  * Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
+  * Call `custom_stoploss()` and `custom_sell()` to find custom exit points.
 * Generate backtest report output
 
 !!! Note

docs/developer.md

@@ -240,11 +240,18 @@ The `IProtection` parent class provides a helper method for this in `calculate_l
 !!! Note
     This section is a Work in Progress and is not a complete guide on how to test a new exchange with Freqtrade.
 
+!!! Note
+    Make sure to use an up-to-date version of CCXT before running any of the below tests.
+    You can get the latest version of ccxt by running `pip install -U ccxt` with activated virtual environment.
+    Native docker is not supported for these tests, however the available dev-container will support all required actions and eventually necessary changes.
+
 Most exchanges supported by CCXT should work out of the box.
 
 To quickly test the public endpoints of an exchange, add a configuration for your exchange to `test_ccxt_compat.py` and run these tests with `pytest --longrun tests/exchange/test_ccxt_compat.py`.
 Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
 
+Also try to use `freqtrade download-data` for an extended timerange and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
+
 ### Stoploss On Exchange
 
 Check if the new exchange supports Stoploss on Exchange orders through their API.

docs/hyperopt.md

@@ -253,7 +253,7 @@ We continue to define hyperoptable parameters:
 class MyAwesomeStrategy(IStrategy):
     buy_adx = DecimalParameter(20, 40, decimals=1, default=30.1, space="buy")
     buy_rsi = IntParameter(20, 40, default=30, space="buy")
-    buy_adx_enabled = CategoricalParameter([True, False], default=True, space="buy")
+    buy_adx_enabled = BooleanParameter(default=True, space="buy")
     buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
     buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
 ```
@@ -316,6 +316,7 @@ There are four parameter types each suited for different purposes.
 * `DecimalParameter` - defines a floating point parameter with a limited number of decimals (default 3). Should be preferred instead of `RealParameter` in most cases.
 * `RealParameter` - defines a floating point parameter with upper and lower boundaries and no precision limit. Rarely used as it creates a space with a near infinite number of possibilities.
 * `CategoricalParameter` - defines a parameter with a predetermined number of choices.
+* `BooleanParameter` - Shorthand for `CategoricalParameter([True, False])` - great for "enable" parameters.
 
 !!! Tip "Disabling parameter optimization"
     Each parameter takes two boolean parameters:
@@ -326,7 +327,7 @@ There are four parameter types each suited for different purposes.
 !!! Warning
     Hyperoptable parameters cannot be used in `populate_indicators` - as hyperopt does not recalculate indicators for each epoch, so the starting value would be used in this case.
 
-### Optimizing an indicator parameter
+## Optimizing an indicator parameter
 
 Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy.
 
@@ -336,8 +337,8 @@ from functools import reduce
 
 import talib.abstract as ta
 
-from freqtrade.strategy import IStrategy
-from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, 
+                                IStrategy, IntParameter)
 import freqtrade.vendor.qtpylib.indicators as qtpylib
 
 class MyAwesomeStrategy(IStrategy):
@@ -413,6 +414,98 @@ While this strategy is most likely too simple to provide consistent profit, it s
     While this may slow down the hyperopt startup speed, the overall performance will increase as the Hyperopt execution itself may pick the same value for multiple epochs (changing other values).
     You should however try to use space ranges as small as possible. Every new column will require more memory, and every possibility hyperopt can try will increase the search space.
 
+## Optimizing protections
+
+Freqtrade can also optimize protections. How you optimize protections is up to you, and the following should be considered as example only.
+
+The strategy will simply need to define the "protections" entry as property returning a list of protection configurations.
+
+``` python
+from pandas import DataFrame
+from functools import reduce
+
+import talib.abstract as ta
+
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, 
+                                IStrategy, IntParameter)
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+class MyAwesomeStrategy(IStrategy):
+    stoploss = -0.05
+    timeframe = '15m'
+    # Define the parameter spaces
+    cooldown_lookback = IntParameter(2, 48, default=5, space="protection", optimize=True)
+    stop_duration = IntParameter(12, 200, default=5, space="protection", optimize=True)
+    use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True)
+
+
+    @property
+    def protections(self):
+        prot = []
+
+        prot.append({
+            "method": "CooldownPeriod",
+            "stop_duration_candles": self.cooldown_lookback.value
+        })
+        if self.use_stop_protection.value:
+            prot.append({
+                "method": "StoplossGuard",
+                "lookback_period_candles": 24 * 3,
+                "trade_limit": 4,
+                "stop_duration_candles": self.stop_duration.value,
+                "only_per_pair": False
+            })
+
+        return protection
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ...
+        
+```
+
+You can then run hyperopt as follows:
+`freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy --spaces protection`
+
+!!! Note
+    The protection space is not part of the default space, and is only available with the Parameters Hyperopt interface, not with the legacy hyperopt interface (which required separate hyperopt files).
+    Freqtrade will also automatically change the "--enable-protections" flag if the protection space is selected.
+
+!!! Warning
+    If protections are defined as property, entries from the configuration will be ignored.
+    It is therefore recommended to not define protections in the configuration.
+
+### Migrating from previous property setups
+
+A migration from a previous setup is pretty simple, and can be accomplished by converting the protections entry to a property.
+In simple terms, the following configuration will be converted to the below.
+
+``` python
+class MyAwesomeStrategy(IStrategy):
+    protections = [
+        {
+            "method": "CooldownPeriod",
+            "stop_duration_candles": 4
+        }
+    ]
+```
+
+Result
+
+``` python
+class MyAwesomeStrategy(IStrategy):
+    
+    @property
+    def protections(self):
+        return [
+            {
+                "method": "CooldownPeriod",
+                "stop_duration_candles": 4
+            }
+        ]
+```
+
+You will then obviously also change potential interesting entries to parameters to allow hyper-optimization.
+
 ## Loss-functions
 
 Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.

docs/includes/pairlists.md

@@ -58,7 +58,7 @@ This option must be configured along with `exchange.skip_pair_validation` in the
 
 When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
 
-When used on the leading position of the chain of Pairlist Handlers, it does not consider `pair_whitelist` configuration setting, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
+When used in the leading position of the chain of Pairlist Handlers, the `pair_whitelist` configuration setting is ignored. Instead, `VolumePairList` selects the top assets from all available markets with matching stake-currency on the exchange.
 
 The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
 The pairlist cache (`refresh_period`) on `VolumePairList` is only applicable to generating pairlists.
@@ -74,11 +74,14 @@ Filtering instances (not the first position in the list) will not apply any cach
         "method": "VolumePairList",
         "number_assets": 20,
         "sort_key": "quoteVolume",
+        "min_value": 0,
         "refresh_period": 1800
     }
 ],
 ```
 
+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` 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:
@@ -89,6 +92,7 @@ For convenience `lookback_days` can be specified, which will imply that 1d candl
         "method": "VolumePairList",
         "number_assets": 20,
         "sort_key": "quoteVolume",
+        "min_value": 0,
         "refresh_period": 86400,
         "lookback_days": 7
     }
@@ -109,6 +113,7 @@ More sophisticated approach can be used, by using `lookback_timeframe` for candl
         "method": "VolumePairList",
         "number_assets": 20,
         "sort_key": "quoteVolume",
+        "min_value": 0,
         "refresh_period": 3600,
         "lookback_timeframe": "1h",
         "lookback_period": 72

docs/includes/protections.md

@@ -15,6 +15,10 @@ All protection end times are rounded up to the next candle to avoid sudden, unex
 !!! Note "Backtesting"
     Protections are supported by backtesting and hyperopt, but must be explicitly enabled by using the `--enable-protections` flag.
 
+!!! Warning "Setting protections from the configuration"
+    Setting protections from the configuration via `"protections": [],` key should be considered deprecated and will be removed in a future version.
+    It is also no longer guaranteed that your protections apply to the strategy in cases where the strategy defines [protections as property](hyperopt.md#optimizing-protections).
+
 ### Available Protections
 
 * [`StoplossGuard`](#stoploss-guard) Stop trading if a certain amount of stoploss occurred within a certain time window.
@@ -47,15 +51,17 @@ This applies across all pairs, unless `only_per_pair` is set to true, which will
 The below example stops trading for all pairs for 4 candles after the last trade if the bot hit stoploss 4 times within the last 24 candles.
 
 ``` python
-protections = [
-    {
-        "method": "StoplossGuard",
-        "lookback_period_candles": 24,
-        "trade_limit": 4,
-        "stop_duration_candles": 4,
-        "only_per_pair": False
-    }
-]
+@property
+def protections(self):
+    return [
+        {
+            "method": "StoplossGuard",
+            "lookback_period_candles": 24,
+            "trade_limit": 4,
+            "stop_duration_candles": 4,
+            "only_per_pair": False
+        }
+    ]
 ```
 
 !!! Note
@@ -69,15 +75,17 @@ protections = [
 The below sample stops trading for 12 candles if max-drawdown is > 20% considering all pairs - with a minimum of `trade_limit` trades - within the last 48 candles. If desired, `lookback_period` and/or `stop_duration` can be used.
 
 ``` python
-protections = [
-      {
-        "method": "MaxDrawdown",
-        "lookback_period_candles": 48,
-        "trade_limit": 20,
-        "stop_duration_candles": 12,
-        "max_allowed_drawdown": 0.2
-      },
-]
+@property
+def protections(self):
+    return  [
+        {
+            "method": "MaxDrawdown",
+            "lookback_period_candles": 48,
+            "trade_limit": 20,
+            "stop_duration_candles": 12,
+            "max_allowed_drawdown": 0.2
+        },
+    ]
 ```
 
 #### Low Profit Pairs
@@ -88,15 +96,17 @@ If that ratio is below `required_profit`, that pair will be locked for `stop_dur
 The below example will stop trading a pair for 60 minutes if the pair does not have a required profit of 2% (and a minimum of 2 trades) within the last 6 candles.
 
 ``` python
-protections = [
-    {
-        "method": "LowProfitPairs",
-        "lookback_period_candles": 6,
-        "trade_limit": 2,
-        "stop_duration": 60,
-        "required_profit": 0.02
-    }
-]
+@property
+def protections(self):
+    return [
+        {
+            "method": "LowProfitPairs",
+            "lookback_period_candles": 6,
+            "trade_limit": 2,
+            "stop_duration": 60,
+            "required_profit": 0.02
+        }
+    ]
 ```
 
 #### Cooldown Period
@@ -106,12 +116,14 @@ protections = [
 The below example will stop trading a pair for 2 candles after closing a trade, allowing this pair to "cool down".
 
 ``` python
-protections = [
-    {
-        "method": "CooldownPeriod",
-        "stop_duration_candles": 2
-    }
-]
+@property
+def protections(self):
+    return  [
+        {
+            "method": "CooldownPeriod",
+            "stop_duration_candles": 2
+        }
+    ]
 ```
 
 !!! Note
@@ -136,39 +148,42 @@ from freqtrade.strategy import IStrategy
 
 class AwesomeStrategy(IStrategy)
     timeframe = '1h'
-    protections = [
-        {
-            "method": "CooldownPeriod",
-            "stop_duration_candles": 5
-        },
-        {
-            "method": "MaxDrawdown",
-            "lookback_period_candles": 48,
-            "trade_limit": 20,
-            "stop_duration_candles": 4,
-            "max_allowed_drawdown": 0.2
-        },
-        {
-            "method": "StoplossGuard",
-            "lookback_period_candles": 24,
-            "trade_limit": 4,
-            "stop_duration_candles": 2,
-            "only_per_pair": False
-        },
-        {
-            "method": "LowProfitPairs",
-            "lookback_period_candles": 6,
-            "trade_limit": 2,
-            "stop_duration_candles": 60,
-            "required_profit": 0.02
-        },
-        {
-            "method": "LowProfitPairs",
-            "lookback_period_candles": 24,
-            "trade_limit": 4,
-            "stop_duration_candles": 2,
-            "required_profit": 0.01
-        }
-    ]
+    
+    @property
+    def protections(self):
+        return [
+            {
+                "method": "CooldownPeriod",
+                "stop_duration_candles": 5
+            },
+            {
+                "method": "MaxDrawdown",
+                "lookback_period_candles": 48,
+                "trade_limit": 20,
+                "stop_duration_candles": 4,
+                "max_allowed_drawdown": 0.2
+            },
+            {
+                "method": "StoplossGuard",
+                "lookback_period_candles": 24,
+                "trade_limit": 4,
+                "stop_duration_candles": 2,
+                "only_per_pair": False
+            },
+            {
+                "method": "LowProfitPairs",
+                "lookback_period_candles": 6,
+                "trade_limit": 2,
+                "stop_duration_candles": 60,
+                "required_profit": 0.02
+            },
+            {
+                "method": "LowProfitPairs",
+                "lookback_period_candles": 24,
+                "trade_limit": 4,
+                "stop_duration_candles": 2,
+                "required_profit": 0.01
+            }
+        ]
     # ...
 ```

docs/index.md

@@ -47,7 +47,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
 Exchanges confirmed working by the community:
 
 - [X] [Bitvavo](https://bitvavo.com/)
-- [X] [Kukoin](https://www.kucoin.com/)
+- [X] [Kucoin](https://www.kucoin.com/)
 
 ## Requirements
 

freqtrade/commands/build_config_commands.py

@@ -193,7 +193,7 @@ def deploy_new_config(config_path: Path, selections: Dict[str, Any]) -> None:
         selections['exchange'] = render_template(
             templatefile=f"subtemplates/exchange_{exchange_template}.j2",
             arguments=selections
-            )
+        )
     except TemplateNotFound:
         selections['exchange'] = render_template(
             templatefile="subtemplates/exchange_generic.j2",

freqtrade/commands/cli_options.py

@@ -218,7 +218,7 @@ AVAILABLE_CLI_OPTIONS = {
     "spaces": Arg(
         '--spaces',
         help='Specify which parameters to hyperopt. Space-separated list.',
-        choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'default'],
+        choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'protection', 'default'],
         nargs='+',
         default='default',
     ),

freqtrade/commands/deploy_commands.py

@@ -38,15 +38,15 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
     indicators = render_template_with_fallback(
         templatefile=f"subtemplates/indicators_{subtemplate}.j2",
         templatefallbackfile=f"subtemplates/indicators_{fallback}.j2",
-        )
+    )
     buy_trend = render_template_with_fallback(
         templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",
         templatefallbackfile=f"subtemplates/buy_trend_{fallback}.j2",
-        )
+    )
     sell_trend = render_template_with_fallback(
         templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",
         templatefallbackfile=f"subtemplates/sell_trend_{fallback}.j2",
-        )
+    )
     plot_config = render_template_with_fallback(
         templatefile=f"subtemplates/plot_config_{subtemplate}.j2",
         templatefallbackfile=f"subtemplates/plot_config_{fallback}.j2",
@@ -97,19 +97,19 @@ def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: st
     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,

freqtrade/commands/hyperopt_commands.py

@@ -1,6 +1,6 @@
 import logging
 from operator import itemgetter
-from typing import Any, Dict, List
+from typing import Any, Dict
 
 from colorama import init as colorama_init
 
@@ -28,30 +28,12 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
     no_details = config.get('hyperopt_list_no_details', False)
     no_header = False
 
-    filteroptions = {
-        'only_best': config.get('hyperopt_list_best', False),
-        'only_profitable': config.get('hyperopt_list_profitable', False),
-        'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
-        'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
-        'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
-        'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
-        'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
-        'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
-        'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
-        'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
-        'filter_min_objective': config.get('hyperopt_list_min_objective', None),
-        'filter_max_objective': config.get('hyperopt_list_max_objective', None),
-    }
-
     results_file = get_latest_hyperopt_file(
         config['user_data_dir'] / 'hyperopt_results',
         config.get('hyperoptexportfilename'))
 
     # Previous evaluations
-    epochs = HyperoptTools.load_previous_results(results_file)
-    total_epochs = len(epochs)
-
-    epochs = hyperopt_filter_epochs(epochs, filteroptions)
+    epochs, total_epochs = HyperoptTools.load_filtered_results(results_file, config)
 
     if print_colorized:
         colorama_init(autoreset=True)
@@ -59,7 +41,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
     if not export_csv:
         try:
             print(HyperoptTools.get_result_table(config, epochs, total_epochs,
-                                                 not filteroptions['only_best'],
+                                                 not config.get('hyperopt_list_best', False),
                                                  print_colorized, 0))
         except KeyboardInterrupt:
             print('User interrupted..')
@@ -71,7 +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 filteroptions['only_best'], export_csv
+            config, epochs, total_epochs, not config.get('hyperopt_list_best', False), export_csv
         )
 
 
@@ -91,26 +73,9 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 
     n = config.get('hyperopt_show_index', -1)
 
-    filteroptions = {
-        'only_best': config.get('hyperopt_list_best', False),
-        'only_profitable': config.get('hyperopt_list_profitable', False),
-        'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
-        'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
-        'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
-        'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
-        'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
-        'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
-        'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
-        'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
-        'filter_min_objective': config.get('hyperopt_list_min_objective', None),
-        'filter_max_objective': config.get('hyperopt_list_max_objective', None)
-    }
-
     # Previous evaluations
-    epochs = HyperoptTools.load_previous_results(results_file)
-    total_epochs = len(epochs)
+    epochs, total_epochs = HyperoptTools.load_filtered_results(results_file, config)
 
-    epochs = hyperopt_filter_epochs(epochs, filteroptions)
     filtered_epochs = len(epochs)
 
     if n > filtered_epochs:
@@ -137,138 +102,3 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 
         HyperoptTools.show_epoch_details(val, total_epochs, print_json, no_header,
                                          header_str="Epoch details")
-
-
-def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
-    """
-    Filter our items from the list of hyperopt results
-    TODO: after 2021.5 remove all "legacy" mode queries.
-    """
-    if filteroptions['only_best']:
-        epochs = [x for x in epochs if x['is_best']]
-    if filteroptions['only_profitable']:
-        epochs = [x for x in epochs if x['results_metrics'].get(
-            'profit', x['results_metrics'].get('profit_total', 0)) > 0]
-
-    epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
-
-    epochs = _hyperopt_filter_epochs_duration(epochs, filteroptions)
-
-    epochs = _hyperopt_filter_epochs_profit(epochs, filteroptions)
-
-    epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions)
-
-    logger.info(f"{len(epochs)} " +
-                ("best " if filteroptions['only_best'] else "") +
-                ("profitable " if filteroptions['only_profitable'] else "") +
-                "epochs found.")
-    return epochs
-
-
-def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
-    """
-    Filter epochs with trade-counts > trades
-    """
-    return [
-        x for x in epochs
-        if x['results_metrics'].get(
-            'trade_count', x['results_metrics'].get('total_trades', 0)
-        ) > trade_count
-    ]
-
-
-def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
-
-    if filteroptions['filter_min_trades'] > 0:
-        epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
-
-    if filteroptions['filter_max_trades'] > 0:
-        epochs = [
-            x for x in epochs
-            if x['results_metrics'].get(
-                'trade_count', x['results_metrics'].get('total_trades')
-                ) < filteroptions['filter_max_trades']
-        ]
-    return epochs
-
-
-def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
-
-    def get_duration_value(x):
-        # Duration in minutes ...
-        if 'duration' in x['results_metrics']:
-            return x['results_metrics']['duration']
-        else:
-            # New mode
-            if 'holding_avg_s' in x['results_metrics']:
-                avg = x['results_metrics']['holding_avg_s']
-                return avg // 60
-            raise OperationalException(
-                "Holding-average not available. Please omit the filter on average time, "
-                "or rerun hyperopt with this version")
-
-    if filteroptions['filter_min_avg_time'] is not None:
-        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
-        epochs = [
-            x for x in epochs
-            if get_duration_value(x) > filteroptions['filter_min_avg_time']
-        ]
-    if filteroptions['filter_max_avg_time'] is not None:
-        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
-        epochs = [
-            x for x in epochs
-            if get_duration_value(x) < filteroptions['filter_max_avg_time']
-        ]
-
-    return epochs
-
-
-def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
-
-    if filteroptions['filter_min_avg_profit'] is not None:
-        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
-        epochs = [
-            x for x in epochs
-            if x['results_metrics'].get(
-                'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
-            ) > filteroptions['filter_min_avg_profit']
-        ]
-    if filteroptions['filter_max_avg_profit'] is not None:
-        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
-        epochs = [
-            x for x in epochs
-            if x['results_metrics'].get(
-                'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
-                ) < filteroptions['filter_max_avg_profit']
-        ]
-    if filteroptions['filter_min_total_profit'] is not None:
-        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
-        epochs = [
-            x for x in epochs
-            if x['results_metrics'].get(
-                'profit', x['results_metrics'].get('profit_total_abs', 0)
-                ) > filteroptions['filter_min_total_profit']
-        ]
-    if filteroptions['filter_max_total_profit'] is not None:
-        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
-        epochs = [
-            x for x in epochs
-            if x['results_metrics'].get(
-                'profit', x['results_metrics'].get('profit_total_abs', 0)
-                ) < filteroptions['filter_max_total_profit']
-        ]
-    return epochs
-
-
-def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
-
-    if filteroptions['filter_min_objective'] is not None:
-        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
-
-        epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
-    if filteroptions['filter_max_objective'] is not None:
-        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
-
-        epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
-
-    return epochs

freqtrade/configuration/check_exchange.py

@@ -51,10 +51,10 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
 
     if not is_exchange_known_ccxt(exchange):
         raise OperationalException(
-                f'Exchange "{exchange}" is not known to the ccxt library '
-                f'and therefore not available for the bot.\n'
-                f'The following exchanges are available for Freqtrade: '
-                f'{", ".join(available_exchanges())}'
+            f'Exchange "{exchange}" is not known to the ccxt library '
+            f'and therefore not available for the bot.\n'
+            f'The following exchanges are available for Freqtrade: '
+            f'{", ".join(available_exchanges())}'
         )
 
     valid, reason = validate_exchange(exchange)

freqtrade/configuration/config_validation.py

@@ -115,7 +115,7 @@ def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
     if conf.get('stoploss') == 0.0:
         raise OperationalException(
             'The config stoploss needs to be different from 0 to avoid problems with sell orders.'
-            )
+        )
     # Skip if trailing stoploss is not activated
     if not conf.get('trailing_stop', False):
         return
@@ -180,7 +180,7 @@ def _validate_protections(conf: Dict[str, Any]) -> None:
             raise OperationalException(
                 "Protections must specify either `stop_duration` or `stop_duration_candles`.\n"
                 f"Please fix the protection {prot.get('method')}"
-                )
+            )
 
         if ('lookback_period' in prot and 'lookback_period_candles' in prot):
             raise OperationalException(

freqtrade/configuration/deprecated_settings.py

@@ -108,5 +108,8 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
             raise OperationalException(
                 "Both 'timeframe' and 'ticker_interval' detected."
                 "Please remove 'ticker_interval' from your configuration to continue operating."
-                )
+            )
         config['timeframe'] = config['ticker_interval']
+
+    if 'protections' in config:
+        logger.warning("DEPRECATED: Setting 'protections' in the configuration is deprecated.")

freqtrade/constants.py

@@ -280,7 +280,7 @@ CONF_SCHEMA = {
                             'type': 'string',
                             'enum': TELEGRAM_SETTING_OPTIONS,
                             'default': 'off'
-                            },
+                        },
                     }
                 },
                 'reload': {'type': 'boolean'},

freqtrade/edge/edge_positioning.py

@@ -151,7 +151,7 @@ class Edge:
         # Fake run-mode to Edge
         prior_rm = self.config['runmode']
         self.config['runmode'] = RunMode.EDGE
-        preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
+        preprocessed = self.strategy.advise_all_indicators(data)
         self.config['runmode'] = prior_rm
 
         # Print timeframe
@@ -231,12 +231,12 @@ class Edge:
                     'Minimum expectancy and minimum winrate are met only for %s,'
                     ' so other pairs are filtered out.',
                     self._final_pairs
-                    )
+                )
             else:
                 logger.info(
                     'Edge removed all pairs as no pair with minimum expectancy '
                     'and minimum winrate was found !'
-                    )
+                )
 
         return self._final_pairs
 
@@ -247,7 +247,7 @@ class Edge:
         final = []
         for pair, info in self._cached_pairs.items():
             if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
-                 info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)):
+                    info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)):
                 final.append({
                     'Pair': pair,
                     'Winrate': info.winrate,

freqtrade/exchange/exchange.py

@@ -618,6 +618,8 @@ class Exchange:
         if self.exchange_has('fetchL2OrderBook'):
             ob = self.fetch_l2_order_book(pair, 20)
             ob_type = 'asks' if side == 'buy' else 'bids'
+            slippage = 0.05
+            max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
 
             remaining_amount = amount
             filled_amount = 0
@@ -626,7 +628,9 @@ class Exchange:
                 book_entry_coin_volume = book_entry[1]
                 if remaining_amount > 0:
                     if remaining_amount < book_entry_coin_volume:
+                        # Orderbook at this slot bigger than remaining amount
                         filled_amount += remaining_amount * book_entry_price
+                        break
                     else:
                         filled_amount += book_entry_coin_volume * book_entry_price
                     remaining_amount -= book_entry_coin_volume
@@ -635,7 +639,14 @@ class Exchange:
             else:
                 # If remaining_amount wasn't consumed completely (break was not called)
                 filled_amount += remaining_amount * book_entry_price
-            forecast_avg_filled_price = filled_amount / amount
+            forecast_avg_filled_price = max(filled_amount, 0) / amount
+            # Limit max. slippage to specified value
+            if side == 'buy':
+                forecast_avg_filled_price = min(forecast_avg_filled_price, max_slippage_val)
+
+            else:
+                forecast_avg_filled_price = max(forecast_avg_filled_price, max_slippage_val)
+
             return self.price_to_precision(pair, forecast_avg_filled_price)
 
         return rate

freqtrade/main.py

@@ -44,7 +44,7 @@ def main(sysargv: List[str] = None) -> None:
                 "as `freqtrade trade [options...]`.\n"
                 "To see the full list of options available, please use "
                 "`freqtrade --help` or `freqtrade <command> --help`."
-                )
+            )
 
     except SystemExit as e:
         return_code = e

freqtrade/optimize/backtesting.py

@@ -130,6 +130,9 @@ class Backtesting:
         self.abort = False
 
     def __del__(self):
+        self.cleanup()
+
+    def cleanup(self):
         LoggingMixin.show_output = True
         PairLocks.use_db = True
         Trade.use_db = True
@@ -146,6 +149,8 @@ class Backtesting:
         # since a "perfect" stoploss-sell is assumed anyway
         # And the regular "stoploss" function would not apply to that case
         self.strategy.order_types['stoploss_on_exchange'] = False
+
+    def _load_protections(self, strategy: IStrategy):
         if self.config.get('enable_protections', False):
             conf = self.config
             if hasattr(strategy, 'protections'):
@@ -194,6 +199,7 @@ class Backtesting:
         Trade.reset_trades()
         self.rejected_trades = 0
         self.dataprovider.clear_cache()
+        self._load_protections(self.strategy)
 
     def check_abort(self):
         """
@@ -212,7 +218,7 @@ class Backtesting:
         """
         # Every change to this headers list must evaluate further usages of the resulting tuple
         # and eventually change the constants for indexes at the top
-        headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
+        headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag']
         data: Dict = {}
         self.progress.init_step(BacktestState.CONVERT, len(processed))
 
@@ -220,13 +226,10 @@ class Backtesting:
         for pair, pair_data in processed.items():
             self.check_abort()
             self.progress.increment()
-            has_buy_tag = 'buy_tag' in pair_data
-            headers = headers + ['buy_tag'] if has_buy_tag else headers
             if not pair_data.empty:
                 pair_data.loc[:, 'buy'] = 0  # cleanup if buy_signal is exist
                 pair_data.loc[:, 'sell'] = 0  # cleanup if sell_signal is exist
-                if has_buy_tag:
-                    pair_data.loc[:, 'buy_tag'] = None  # cleanup if buy_tag is exist
+                pair_data.loc[:, 'buy_tag'] = None  # cleanup if buy_tag is exist
 
             df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
@@ -237,14 +240,13 @@ class Backtesting:
             # from the previous candle
             df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
             df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
-            if has_buy_tag:
-                df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
-
-            df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
+            df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
 
             # Update dataprovider cache
             self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
 
+            df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
+
             # Convert from Pandas to list for performance reasons
             # (Looping Pandas is slow.)
             data[pair] = df_analyzed[headers].values.tolist()
@@ -317,14 +319,14 @@ class Backtesting:
             return sell_row[OPEN_IDX]
 
     def _get_sell_trade_entry(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_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX],
+                                         sell_candle_time, sell_row[BUY_IDX],
                                          sell_row[SELL_IDX],
                                          low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
 
         if sell.sell_flag:
-            trade.close_date = sell_row[DATE_IDX].to_pydatetime()
+            trade.close_date = sell_candle_time
             trade.sell_reason = sell.sell_reason
             trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
             closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
@@ -336,7 +338,7 @@ class Backtesting:
                     rate=closerate,
                     time_in_force=time_in_force,
                     sell_reason=sell.sell_reason,
-                    current_time=sell_row[DATE_IDX].to_pydatetime()):
+                    current_time=sell_candle_time):
                 return None
 
             trade.close(closerate, show_msg=False)
@@ -460,6 +462,8 @@ class Backtesting:
             for i, pair in enumerate(data):
                 row_index = indexes[pair]
                 try:
+                    # Row is treated as "current incomplete candle".
+                    # Buy / sell signals are shifted by 1 to compensate for this.
                     row = data[pair][row_index]
                 except IndexError:
                     # missing Data for one pair at the end.
@@ -471,8 +475,8 @@ class Backtesting:
                     continue
 
                 row_index += 1
-                self.dataprovider._set_dataframe_max_index(row_index)
                 indexes[pair] = row_index
+                self.dataprovider._set_dataframe_max_index(row_index)
 
                 # without positionstacking, we can only have one open trade per pair.
                 # max_open_trades must be respected
@@ -496,7 +500,7 @@ class Backtesting:
                         open_trades[pair].append(trade)
                         LocalTrade.add_bt_trade(trade)
 
-                for trade in open_trades[pair]:
+                for trade in list(open_trades[pair]):
                     # also check the buying candle for sell conditions.
                     trade_entry = self._get_sell_trade_entry(trade, row)
                     # Sell occurred
@@ -527,7 +531,8 @@ class Backtesting:
             'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
         }
 
-    def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
+    def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
+                              timerange: TimeRange):
         self.progress.init_step(BacktestState.ANALYZE, 0)
 
         logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@@ -546,7 +551,7 @@ class Backtesting:
             max_open_trades = 0
 
         # need to reprocess data every time to populate signals
-        preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
+        preprocessed = self.strategy.advise_all_indicators(data)
 
         # Trim startup period from analyzed dataframe
         preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)

freqtrade/optimize/hyperopt.py

@@ -66,6 +66,7 @@ class Hyperopt:
     def __init__(self, config: Dict[str, Any]) -> None:
         self.buy_space: List[Dimension] = []
         self.sell_space: List[Dimension] = []
+        self.protection_space: List[Dimension] = []
         self.roi_space: List[Dimension] = []
         self.stoploss_space: List[Dimension] = []
         self.trailing_space: List[Dimension] = []
@@ -191,6 +192,8 @@ class Hyperopt:
             result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
         if HyperoptTools.has_space(self.config, 'sell'):
             result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
+        if HyperoptTools.has_space(self.config, 'protection'):
+            result['protection'] = {p.name: params.get(p.name) for p in self.protection_space}
         if HyperoptTools.has_space(self.config, 'roi'):
             result['roi'] = {str(k): v for k, v in
                              self.custom_hyperopt.generate_roi_table(params).items()}
@@ -241,6 +244,12 @@ class Hyperopt:
         """
         Assign the dimensions in the hyperoptimization space.
         """
+        if self.auto_hyperopt and 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.
+            self.config['enable_protections'] = True
+            self.protection_space = self.custom_hyperopt.protection_space()
 
         if HyperoptTools.has_space(self.config, 'buy'):
             logger.debug("Hyperopt has 'buy' space")
@@ -261,8 +270,8 @@ class Hyperopt:
         if HyperoptTools.has_space(self.config, 'trailing'):
             logger.debug("Hyperopt has 'trailing' space")
             self.trailing_space = self.custom_hyperopt.trailing_space()
-        self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
-                           self.stoploss_space + self.trailing_space)
+        self.dimensions = (self.buy_space + self.sell_space + self.protection_space
+                           + self.roi_space + self.stoploss_space + self.trailing_space)
 
     def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
         """
@@ -282,6 +291,12 @@ class Hyperopt:
             self.backtesting.strategy.advise_sell = (  # type: ignore
                 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'):
+                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
                 self.custom_hyperopt.generate_roi_table(params_dict))
@@ -379,7 +394,7 @@ class Hyperopt:
         data, timerange = self.backtesting.load_bt_data()
         logger.info("Dataload complete. Calculating indicators")
 
-        preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
+        preprocessed = self.backtesting.strategy.advise_all_indicators(data)
 
         # Trim startup period from analyzed dataframe to get correct dates for output.
         processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
@@ -444,9 +459,9 @@ class Hyperopt:
                         ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
                     ]
                 with progressbar.ProgressBar(
-                         max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
-                         widgets=widgets
-                     ) as pbar:
+                    max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
+                    widgets=widgets
+                ) as pbar:
                     EVALS = ceil(self.total_epochs / jobs)
                     for i in range(EVALS):
                         # Correct the number of epochs to be processed for the last

freqtrade/optimize/hyperopt_auto.py

@@ -73,6 +73,9 @@ class HyperOptAuto(IHyperOpt):
     def sell_indicator_space(self) -> List['Dimension']:
         return self._get_indicator_space('sell', 'sell_indicator_space')
 
+    def protection_space(self) -> List['Dimension']:
+        return self._get_indicator_space('protection', 'indicator_space')
+
     def generate_roi_table(self, params: Dict) -> Dict[int, float]:
         return self._get_func('generate_roi_table')(params)
 

freqtrade/optimize/hyperopt_epoch_filters.py

@@ -0,0 +1,128 @@
+import logging
+from typing import List
+
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def hyperopt_filter_epochs(epochs: List, filteroptions: dict, log: bool = True) -> List:
+    """
+    Filter our items from the list of hyperopt results
+    """
+    if filteroptions['only_best']:
+        epochs = [x for x in epochs if x['is_best']]
+    if filteroptions['only_profitable']:
+        epochs = [x for x in epochs
+                  if x['results_metrics'].get('profit_total', 0) > 0]
+
+    epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
+
+    epochs = _hyperopt_filter_epochs_duration(epochs, filteroptions)
+
+    epochs = _hyperopt_filter_epochs_profit(epochs, filteroptions)
+
+    epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions)
+    if log:
+        logger.info(f"{len(epochs)} " +
+                    ("best " if filteroptions['only_best'] else "") +
+                    ("profitable " if filteroptions['only_profitable'] else "") +
+                    "epochs found.")
+    return epochs
+
+
+def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
+    """
+    Filter epochs with trade-counts > trades
+    """
+    return [
+        x for x in epochs if x['results_metrics'].get('total_trades', 0) > trade_count
+    ]
+
+
+def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
+
+    if filteroptions['filter_min_trades'] > 0:
+        epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
+
+    if filteroptions['filter_max_trades'] > 0:
+        epochs = [
+            x for x in epochs
+            if x['results_metrics'].get('total_trades') < filteroptions['filter_max_trades']
+        ]
+    return epochs
+
+
+def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
+
+    def get_duration_value(x):
+        # Duration in minutes ...
+        if 'holding_avg_s' in x['results_metrics']:
+            avg = x['results_metrics']['holding_avg_s']
+            return avg // 60
+        raise OperationalException(
+            "Holding-average not available. Please omit the filter on average time, "
+            "or rerun hyperopt with this version")
+
+    if filteroptions['filter_min_avg_time'] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x for x in epochs
+            if get_duration_value(x) > filteroptions['filter_min_avg_time']
+        ]
+    if filteroptions['filter_max_avg_time'] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x for x in epochs
+            if get_duration_value(x) < filteroptions['filter_max_avg_time']
+        ]
+
+    return epochs
+
+
+def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
+
+    if filteroptions['filter_min_avg_profit'] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x for x in epochs
+            if x['results_metrics'].get('profit_mean', 0) * 100
+            > filteroptions['filter_min_avg_profit']
+        ]
+    if filteroptions['filter_max_avg_profit'] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x for x in epochs
+            if x['results_metrics'].get('profit_mean', 0) * 100
+            < filteroptions['filter_max_avg_profit']
+        ]
+    if filteroptions['filter_min_total_profit'] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x for x in epochs
+            if x['results_metrics'].get('profit_total_abs', 0)
+            > filteroptions['filter_min_total_profit']
+        ]
+    if filteroptions['filter_max_total_profit'] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x for x in epochs
+            if x['results_metrics'].get('profit_total_abs', 0)
+            < filteroptions['filter_max_total_profit']
+        ]
+    return epochs
+
+
+def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
+
+    if filteroptions['filter_min_objective'] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+
+        epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
+    if filteroptions['filter_max_objective'] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+
+        epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
+
+    return epochs

freqtrade/optimize/hyperopt_interface.py

@@ -57,6 +57,13 @@ class IHyperOpt(ABC):
         """
         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.

freqtrade/optimize/hyperopt_tools.py

@@ -4,7 +4,7 @@ import logging
 from copy import deepcopy
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Iterator, List, Optional, Tuple
 
 import numpy as np
 import rapidjson
@@ -15,6 +15,7 @@ from pandas import isna, json_normalize
 from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
+from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
 
 
 logger = logging.getLogger(__name__)
@@ -82,53 +83,77 @@ class HyperoptTools():
         """
         Tell if the space value is contained in the configuration
         """
-        # The 'trailing' space is not included in the 'default' set of spaces
-        if space == 'trailing':
+        # 'trailing' and 'protection spaces are not included in the 'default' set of spaces
+        if space in ('trailing', 'protection'):
             return any(s in config['spaces'] for s in [space, 'all'])
         else:
             return any(s in config['spaces'] for s in [space, 'all', 'default'])
 
     @staticmethod
-    def _read_results_pickle(results_file: Path) -> List:
+    def _read_results(results_file: Path, batch_size: int = 10) -> Iterator[List[Any]]:
         """
-        Read hyperopt results from pickle file
-        LEGACY method - new files are written as json and cannot be read with this method.
-        """
-        from joblib import load
-
-        logger.info(f"Reading pickled epochs from '{results_file}'")
-        data = load(results_file)
-        return data
-
-    @staticmethod
-    def _read_results(results_file: Path) -> List:
-        """
-        Read hyperopt results from file
+        Stream hyperopt results from file
         """
         import rapidjson
         logger.info(f"Reading epochs from '{results_file}'")
         with results_file.open('r') as f:
-            data = [rapidjson.loads(line) for line in f]
-        return data
+            data = []
+            for line in f:
+                data += [rapidjson.loads(line)]
+                if len(data) >= batch_size:
+                    yield data
+                    data = []
+        yield data
 
     @staticmethod
-    def load_previous_results(results_file: Path) -> List:
-        """
-        Load data for epochs from the file if we have one
-        """
-        epochs: List = []
+    def _test_hyperopt_results_exist(results_file) -> bool:
         if results_file.is_file() and results_file.stat().st_size > 0:
             if results_file.suffix == '.pickle':
-                epochs = HyperoptTools._read_results_pickle(results_file)
-            else:
-                epochs = HyperoptTools._read_results(results_file)
-            # Detection of some old format, without 'is_best' field saved
-            if epochs[0].get('is_best') is None:
+                raise OperationalException(
+                    "Legacy hyperopt results are no longer supported."
+                    "Please rerun hyperopt or use an older version to load this file."
+                )
+            return True
+        else:
+            # No file found.
+            return False
+
+    @staticmethod
+    def load_filtered_results(results_file: Path, config: Dict[str, Any]) -> Tuple[List, int]:
+        filteroptions = {
+            'only_best': config.get('hyperopt_list_best', False),
+            'only_profitable': config.get('hyperopt_list_profitable', False),
+            'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
+            'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
+            'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
+            'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
+            'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
+            'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
+            'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
+            'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
+            'filter_min_objective': config.get('hyperopt_list_min_objective', None),
+            'filter_max_objective': config.get('hyperopt_list_max_objective', None),
+        }
+        if not HyperoptTools._test_hyperopt_results_exist(results_file):
+            # No file found.
+            return [], 0
+
+        epochs = []
+        total_epochs = 0
+        for epochs_tmp in HyperoptTools._read_results(results_file):
+            if total_epochs == 0 and epochs_tmp[0].get('is_best') is None:
                 raise OperationalException(
                     "The file with HyperoptTools results is incompatible with this version "
                     "of Freqtrade and cannot be loaded.")
-            logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
-        return epochs
+            total_epochs += len(epochs_tmp)
+            epochs += hyperopt_filter_epochs(epochs_tmp, filteroptions, log=False)
+
+        logger.info(f"Loaded {total_epochs} previous evaluations from disk.")
+
+        # Final filter run ...
+        epochs = hyperopt_filter_epochs(epochs, filteroptions, log=True)
+
+        return epochs, total_epochs
 
     @staticmethod
     def show_epoch_details(results, total_epochs: int, print_json: bool,
@@ -149,7 +174,7 @@ class HyperoptTools():
 
         if print_json:
             result_dict: Dict = {}
-            for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
+            for s in ['buy', 'sell', 'protection', 'roi', 'stoploss', 'trailing']:
                 HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
             print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
 
@@ -158,6 +183,8 @@ class HyperoptTools():
                                                non_optimized)
             HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
                                                non_optimized)
+            HyperoptTools._params_pretty_print(params, 'protection',
+                                               "Protection hyperspace params:", non_optimized)
             HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized)
             HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized)
             HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized)
@@ -203,7 +230,7 @@ class HyperoptTools():
             elif space == "roi":
                 result = result[:-1] + f'{appendix}\n'
                 minimal_roi_result = rapidjson.dumps({
-                        str(k): v for k, v in (space_params or no_params).items()
+                    str(k): v for k, v in (space_params or no_params).items()
                 }, default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
                 result += f"minimal_roi = {minimal_roi_result}"
             elif space == "trailing":
@@ -431,21 +458,14 @@ class HyperoptTools():
         trials['Best'] = ''
         trials['Stake currency'] = config['stake_currency']
 
-        if 'results_metrics.total_trades' in trials:
-            base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
-                            'results_metrics.profit_mean', 'results_metrics.profit_median',
-                            'results_metrics.profit_total',
-                            'Stake currency',
-                            'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
-                            'loss', 'is_initial_point', 'is_best']
-            perc_multi = 100
-        else:
-            perc_multi = 1
-            base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
-                            'results_metrics.avg_profit', 'results_metrics.median_profit',
-                            'results_metrics.total_profit',
-                            'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
-                            'loss', 'is_initial_point', 'is_best']
+        base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
+                        'results_metrics.profit_mean', 'results_metrics.profit_median',
+                        'results_metrics.profit_total',
+                        'Stake currency',
+                        'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
+                        'loss', 'is_initial_point', 'is_best']
+        perc_multi = 100
+
         param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
         trials = trials[base_metrics + param_metrics]
 
@@ -473,11 +493,6 @@ class HyperoptTools():
         trials['Avg profit'] = trials['Avg profit'].apply(
             lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
         )
-        if perc_multi == 1:
-            trials['Avg duration'] = trials['Avg duration'].apply(
-                lambda x: f'{x:,.1f} m' if isinstance(
-                    x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
-            )
         trials['Objective'] = trials['Objective'].apply(
             lambda x: f'{x:,.5f}' if x != 100000 else ""
         )

freqtrade/optimize/optimize_reports.py

@@ -31,7 +31,7 @@ def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> N
         filename = Path.joinpath(
             recordfilename.parent,
             f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
-            ).with_suffix(recordfilename.suffix)
+        ).with_suffix(recordfilename.suffix)
     file_dump_json(filename, stats)
 
     latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
@@ -173,7 +173,7 @@ def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
     for strategy, results in all_results.items():
         tabular_data.append(_generate_result_line(
             results['results'], results['config']['dry_run_wallet'], strategy)
-            )
+        )
         try:
             max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
                                                                   value_col='profit_ratio')
@@ -604,7 +604,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                          strat_results['stake_currency'])
         stake_amount = round_coin_value(
             strat_results['stake_amount'], strat_results['stake_currency']
-            ) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
+        ) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
 
         message = ("No trades made. "
                    f"Your starting balance was {start_balance}, "

freqtrade/persistence/models.py

@@ -161,7 +161,7 @@ class Order(_DECL_BASE):
         self.ft_is_open = True
         if self.status in ('closed', 'canceled', 'cancelled'):
             self.ft_is_open = False
-            if order.get('filled', 0) > 0:
+            if (order.get('filled', 0.0) or 0.0) > 0:
                 self.order_filled_date = datetime.now(timezone.utc)
         self.order_update_date = datetime.now(timezone.utc)
 
@@ -354,12 +354,12 @@ class LocalTrade():
         LocalTrade.trades_open = []
         LocalTrade.total_profit = 0
 
-    def adjust_min_max_rates(self, current_price: float) -> None:
+    def adjust_min_max_rates(self, current_price: float, current_price_low: float) -> None:
         """
         Adjust the max_rate and min_rate.
         """
         self.max_rate = max(current_price, self.max_rate or self.open_rate)
-        self.min_rate = min(current_price, self.min_rate or self.open_rate)
+        self.min_rate = min(current_price_low, self.min_rate or self.open_rate)
 
     def _set_new_stoploss(self, new_loss: float, stoploss: float):
         """Assign new stop value"""

freqtrade/plot/plotting.py

@@ -334,8 +334,8 @@ def add_areas(fig, row: int, data: pd.DataFrame, indicators) -> make_subplots:
                 )
             elif indicator_b not in data:
                 logger.info(
-                        'fill_to: "%s" ignored. Reason: This indicator is not '
-                        'in your strategy.', indicator_b
+                    'fill_to: "%s" ignored. Reason: This indicator is not '
+                    'in your strategy.', indicator_b
                 )
     return fig
 

freqtrade/plugins/pairlist/IPairList.py

@@ -144,7 +144,7 @@ class IPairList(LoggingMixin, ABC):
         markets = self._exchange.markets
         if not markets:
             raise OperationalException(
-                    'Markets not loaded. Make sure that exchange is initialized correctly.')
+                'Markets not loaded. Make sure that exchange is initialized correctly.')
 
         sanitized_whitelist: List[str] = []
         for pair in pairlist:

freqtrade/plugins/pairlist/VolumePairList.py

@@ -120,9 +120,9 @@ class VolumePairList(IPairList):
             # Use fresh pairlist
             # Check if pair quote currency equals to the stake currency.
             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)]
+                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)]
             pairlist = [s['symbol'] for s in filtered_tickers]
 
             pairlist = self.filter_pairlist(pairlist, tickers)
@@ -197,7 +197,7 @@ class VolumePairList(IPairList):
 
         if self._min_value > 0:
             filtered_tickers = [
-                    v for v in filtered_tickers if v[self._sort_key] > self._min_value]
+                v for v in filtered_tickers if v[self._sort_key] > self._min_value]
 
         sorted_tickers = sorted(filtered_tickers, reverse=True, key=lambda t: t[self._sort_key])
 

freqtrade/plugins/pairlistmanager.py

@@ -28,13 +28,13 @@ class PairListManager():
         self._tickers_needed = False
         for pairlist_handler_config in self._config.get('pairlists', None):
             pairlist_handler = PairListResolver.load_pairlist(
-                    pairlist_handler_config['method'],
-                    exchange=exchange,
-                    pairlistmanager=self,
-                    config=config,
-                    pairlistconfig=pairlist_handler_config,
-                    pairlist_pos=len(self._pairlist_handlers)
-                    )
+                pairlist_handler_config['method'],
+                exchange=exchange,
+                pairlistmanager=self,
+                config=config,
+                pairlistconfig=pairlist_handler_config,
+                pairlist_pos=len(self._pairlist_handlers)
+            )
             self._tickers_needed |= pairlist_handler.needstickers
             self._pairlist_handlers.append(pairlist_handler)
 

freqtrade/plugins/protections/iprotection.py

@@ -25,19 +25,22 @@ class IProtection(LoggingMixin, ABC):
     def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
         self._config = config
         self._protection_config = protection_config
+        self._stop_duration_candles: Optional[int] = None
+        self._lookback_period_candles: Optional[int] = None
+
         tf_in_min = timeframe_to_minutes(config['timeframe'])
         if 'stop_duration_candles' in protection_config:
-            self._stop_duration_candles = protection_config.get('stop_duration_candles', 1)
+            self._stop_duration_candles = int(protection_config.get('stop_duration_candles', 1))
             self._stop_duration = (tf_in_min * self._stop_duration_candles)
         else:
             self._stop_duration_candles = None
             self._stop_duration = protection_config.get('stop_duration', 60)
         if 'lookback_period_candles' in protection_config:
-            self._lookback_period_candles = protection_config.get('lookback_period_candles', 1)
+            self._lookback_period_candles = int(protection_config.get('lookback_period_candles', 1))
             self._lookback_period = tf_in_min * self._lookback_period_candles
         else:
             self._lookback_period_candles = None
-            self._lookback_period = protection_config.get('lookback_period', 60)
+            self._lookback_period = int(protection_config.get('lookback_period', 60))
 
         LoggingMixin.__init__(self, logger)
 

freqtrade/plugins/protections/stoploss_guard.py

@@ -54,9 +54,9 @@ class StoplossGuard(IProtection):
 
         trades1 = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until)
         trades = [trade for trade in trades1 if (str(trade.sell_reason) in (
-                    SellType.TRAILING_STOP_LOSS.value, SellType.STOP_LOSS.value,
-                    SellType.STOPLOSS_ON_EXCHANGE.value)
-                      and trade.close_profit and trade.close_profit < 0)]
+            SellType.TRAILING_STOP_LOSS.value, SellType.STOP_LOSS.value,
+            SellType.STOPLOSS_ON_EXCHANGE.value)
+            and trade.close_profit and trade.close_profit < 0)]
 
         if len(trades) < self._trade_limit:
             return False, None, None

freqtrade/resolvers/__init__.py

@@ -8,6 +8,3 @@ from freqtrade.resolvers.exchange_resolver import ExchangeResolver
 from freqtrade.resolvers.pairlist_resolver import PairListResolver
 from freqtrade.resolvers.protection_resolver import ProtectionResolver
 from freqtrade.resolvers.strategy_resolver import StrategyResolver
-
-
-

freqtrade/resolvers/strategy_resolver.py

@@ -50,7 +50,7 @@ class StrategyResolver(IResolver):
             if 'timeframe' not in config:
                 logger.warning(
                     "DEPRECATED: Please migrate to using 'timeframe' instead of 'ticker_interval'."
-                    )
+                )
                 strategy.timeframe = strategy.ticker_interval
 
         if strategy._ft_params_from_file:
@@ -119,7 +119,7 @@ class StrategyResolver(IResolver):
         - default (if not None)
         """
         if (attribute in config
-                and not isinstance(getattr(type(strategy), 'my_property', None), property)):
+                and not isinstance(getattr(type(strategy), attribute, None), property)):
             # Ensure Properties are not overwritten
             setattr(strategy, attribute, config[attribute])
             logger.info("Override strategy '%s' with value in config file: %s.",

freqtrade/rpc/api_server/api_v1.py

@@ -199,8 +199,8 @@ def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
                  config=Depends(get_config)):
     config = deepcopy(config)
     config.update({
-            'strategy': strategy,
-        })
+        'strategy': strategy,
+    })
     return RPC._rpc_analysed_history_full(config, pair, timeframe, timerange)
 
 

freqtrade/rpc/fiat_convert.py

@@ -62,7 +62,7 @@ class CryptoToFiatConverter:
             # If the request is not a 429 error we want to raise the normal error
             logger.error(
                 "Could not load FIAT Cryptocurrency map for the following problem: {}".format(
-                  request_exception
+                    request_exception
                 )
             )
         except (Exception) as exception:

freqtrade/rpc/rpc_manager.py

@@ -15,6 +15,7 @@ class RPCManager:
     """
     Class to manage RPC objects (Telegram, API, ...)
     """
+
     def __init__(self, freqtrade) -> None:
         """ Initializes all enabled rpc modules """
         self.registered_modules: List[RPCHandler] = []

freqtrade/rpc/telegram.py

@@ -77,7 +77,6 @@ class Telegram(RPCHandler):
     """  This class handles all telegram communication """
 
     def __init__(self, rpc: RPC, config: Dict[str, Any]) -> None:
-
         """
         Init the Telegram call, and init the super class RPCHandler
         :param rpc: instance of RPC Helper class
@@ -270,7 +269,7 @@ class Telegram(RPCHandler):
         noti = ''
         if msg_type == RPCMessageType.SELL:
             sell_noti = self._config['telegram'] \
-              .get('notification_settings', {}).get(str(msg_type), {})
+                .get('notification_settings', {}).get(str(msg_type), {})
             # For backward compatibility sell still can be string
             if isinstance(sell_noti, str):
                 noti = sell_noti
@@ -278,7 +277,7 @@ class Telegram(RPCHandler):
                 noti = sell_noti.get(str(msg['sell_reason']), default_noti)
         else:
             noti = self._config['telegram'] \
-              .get('notification_settings', {}).get(str(msg_type), default_noti)
+                .get('notification_settings', {}).get(str(msg_type), default_noti)
 
         if noti == 'off':
             logger.info(f"Notification '{msg_type}' not sent.")
@@ -541,7 +540,7 @@ class Telegram(RPCHandler):
                 f"`{first_trade_date}`\n"
                 f"*Latest Trade opened:* `{latest_trade_date}\n`"
                 f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`"
-                )
+            )
             if stats['closed_trade_count'] > 0:
                 markdown_msg += (f"\n*Avg. Duration:* `{avg_duration}`\n"
                                  f"*Best Performing:* `{best_pair}: {best_rate:.2f}%`")
@@ -576,13 +575,14 @@ class Telegram(RPCHandler):
         sell_reasons_msg = tabulate(
             sell_reasons_tabulate,
             headers=['Sell Reason', 'Sells', 'Wins', 'Losses']
-            )
+        )
         durations = stats['durations']
-        duration_msg = tabulate([
-            ['Wins', str(timedelta(seconds=durations['wins']))
-             if durations['wins'] != 'N/A' else 'N/A'],
-            ['Losses', str(timedelta(seconds=durations['losses']))
-             if durations['losses'] != 'N/A' else 'N/A']
+        duration_msg = tabulate(
+            [
+                ['Wins', str(timedelta(seconds=durations['wins']))
+                 if durations['wins'] != 'N/A' else 'N/A'],
+                ['Losses', str(timedelta(seconds=durations['losses']))
+                 if durations['losses'] != 'N/A' else 'N/A']
             ],
             headers=['', 'Avg. Duration']
         )
@@ -1100,7 +1100,7 @@ class Telegram(RPCHandler):
         if reload_able:
             reply_markup = InlineKeyboardMarkup([
                 [InlineKeyboardButton("Refresh", callback_data=callback_path)],
-                ])
+            ])
         else:
             reply_markup = InlineKeyboardMarkup([[]])
         msg += "\nUpdated: {}".format(datetime.now().ctime())

freqtrade/strategy/__init__.py

@@ -1,7 +1,7 @@
 # flake8: noqa: F401
 from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date,
                                 timeframe_to_prev_date, timeframe_to_seconds)
-from freqtrade.strategy.hyper import (CategoricalParameter, DecimalParameter, IntParameter,
-                                      RealParameter)
+from freqtrade.strategy.hyper import (BooleanParameter, CategoricalParameter, DecimalParameter,
+                                      IntParameter, RealParameter)
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.strategy.strategy_helper import merge_informative_pair, stoploss_from_open

freqtrade/strategy/hyper.py

@@ -270,6 +270,28 @@ class CategoricalParameter(BaseParameter):
             return [self.value]
 
 
+class BooleanParameter(CategoricalParameter):
+
+    def __init__(self, *, default: Optional[Any] = None,
+                 space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
+        """
+        Initialize hyperopt-optimizable Boolean Parameter.
+        It's a shortcut to `CategoricalParameter([True, False])`.
+        :param default: A default value. If not specified, first item from specified space will be
+         used.
+        :param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
+         parameter field
+         name is prefixed with 'buy_' or 'sell_'.
+        :param optimize: Include parameter in hyperopt optimizations.
+        :param load: Load parameter value from {space}_params.
+        :param kwargs: Extra parameters to skopt.space.Categorical.
+        """
+
+        categories = [True, False]
+        super().__init__(categories=categories, default=default, space=space, optimize=optimize,
+                         load=load, **kwargs)
+
+
 class HyperStrategyMixin(object):
     """
     A helper base class which allows HyperOptAuto class to reuse implementations of buy/sell
@@ -283,6 +305,7 @@ class HyperStrategyMixin(object):
         self.config = config
         self.ft_buy_params: List[BaseParameter] = []
         self.ft_sell_params: List[BaseParameter] = []
+        self.ft_protection_params: List[BaseParameter] = []
 
         self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
 
@@ -292,11 +315,12 @@ class HyperStrategyMixin(object):
         :param category:
         :return:
         """
-        if category not in ('buy', 'sell', None):
-            raise OperationalException('Category must be one of: "buy", "sell", None.')
+        if category not in ('buy', 'sell', 'protection', None):
+            raise OperationalException(
+                'Category must be one of: "buy", "sell", "protection", None.')
 
         if category is None:
-            params = self.ft_buy_params + self.ft_sell_params
+            params = self.ft_buy_params + self.ft_sell_params + self.ft_protection_params
         else:
             params = getattr(self, f"ft_{category}_params")
 
@@ -324,9 +348,10 @@ class HyperStrategyMixin(object):
         params: Dict = {
             'buy': list(cls.detect_parameters('buy')),
             'sell': list(cls.detect_parameters('sell')),
+            'protection': list(cls.detect_parameters('protection')),
         }
         params.update({
-            'count': len(params['buy'] + params['sell'])
+            'count': len(params['buy'] + params['sell'] + params['protection'])
         })
 
         return params
@@ -340,9 +365,12 @@ class HyperStrategyMixin(object):
         self._ft_params_from_file = params
         buy_params = deep_merge_dicts(params.get('buy', {}), getattr(self, 'buy_params', {}))
         sell_params = deep_merge_dicts(params.get('sell', {}), getattr(self, 'sell_params', {}))
+        protection_params = deep_merge_dicts(params.get('protection', {}),
+                                             getattr(self, 'protection_params', {}))
 
         self._load_params(buy_params, 'buy', hyperopt)
         self._load_params(sell_params, 'sell', hyperopt)
+        self._load_params(protection_params, 'protection', hyperopt)
 
     def load_params_from_file(self) -> Dict:
         filename_str = getattr(self, '__file__', '')
@@ -397,7 +425,8 @@ class HyperStrategyMixin(object):
         """
         params = {
             'buy': {},
-            'sell': {}
+            'sell': {},
+            'protection': {},
         }
         for name, p in self.enumerate_parameters():
             if not p.optimize or not p.in_space:

freqtrade/strategy/interface.py

@@ -605,7 +605,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         current_rate = rate
         current_profit = trade.calc_profit_ratio(current_rate)
 
-        trade.adjust_min_max_rates(high or current_rate)
+        trade.adjust_min_max_rates(high or current_rate, low or current_rate)
 
         stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
                                               current_time=date, current_profit=current_profit,
@@ -769,7 +769,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         else:
             return current_profit > roi
 
-    def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
+    def advise_all_indicators(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
         """
         Populates indicators for given candle (OHLCV) data (for multiple pairs)
         Does not run advise_buy or advise_sell!

freqtrade/strategy/strategy_helper.py

@@ -38,7 +38,7 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
         # 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')
-            )
+        )
     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.")

freqtrade/templates/base_config.json.j2

@@ -25,7 +25,7 @@
     "ask_strategy": {
         "price_side": "ask",
         "use_order_book": true,
-        "order_book_top": 1,
+        "order_book_top": 1
     },
     {{ exchange | indent(4) }},
     "pairlists": [

freqtrade/templates/base_strategy.py.j2

@@ -6,8 +6,8 @@ import numpy as np  # noqa
 import pandas as pd  # noqa
 from pandas import DataFrame
 
-from freqtrade.strategy import IStrategy
-from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
+                                IStrategy, IntParameter)
 
 # --------------------------------
 # Add your lib to import here

freqtrade/templates/sample_strategy.py

@@ -6,8 +6,8 @@ import numpy as np  # noqa
 import pandas as pd  # noqa
 from pandas import DataFrame
 
-from freqtrade.strategy import IStrategy
-from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
+                                IStrategy, IntParameter)
 
 # --------------------------------
 # Add your lib to import here

requirements-dev.txt

@@ -19,7 +19,7 @@ isort==5.9.3
 nbconvert==6.1.0
 
 # mypy types
-types-cachetools==0.1.9
-types-filelock==0.1.4
-types-requests==2.25.1
-types-tabulate==0.1.1
+types-cachetools==0.1.10
+types-filelock==0.1.5
+types-requests==2.25.6
+types-tabulate==0.8.2

requirements.txt

@@ -1,7 +1,7 @@
 numpy==1.21.1
 pandas==1.3.1
 
-ccxt==1.54.24
+ccxt==1.54.74
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==3.4.7
 aiohttp==3.7.4.post0

tests/commands/test_commands.py

@@ -938,247 +938,261 @@ def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
         pytest.fail(f'Expected well formed JSON, but failed to parse: {captured.out}')
 
 
-def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results,
-                       saved_hyperopt_results_legacy, tmpdir):
+def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results, tmpdir):
     csv_file = Path(tmpdir) / "test.csv"
-    for res in (saved_hyperopt_results, saved_hyperopt_results_legacy):
-        mocker.patch(
-            'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
-            MagicMock(return_value=res)
+    mocker.patch(
+        'freqtrade.optimize.hyperopt_tools.HyperoptTools._test_hyperopt_results_exist',
+        return_value=True
         )
 
-        args = [
-            "hyperopt-list",
-            "--no-details",
-            "--no-color",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12",
-                             " 6/12", " 7/12", " 8/12", " 9/12", " 10/12",
-                             " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--best",
-            "--no-details",
-            "--no-color",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 1/12", " 5/12", " 10/12"])
-        assert all(x not in captured.out
-                   for x in [" 2/12", " 3/12", " 4/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                             " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--profitable",
-            "--no-details",
-            "--no-color",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 2/12", " 10/12"])
-        assert all(x not in captured.out
-                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                             " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--profitable",
-            "--no-color",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 2/12", " 10/12", "Best result:", "Buy hyperspace params",
-                             "Sell hyperspace params", "ROI table", "Stoploss"])
-        assert all(x not in captured.out
-                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                             " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--no-details",
-            "--no-color",
-            "--min-trades", "20",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 3/12", " 6/12", " 7/12", " 9/12", " 11/12"])
-        assert all(x not in captured.out
-                   for x in [" 1/12", " 2/12", " 4/12", " 5/12", " 8/12", " 10/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--profitable",
-            "--no-details",
-            "--no-color",
-            "--max-trades", "20",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 2/12", " 10/12"])
-        assert all(x not in captured.out
-                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                             " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--profitable",
-            "--no-details",
-            "--no-color",
-            "--min-avg-profit", "0.11",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 2/12"])
-        assert all(x not in captured.out
-                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                             " 10/12", " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--no-details",
-            "--no-color",
-            "--max-avg-profit", "0.10",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 1/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
-                             " 11/12"])
-        assert all(x not in captured.out
-                   for x in [" 2/12", " 4/12", " 10/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--no-details",
-            "--no-color",
-            "--min-total-profit", "0.4",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 10/12"])
-        assert all(x not in captured.out
-                   for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12",
-                             " 9/12", " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--no-details",
-            "--no-color",
-            "--max-total-profit", "0.4",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12",
-                             " 9/12", " 11/12"])
-        assert all(x not in captured.out
-                   for x in [" 4/12", " 10/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--no-details",
-            "--no-color",
-            "--min-objective", "0.1",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 10/12"])
-        assert all(x not in captured.out
-                   for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12",
-                             " 9/12", " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--no-details",
-            "--max-objective", "0.1",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12",
-                             " 9/12", " 11/12"])
-        assert all(x not in captured.out
-                   for x in [" 4/12", " 10/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--profitable",
-            "--no-details",
-            "--no-color",
-            "--min-avg-time", "2000",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 10/12"])
-        assert all(x not in captured.out
-                   for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12",
-                             " 8/12", " 9/12", " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--no-details",
-            "--no-color",
-            "--max-avg-time", "1500",
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        assert all(x in captured.out
-                   for x in [" 2/12", " 6/12"])
-        assert all(x not in captured.out
-                   for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 7/12", " 8/12"
-                             " 9/12", " 10/12", " 11/12", " 12/12"])
-        args = [
-            "hyperopt-list",
-            "--no-details",
-            "--no-color",
-            "--export-csv",
-            str(csv_file),
-        ]
-        pargs = get_args(args)
-        pargs['config'] = None
-        start_hyperopt_list(pargs)
-        captured = capsys.readouterr()
-        log_has("CSV file created: test_file.csv", caplog)
-        assert csv_file.is_file()
-        line = csv_file.read_text()
-        assert ('Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,"3,930.0 m",0.43662' in line
-                or "Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,2 days 17:30:00,0.43662" in line)
-        csv_file.unlink()
+    def fake_iterator(*args, **kwargs):
+        yield from [saved_hyperopt_results]
+
+    mocker.patch(
+        'freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results',
+        side_effect=fake_iterator
+    )
+
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12",
+                         " 6/12", " 7/12", " 8/12", " 9/12", " 10/12",
+                         " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--best",
+        "--no-details",
+        "--no-color",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 1/12", " 5/12", " 10/12"])
+    assert all(x not in captured.out
+               for x in [" 2/12", " 3/12", " 4/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                         " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-details",
+        "--no-color",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 2/12", " 10/12"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                         " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-color",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 2/12", " 10/12", "Best result:", "Buy hyperspace params",
+                         "Sell hyperspace params", "ROI table", "Stoploss"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                         " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--min-trades", "20",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 3/12", " 6/12", " 7/12", " 9/12", " 11/12"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 2/12", " 4/12", " 5/12", " 8/12", " 10/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-details",
+        "--no-color",
+        "--max-trades", "20",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 2/12", " 10/12"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                         " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-details",
+        "--no-color",
+        "--min-avg-profit", "0.11",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 2/12"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                         " 10/12", " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--max-avg-profit", "0.10",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 1/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
+                         " 11/12"])
+    assert all(x not in captured.out
+               for x in [" 2/12", " 4/12", " 10/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--min-total-profit", "0.4",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 10/12"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12",
+                         " 9/12", " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--max-total-profit", "0.4",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12",
+                         " 9/12", " 11/12"])
+    assert all(x not in captured.out
+               for x in [" 4/12", " 10/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--min-objective", "0.1",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 10/12"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12",
+                         " 9/12", " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--max-objective", "0.1",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12",
+                         " 9/12", " 11/12"])
+    assert all(x not in captured.out
+               for x in [" 4/12", " 10/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-details",
+        "--no-color",
+        "--min-avg-time", "2000",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 10/12"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 2/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12",
+                         " 8/12", " 9/12", " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--max-avg-time", "1500",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out
+               for x in [" 2/12", " 6/12"])
+    assert all(x not in captured.out
+               for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 7/12", " 8/12"
+                         " 9/12", " 10/12", " 11/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--export-csv",
+        str(csv_file),
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    log_has("CSV file created: test_file.csv", caplog)
+    assert csv_file.is_file()
+    line = csv_file.read_text()
+    assert ('Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,"3,930.0 m",0.43662' in line
+            or "Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,2 days 17:30:00,0.43662" in line)
+    csv_file.unlink()
 
 
 def test_hyperopt_show(mocker, capsys, saved_hyperopt_results):
     mocker.patch(
-        'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
-        MagicMock(return_value=saved_hyperopt_results)
+        'freqtrade.optimize.hyperopt_tools.HyperoptTools._test_hyperopt_results_exist',
+        return_value=True
+    )
+
+    def fake_iterator(*args, **kwargs):
+        yield from [saved_hyperopt_results]
+
+    mocker.patch(
+        'freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results',
+        side_effect=fake_iterator
     )
     mocker.patch('freqtrade.commands.hyperopt_commands.show_backtest_result')
 

tests/config_test_comments.json

@@ -6,8 +6,8 @@
      */
     "stake_currency": "BTC",
     "stake_amount": 0.05,
-    "fiat_display_currency": "USD",             // C++-style comment
-    "amount_reserve_percent" : 0.05,		// And more, tabs before this comment
+    "fiat_display_currency": "USD", // C++-style comment
+    "amount_reserve_percent": 0.05, // And more, tabs before this comment
     "dry_run": false,
     "timeframe": "5m",
     "trailing_stop": false,
@@ -15,15 +15,15 @@
     "trailing_stop_positive_offset": 0.0051,
     "trailing_only_offset_is_reached": false,
     "minimal_roi": {
-        "40":  0.0,
-        "30":  0.01,
-        "20":  0.02,
-        "0":  0.04
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
     },
     "stoploss": -0.10,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30,				// Trailing comma should also be accepted now
+        "sell": 30, // Trailing comma should also be accepted now
     },
     "bid_strategy": {
         "use_order_book": false,
@@ -34,7 +34,7 @@
             "bids_to_ask_delta": 1
         }
     },
-    "ask_strategy":{
+    "ask_strategy": {
         "use_order_book": false,
         "order_book_min": 1,
         "order_book_max": 9
@@ -64,7 +64,9 @@
         "key": "your_exchange_key",
         "secret": "your_exchange_secret",
         "password": "",
-        "ccxt_config": {"enableRateLimit": true},
+        "ccxt_config": {
+            "enableRateLimit": true
+        },
         "ccxt_async_config": {
             "enableRateLimit": false,
             "rateLimit": 500,
@@ -103,8 +105,8 @@
         "remove_pumps": false
     },
     "telegram": {
-// We can now comment out some settings
-//        "enabled": true,
+        // We can now comment out some settings
+        //        "enabled": true,
         "enabled": false,
         "token": "your_telegram_token",
         "chat_id": "your_telegram_chat_id"

tests/conftest.py

@@ -1814,138 +1814,6 @@ def open_trade():
     )
 
 
-@pytest.fixture
-def saved_hyperopt_results_legacy():
-    return [
-        {
-            'loss': 0.4366182531161519,
-            'params_dict': {
-                'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556},  # noqa: E501
-            'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}},  # noqa: E501
-            'results_metrics': {'trade_count': 2, 'avg_profit': -1.254995, 'median_profit': -1.2222, 'total_profit': -0.00125625, 'profit': -2.50999, 'duration': 3930.0},  # noqa: E501
-            'results_explanation': '     2 trades. Avg profit  -1.25%. Total profit -0.00125625 BTC (  -2.51Σ%). Avg duration 3930.0 min.',  # noqa: E501
-            'total_profit': -0.00125625,
-            'current_epoch': 1,
-            'is_initial_point': True,
-            'is_best': True
-        }, {
-            'loss': 20.0,
-            'params_dict': {
-                'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 334, 'roi_t2': 683, 'roi_t3': 140, 'roi_p1': 0.06403981740598495, 'roi_p2': 0.055519840060645045, 'roi_p3': 0.3253712811342459, 'stoploss': -0.338070047333259},  # noqa: E501
-            'params_details': {
-                'buy': {'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'},  # noqa: E501
-                'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'},  # noqa: E501
-                'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0},  # noqa: E501
-                'stoploss': {'stoploss': -0.338070047333259}},
-            'results_metrics': {'trade_count': 1, 'avg_profit': 0.12357, 'median_profit': -1.2222, 'total_profit': 6.185e-05, 'profit': 0.12357, 'duration': 1200.0},  # noqa: E501
-            'results_explanation': '     1 trades. Avg profit   0.12%. Total profit  0.00006185 BTC (   0.12Σ%). Avg duration 1200.0 min.',  # noqa: E501
-            'total_profit': 6.185e-05,
-            'current_epoch': 2,
-            'is_initial_point': True,
-            'is_best': False
-        }, {
-            'loss': 14.241196856510731,
-            'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611},  # noqa: E501
-            'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}},  # noqa: E501
-            'results_metrics': {'trade_count': 621, 'avg_profit': -0.43883302093397747, 'median_profit': -1.2222, 'total_profit': -0.13639474, 'profit': -272.515306, 'duration': 1691.207729468599},  # noqa: E501
-            'results_explanation': '   621 trades. Avg profit  -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.',  # noqa: E501
-            'total_profit': -0.13639474,
-            'current_epoch': 3,
-            'is_initial_point': True,
-            'is_best': False
-        }, {
-            'loss': 100000,
-            'params_dict': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1402, 'roi_t2': 676, 'roi_t3': 215, 'roi_p1': 0.06264755784937427, 'roi_p2': 0.14258587851894644, 'roi_p3': 0.20671291201040828, 'stoploss': -0.11818343570194478},  # noqa: E501
-            'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}},  # noqa: E501
-            'results_metrics': {'trade_count': 0, 'avg_profit': None, 'median_profit': None, 'total_profit': 0, 'profit': 0.0, 'duration': None},  # noqa: E501
-            'results_explanation': '     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.',  # noqa: E501
-            'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_best': False
-        }, {
-            'loss': 0.22195522184191518,
-            'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014},   # noqa: E501
-            'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}},  # noqa: E501
-            'results_metrics': {'trade_count': 14, 'avg_profit': -0.3539515, 'median_profit': -1.2222, 'total_profit': -0.002480140000000001, 'profit': -4.955321, 'duration': 3402.8571428571427},  # noqa: E501
-            'results_explanation': '    14 trades. Avg profit  -0.35%. Total profit -0.00248014 BTC (  -4.96Σ%). Avg duration 3402.9 min.',  # noqa: E501
-            'total_profit': -0.002480140000000001,
-            'current_epoch': 5,
-            'is_initial_point': True,
-            'is_best': True
-        }, {
-            'loss': 0.545315889154162,
-            'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423},  # noqa: E501
-            'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}},  # noqa: E501
-            'results_metrics': {'trade_count': 39, 'avg_profit': -0.21400679487179478, 'median_profit': -1.2222, 'total_profit': -0.0041773, 'profit': -8.346264999999997, 'duration': 636.9230769230769},  # noqa: E501
-            'results_explanation': '    39 trades. Avg profit  -0.21%. Total profit -0.00417730 BTC (  -8.35Σ%). Avg duration 636.9 min.',  # noqa: E501
-            'total_profit': -0.0041773,
-            'current_epoch': 6,
-            'is_initial_point': True,
-            'is_best': False
-        }, {
-            'loss': 4.713497421432944,
-            'params_dict': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 771, 'roi_t2': 620, 'roi_t3': 145, 'roi_p1': 0.0586919200378493, 'roi_p2': 0.04984118697312542, 'roi_p3': 0.37521058680247044, 'stoploss': -0.14613268022709905},  # noqa: E501
-            'params_details': {
-                'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0},  # noqa: E501
-                'stoploss': {'stoploss': -0.14613268022709905}},  # noqa: E501
-            'results_metrics': {'trade_count': 318, 'avg_profit': -0.39833954716981146, 'median_profit': -1.2222, 'total_profit': -0.06339929, 'profit': -126.67197600000004, 'duration': 3140.377358490566},  # noqa: E501
-            'results_explanation': '   318 trades. Avg profit  -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.',  # noqa: E501
-            'total_profit': -0.06339929,
-            'current_epoch': 7,
-            'is_initial_point': True,
-            'is_best': False
-        }, {
-            'loss': 20.0,  # noqa: E501
-            'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401},  # noqa: E501
-            'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}},  # noqa: E501
-            'results_metrics': {'trade_count': 1, 'avg_profit': 0.0, 'median_profit': 0.0, 'total_profit': 0.0, 'profit': 0.0, 'duration': 5340.0},  # noqa: E501
-            'results_explanation': '     1 trades. Avg profit   0.00%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration 5340.0 min.',  # noqa: E501
-            'total_profit': 0.0,
-            'current_epoch': 8,
-            'is_initial_point': True,
-            'is_best': False
-        }, {
-            'loss': 2.4731817780991223,
-            'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503},  # noqa: E501
-            'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}},  # noqa: E501
-            'results_metrics': {'trade_count': 229, 'avg_profit': -0.38433433624454144, 'median_profit': -1.2222, 'total_profit': -0.044050070000000004, 'profit': -88.01256299999999, 'duration': 6505.676855895196},  # noqa: E501
-            'results_explanation': '   229 trades. Avg profit  -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.',  # noqa: E501
-            'total_profit': -0.044050070000000004,  # noqa: E501
-            'current_epoch': 9,
-            'is_initial_point': True,
-            'is_best': False
-        }, {
-            'loss': -0.2604606005845212,  # noqa: E501
-            'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415},  # noqa: E501
-            'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}},  # noqa: E501
-            'results_metrics': {'trade_count': 4, 'avg_profit': 0.1080385, 'median_profit': -1.2222, 'total_profit': 0.00021629, 'profit': 0.432154, 'duration': 2850.0},  # noqa: E501
-            'results_explanation': '     4 trades. Avg profit   0.11%. Total profit  0.00021629 BTC (   0.43Σ%). Avg duration 2850.0 min.',  # noqa: E501
-            'total_profit': 0.00021629,
-            'current_epoch': 10,
-            'is_initial_point': True,
-            'is_best': True
-        }, {
-            'loss': 4.876465945994304,  # noqa: E501
-            'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887},  # noqa: E501
-            'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}},  # noqa: E501
-            'results_metrics': {'trade_count': 117, 'avg_profit': -1.2698609145299145, 'median_profit': -1.2222, 'total_profit': -0.07436117, 'profit': -148.573727, 'duration': 4282.5641025641025},  # noqa: E501
-            'results_explanation': '   117 trades. Avg profit  -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.',  # noqa: E501
-            'total_profit': -0.07436117,
-            'current_epoch': 11,
-            'is_initial_point': True,
-            'is_best': False
-        }, {
-            'loss': 100000,
-            'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806},  # noqa: E501
-            'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}},  # noqa: E501
-            'results_metrics': {'trade_count': 0, 'avg_profit': None, 'median_profit': None, 'total_profit': 0, 'profit': 0.0, 'duration': None},  # noqa: E501
-            'results_explanation': '     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.',  # noqa: E501
-            'total_profit': 0,
-            'current_epoch': 12,
-            'is_initial_point': True,
-            'is_best': False
-            }
-    ]
-
-
 @pytest.fixture
 def saved_hyperopt_results():
     hyperopt_res = [

tests/data/test_history.py

@@ -381,7 +381,7 @@ def test_get_timerange(default_conf, mocker, testdatadir) -> None:
     default_conf.update({'strategy': 'DefaultStrategy'})
     strategy = StrategyResolver.load_strategy(default_conf)
 
-    data = strategy.ohlcvdata_to_dataframe(
+    data = strategy.advise_all_indicators(
         load_data(
             datadir=testdatadir,
             timeframe='1m',
@@ -399,7 +399,7 @@ def test_validate_backtest_data_warn(default_conf, mocker, caplog, testdatadir)
     default_conf.update({'strategy': 'DefaultStrategy'})
     strategy = StrategyResolver.load_strategy(default_conf)
 
-    data = strategy.ohlcvdata_to_dataframe(
+    data = strategy.advise_all_indicators(
         load_data(
             datadir=testdatadir,
             timeframe='1m',
@@ -424,7 +424,7 @@ def test_validate_backtest_data(default_conf, mocker, caplog, testdatadir) -> No
     strategy = StrategyResolver.load_strategy(default_conf)
 
     timerange = TimeRange('index', 'index', 200, 250)
-    data = strategy.ohlcvdata_to_dataframe(
+    data = strategy.advise_all_indicators(
         load_data(
             datadir=testdatadir,
             timeframe='5m',

tests/exchange/test_exchange.py

@@ -984,16 +984,21 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice,
     assert order['fee']
 
 
-@pytest.mark.parametrize("side,amount,endprice", [
-    ("buy", 1, 25.566),
-    ("buy", 100, 25.5672),  # Requires interpolation
-    ("buy", 1000, 25.575),  # More than orderbook return
-    ("sell", 1, 25.563),
-    ("sell", 100, 25.5625),  # Requires interpolation
-    ("sell", 1000, 25.5555),  # More than orderbook return
+@pytest.mark.parametrize("side,rate,amount,endprice", [
+    # spread is 25.263-25.266
+    ("buy", 25.564, 1, 25.566),
+    ("buy", 25.564, 100, 25.5672),  # Requires interpolation
+    ("buy", 25.590, 100, 25.5672),  # Price above spread ... average is lower
+    ("buy", 25.564, 1000, 25.575),  # More than orderbook return
+    ("buy", 24.000, 100000, 25.200),  # Run into max_slippage of 5%
+    ("sell", 25.564, 1, 25.563),
+    ("sell", 25.564, 100, 25.5625),  # Requires interpolation
+    ("sell", 25.510, 100, 25.5625),  # price below spread - average is higher
+    ("sell", 25.564, 1000, 25.5555),  # More than orderbook return
+    ("sell", 27, 10000, 25.65),  # max-slippage 5%
 ])
 @pytest.mark.parametrize("exchange_name", EXCHANGES)
-def test_create_dry_run_order_market_fill(default_conf, mocker, side, amount, endprice,
+def test_create_dry_run_order_market_fill(default_conf, mocker, side, rate, amount, endprice,
                                           exchange_name, order_book_l2_usd):
     default_conf['dry_run'] = True
     exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
@@ -1003,7 +1008,7 @@ def test_create_dry_run_order_market_fill(default_conf, mocker, side, amount, en
                           )
 
     order = exchange.create_dry_run_order(
-        pair='LTC/USDT', ordertype='market', side=side, amount=amount, rate=25.5)
+        pair='LTC/USDT', ordertype='market', side=side, amount=amount, rate=rate)
     assert 'id' in order
     assert f'dry_run_{side}_' in order["id"]
     assert order["side"] == side

tests/optimize/__init__.py

@@ -52,4 +52,6 @@ def _build_backtest_dataframe(data):
     # Ensure floats are in place
     for column in ['open', 'high', 'low', 'close', 'volume']:
         frame[column] = frame[column].astype('float64')
+    if 'buy_tag' not in columns:
+        frame['buy_tag'] = None
     return frame

tests/optimize/test_backtesting.py

@@ -1,6 +1,7 @@
 # pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
 
 import random
+from datetime import timedelta
 from pathlib import Path
 from unittest.mock import MagicMock, PropertyMock
 
@@ -85,7 +86,7 @@ def simple_backtest(config, contour, mocker, testdatadir) -> None:
     backtesting._set_strategy(backtesting.strategylist[0])
 
     data = load_data_test(contour, testdatadir)
-    processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
+    processed = backtesting.strategy.advise_all_indicators(data)
     min_date, max_date = get_timerange(processed)
     assert isinstance(processed, dict)
     results = backtesting.backtest(
@@ -107,7 +108,7 @@ def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
     patch_exchange(mocker)
     backtesting = Backtesting(conf)
     backtesting._set_strategy(backtesting.strategylist[0])
-    processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
+    processed = backtesting.strategy.advise_all_indicators(data)
     min_date, max_date = get_timerange(processed)
     return {
         'processed': processed,
@@ -289,7 +290,7 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
     backtesting._set_strategy(backtesting.strategylist[0])
     assert backtesting.config == default_conf
     assert backtesting.timeframe == '5m'
-    assert callable(backtesting.strategy.ohlcvdata_to_dataframe)
+    assert callable(backtesting.strategy.advise_all_indicators)
     assert callable(backtesting.strategy.advise_buy)
     assert callable(backtesting.strategy.advise_sell)
     assert isinstance(backtesting.strategy.dp, DataProvider)
@@ -335,14 +336,14 @@ def test_data_to_dataframe_bt(default_conf, mocker, testdatadir) -> None:
                              fill_up_missing=True)
     backtesting = Backtesting(default_conf)
     backtesting._set_strategy(backtesting.strategylist[0])
-    processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
+    processed = backtesting.strategy.advise_all_indicators(data)
     assert len(processed['UNITTEST/BTC']) == 102
 
     # Load strategy to compare the result between Backtesting function and strategy are the same
     default_conf.update({'strategy': 'DefaultStrategy'})
     strategy = StrategyResolver.load_strategy(default_conf)
 
-    processed2 = strategy.ohlcvdata_to_dataframe(data)
+    processed2 = strategy.advise_all_indicators(data)
     assert processed['UNITTEST/BTC'].equals(processed2['UNITTEST/BTC'])
 
 
@@ -535,6 +536,8 @@ def test_backtest__enter_trade(default_conf, fee, mocker) -> None:
     trade = backtesting._enter_trade(pair, row=row)
     assert trade is None
 
+    backtesting.cleanup()
+
 
 def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
     default_conf['use_sell_signal'] = False
@@ -547,7 +550,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
     timerange = TimeRange('date', None, 1517227800, 0)
     data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
                              timerange=timerange)
-    processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
+    processed = backtesting.strategy.advise_all_indicators(data)
     min_date, max_date = get_timerange(processed)
     result = backtesting.backtest(
         processed=processed,
@@ -581,7 +584,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
          'initial_stop_loss_ratio': [-0.1, -0.1],
          'stop_loss_abs': [0.0940005, 0.09272236],
          'stop_loss_ratio': [-0.1, -0.1],
-         'min_rate': [0.1038, 0.10302485],
+         'min_rate': [0.10370188, 0.10300000000000001],
          'max_rate': [0.10501, 0.1038888],
          'is_open': [False, False],
          'buy_tag': [None, None],
@@ -612,7 +615,7 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
     timerange = TimeRange.parse_timerange('1510688220-1510700340')
     data = history.load_data(datadir=testdatadir, timeframe='1m', pairs=['UNITTEST/BTC'],
                              timerange=timerange)
-    processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
+    processed = backtesting.strategy.advise_all_indicators(data)
     min_date, max_date = get_timerange(processed)
     results = backtesting.backtest(
         processed=processed,
@@ -631,7 +634,7 @@ def test_processed(default_conf, mocker, testdatadir) -> None:
     backtesting._set_strategy(backtesting.strategylist[0])
 
     dict_of_tickerrows = load_data_test('raise', testdatadir)
-    dataframes = backtesting.strategy.ohlcvdata_to_dataframe(dict_of_tickerrows)
+    dataframes = backtesting.strategy.advise_all_indicators(dict_of_tickerrows)
     dataframe = dataframes['UNITTEST/BTC']
     cols = dataframe.columns
     # assert the dataframe got some of the indicator columns
@@ -739,8 +742,13 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
     # 100 buys signals
     results = result['results']
     assert len(results) == 100
-    # Cached data should be 200 (no change since required_startup is 0)
-    assert len(backtesting.dataprovider.get_analyzed_dataframe('UNITTEST/BTC', '1m')[0]) == 200
+    # Cached data should be 200
+    analyzed_df = backtesting.dataprovider.get_analyzed_dataframe('UNITTEST/BTC', '1m')[0]
+    assert len(analyzed_df) == 200
+    # Expect last candle to be 1 below end date (as the last candle is assumed as "incomplete"
+    # during backtesting)
+    expected_last_candle_date = backtest_conf['end_date'] - timedelta(minutes=1)
+    assert analyzed_df.iloc[-1]['date'].to_pydatetime() == expected_last_candle_date
 
     # One trade was force-closed at the end
     assert len(results.loc[results['is_open']]) == 0
@@ -772,7 +780,8 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
     data = trim_dictlist(data, -500)
 
     # Remove data for one pair from the beginning of the data
-    data[pair] = data[pair][tres:].reset_index()
+    if tres > 0:
+        data[pair] = data[pair][tres:].reset_index()
     default_conf['timeframe'] = '5m'
 
     backtesting = Backtesting(default_conf)
@@ -780,7 +789,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
     backtesting.strategy.advise_buy = _trend_alternate_hold  # Override
     backtesting.strategy.advise_sell = _trend_alternate_hold  # Override
 
-    processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
+    processed = backtesting.strategy.advise_all_indicators(data)
     min_date, max_date = get_timerange(processed)
     backtest_conf = {
         'processed': processed,
@@ -798,8 +807,11 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
     assert len(evaluate_result_multi(results['results'], '5m', 3)) == 0
 
     # Cached data correctly removed amounts
-    removed_candles = len(data[pair]) - 1 - backtesting.strategy.startup_candle_count
+    offset = 1 if tres == 0 else 0
+    removed_candles = len(data[pair]) - offset - backtesting.strategy.startup_candle_count
     assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, '5m')[0]) == removed_candles
+    assert len(backtesting.dataprovider.get_analyzed_dataframe(
+        'NXT/BTC', '5m')[0]) == len(data['NXT/BTC']) - 1 - backtesting.strategy.startup_candle_count
 
     backtest_conf = {
         'processed': processed,

tests/optimize/test_hyperopt.py

@@ -351,7 +351,7 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
     del hyperopt_conf['timeframe']
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
+    hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
     hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
 
     hyperopt.start()
@@ -399,7 +399,7 @@ def test_hyperopt_format_results(hyperopt):
         'rejected_signals': 2,
         'backtest_start_time': 1619718665,
         'backtest_end_time': 1619718665,
-        }
+    }
     results_metrics = generate_strategy_stats({'XRP/BTC': None}, '', bt_result,
                                               Arrow(2017, 11, 14, 19, 32, 00),
                                               Arrow(2017, 12, 14, 19, 32, 00), market_change=0)
@@ -426,7 +426,7 @@ def test_hyperopt_format_results(hyperopt):
 
 def test_populate_indicators(hyperopt, testdatadir) -> None:
     data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
-    dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
+    dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
     dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
                                                              {'pair': 'UNITTEST/BTC'})
 
@@ -438,7 +438,7 @@ def test_populate_indicators(hyperopt, testdatadir) -> None:
 
 def test_buy_strategy_generator(hyperopt, testdatadir) -> None:
     data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
-    dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
+    dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
     dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
                                                              {'pair': 'UNITTEST/BTC'})
 
@@ -463,7 +463,7 @@ def test_buy_strategy_generator(hyperopt, testdatadir) -> None:
 
 def test_sell_strategy_generator(hyperopt, testdatadir) -> None:
     data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
-    dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
+    dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
     dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
                                                              {'pair': 'UNITTEST/BTC'})
 
@@ -577,6 +577,7 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
                                    "20.0": 0.02,
                                    "50.0": 0.01,
                                    "110.0": 0},
+                           'protection': {},
                            'sell': {'sell-adx-enabled': False,
                                     'sell-adx-value': 0,
                                     'sell-fastd-enabled': True,
@@ -592,7 +593,7 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
                                         'trailing_stop_positive': 0.02,
                                         'trailing_stop_positive_offset': 0.07}},
         'params_dict': optimizer_param,
-        'params_not_optimized': {'buy': {}, 'sell': {}},
+        'params_not_optimized': {'buy': {}, 'protection': {}, 'sell': {}},
         'results_metrics': ANY,
         'total_profit': 3.1e-08
     }
@@ -659,7 +660,7 @@ def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
                           })
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
+    hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
     hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
 
     hyperopt.start()
@@ -712,7 +713,7 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
     hyperopt_conf.update({'print_json': True})
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
+    hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
     hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
 
     hyperopt.start()
@@ -760,7 +761,7 @@ def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
                           })
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
+    hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
     hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
 
     hyperopt.start()
@@ -804,7 +805,7 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
     hyperopt_conf.update({'spaces': 'roi stoploss'})
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
+    hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
     hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
 
     del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
@@ -843,7 +844,7 @@ def test_simplified_interface_all_failed(mocker, hyperopt_conf) -> None:
     hyperopt_conf.update({'spaces': 'all', })
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
+    hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
     hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
 
     del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
@@ -885,7 +886,7 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
     hyperopt_conf.update({'spaces': 'buy'})
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
+    hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
     hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
 
     # TODO: sell_strategy_generator() is actually not called because
@@ -939,7 +940,7 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
     hyperopt_conf.update({'spaces': 'sell', })
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
+    hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
     hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
 
     # TODO: buy_strategy_generator() is actually not called because
@@ -984,7 +985,7 @@ def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> No
     hyperopt_conf.update({'spaces': space})
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
+    hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
     hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
 
     delattr(hyperopt.custom_hyperopt.__class__, method)
@@ -1002,6 +1003,8 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
     hyperopt_conf.update({
         'strategy': 'HyperoptableStrategy',
         'user_data_dir': Path(tmpdir),
+        'hyperopt_random_state': 42,
+        'spaces': ['all']
     })
     hyperopt = Hyperopt(hyperopt_conf)
     assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto)
@@ -1009,12 +1012,18 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
 
     assert hyperopt.backtesting.strategy.buy_rsi.in_space is True
     assert hyperopt.backtesting.strategy.buy_rsi.value == 35
+    assert hyperopt.backtesting.strategy.sell_rsi.value == 74
+    assert hyperopt.backtesting.strategy.protection_cooldown_lookback.value == 30
     buy_rsi_range = hyperopt.backtesting.strategy.buy_rsi.range
     assert isinstance(buy_rsi_range, range)
     # Range from 0 - 50 (inclusive)
     assert len(list(buy_rsi_range)) == 51
 
     hyperopt.start()
+    # All values should've changed.
+    assert hyperopt.backtesting.strategy.protection_cooldown_lookback.value != 30
+    assert hyperopt.backtesting.strategy.buy_rsi.value != 35
+    assert hyperopt.backtesting.strategy.sell_rsi.value != 74
 
 
 def test_SKDecimal():

tests/optimize/test_hyperopt_tools.py

@@ -10,7 +10,7 @@ import rapidjson
 from freqtrade.constants import FTHYPT_FILEVERSION
 from freqtrade.exceptions import OperationalException
 from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
-from tests.conftest import log_has, log_has_re
+from tests.conftest import log_has
 
 
 # Functions for recurrent object patching
@@ -20,9 +20,14 @@ def create_results() -> List[Dict]:
 
 
 def test_save_results_saves_epochs(hyperopt, tmpdir, caplog) -> None:
+
+    hyperopt.results_file = Path(tmpdir / 'ut_results.fthypt')
+
+    hyperopt_epochs = HyperoptTools.load_filtered_results(hyperopt.results_file, {})
+    assert hyperopt_epochs == ([], 0)
+
     # Test writing to temp dir and reading again
     epochs = create_results()
-    hyperopt.results_file = Path(tmpdir / 'ut_results.fthypt')
 
     caplog.set_level(logging.DEBUG)
 
@@ -33,36 +38,28 @@ def test_save_results_saves_epochs(hyperopt, tmpdir, caplog) -> None:
     hyperopt._save_result(epochs[0])
     assert log_has(f"2 epochs saved to '{hyperopt.results_file}'.", caplog)
 
-    hyperopt_epochs = HyperoptTools.load_previous_results(hyperopt.results_file)
+    hyperopt_epochs = HyperoptTools.load_filtered_results(hyperopt.results_file, {})
     assert len(hyperopt_epochs) == 2
+    assert hyperopt_epochs[1] == 2
+    assert len(hyperopt_epochs[0]) == 2
 
-
-def test_load_previous_results(testdatadir, caplog) -> None:
-
-    results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
-
-    hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
-
-    assert len(hyperopt_epochs) == 5
-    assert log_has_re(r"Reading pickled epochs from .*", caplog)
-
-    caplog.clear()
-
-    # Modern version
-    results_file = testdatadir / 'strategy_SampleStrategy.fthypt'
-
-    hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
-
-    assert len(hyperopt_epochs) == 5
-    assert log_has_re(r"Reading epochs from .*", caplog)
+    result_gen = HyperoptTools._read_results(hyperopt.results_file, 1)
+    epoch = next(result_gen)
+    assert len(epoch) == 1
+    assert epoch[0] == epochs[0]
+    epoch = next(result_gen)
+    assert len(epoch) == 1
+    epoch = next(result_gen)
+    assert len(epoch) == 0
+    with pytest.raises(StopIteration):
+        next(result_gen)
 
 
 def test_load_previous_results2(mocker, testdatadir, caplog) -> None:
-    mocker.patch('freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results_pickle',
-                 return_value=[{'asdf': '222'}])
     results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
-    with pytest.raises(OperationalException, match=r"The file .* incompatible.*"):
-        HyperoptTools.load_previous_results(results_file)
+    with pytest.raises(OperationalException,
+                       match=r"Legacy hyperopt results are no longer supported.*"):
+        HyperoptTools.load_filtered_results(results_file, {})
 
 
 @pytest.mark.parametrize("spaces, expected_results", [

tests/plugins/test_protections.py

@@ -93,7 +93,7 @@ def test_stoploss_guard(mocker, default_conf, fee, caplog):
     Trade.query.session.add(generate_mock_trade(
         'XRP/BTC', fee.return_value, False, sell_reason=SellType.STOP_LOSS.value,
         min_ago_open=200, min_ago_close=30,
-        ))
+    ))
 
     assert not freqtrade.protections.global_stop()
     assert not log_has_re(message, caplog)
@@ -150,7 +150,7 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair
     Trade.query.session.add(generate_mock_trade(
         pair, fee.return_value, False, sell_reason=SellType.STOP_LOSS.value,
         min_ago_open=200, min_ago_close=30, profit_rate=0.9,
-        ))
+    ))
 
     assert not freqtrade.protections.stop_per_pair(pair)
     assert not freqtrade.protections.global_stop()

tests/rpc/test_fiat_convert.py

@@ -139,9 +139,9 @@ def test_fiat_too_many_requests_response(mocker, caplog):
     assert length_cryptomap == 0
     assert fiat_convert._backoff > datetime.datetime.now().timestamp()
     assert log_has(
-              'Too many requests for Coingecko API, backing off and trying again later.',
-              caplog
-            )
+        'Too many requests for Coingecko API, backing off and trying again later.',
+        caplog
+    )
 
 
 def test_fiat_invalid_response(mocker, caplog):

tests/rpc/test_rpc_apiserver.py

@@ -942,7 +942,7 @@ def test_api_whitelist(botclient):
         "whitelist": ['ETH/BTC', 'LTC/BTC', 'XRP/BTC', 'NEO/BTC'],
         "length": 4,
         "method": ["StaticPairList"]
-        }
+    }
 
 
 def test_api_forcebuy(botclient, mocker, fee):
@@ -1033,7 +1033,7 @@ def test_api_forcebuy(botclient, mocker, fee):
         'buy_tag': None,
         'timeframe': 5,
         'exchange': 'binance',
-        }
+    }
 
 
 def test_api_forcesell(botclient, mocker, ticker, fee, markets):
@@ -1215,7 +1215,7 @@ def test_api_strategies(botclient):
         'DefaultStrategy',
         'HyperoptableStrategy',
         'TestStrategyLegacy'
-        ]}
+    ]}
 
 
 def test_api_strategy(botclient):

tests/strategy/strats/hyperoptable_strategy.py

@@ -4,7 +4,8 @@ import talib.abstract as ta
 from pandas import DataFrame
 
 import freqtrade.vendor.qtpylib.indicators as qtpylib
-from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, RealParameter
+from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
+                                RealParameter)
 
 
 class HyperoptableStrategy(IStrategy):
@@ -64,6 +65,18 @@ class HyperoptableStrategy(IStrategy):
     sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
     sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
                                     load=False)
+    protection_enabled = BooleanParameter(default=True)
+    protection_cooldown_lookback = IntParameter([0, 50], default=30)
+
+    @property
+    def protections(self):
+        prot = []
+        if self.protection_enabled.value:
+            prot.append({
+                "method": "CooldownPeriod",
+                "stop_duration_candles": self.protection_cooldown_lookback.value
+            })
+        return prot
 
     def informative_pairs(self):
         """

tests/strategy/test_interface.py

@@ -16,8 +16,8 @@ from freqtrade.exceptions import OperationalException, StrategyError
 from freqtrade.optimize.space import SKDecimal
 from freqtrade.persistence import PairLocks, Trade
 from freqtrade.resolvers import StrategyResolver
-from freqtrade.strategy.hyper import (BaseParameter, CategoricalParameter, DecimalParameter,
-                                      IntParameter, RealParameter)
+from freqtrade.strategy.hyper import (BaseParameter, BooleanParameter, CategoricalParameter,
+                                      DecimalParameter, IntParameter, RealParameter)
 from freqtrade.strategy.interface import SellCheckTuple
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from tests.conftest import log_has, log_has_re
@@ -228,25 +228,25 @@ def test_assert_df(ohlcv_history, caplog):
     _STRATEGY.disable_dataframe_checks = False
 
 
-def test_ohlcvdata_to_dataframe(default_conf, testdatadir) -> None:
+def test_advise_all_indicators(default_conf, testdatadir) -> None:
     default_conf.update({'strategy': 'DefaultStrategy'})
     strategy = StrategyResolver.load_strategy(default_conf)
 
     timerange = TimeRange.parse_timerange('1510694220-1510700340')
     data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
                      fill_up_missing=True)
-    processed = strategy.ohlcvdata_to_dataframe(data)
+    processed = strategy.advise_all_indicators(data)
     assert len(processed['UNITTEST/BTC']) == 102  # partial candle was removed
 
 
-def test_ohlcvdata_to_dataframe_copy(mocker, default_conf, testdatadir) -> None:
+def test_advise_all_indicators_copy(mocker, default_conf, testdatadir) -> None:
     default_conf.update({'strategy': 'DefaultStrategy'})
     strategy = StrategyResolver.load_strategy(default_conf)
     aimock = mocker.patch('freqtrade.strategy.interface.IStrategy.advise_indicators')
     timerange = TimeRange.parse_timerange('1510694220-1510700340')
     data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
                      fill_up_missing=True)
-    strategy.ohlcvdata_to_dataframe(data)
+    strategy.advise_all_indicators(data)
     assert aimock.call_count == 1
     # Ensure that a copy of the dataframe is passed to advice_indicators
     assert aimock.call_args_list[0][0][0] is not data
@@ -398,7 +398,7 @@ def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, traili
         exchange='binance',
         open_rate=1,
     )
-    trade.adjust_min_max_rates(trade.open_rate)
+    trade.adjust_min_max_rates(trade.open_rate, trade.open_rate)
     strategy.trailing_stop = trailing
     strategy.trailing_stop_positive = -0.05
     strategy.use_custom_stoploss = custom
@@ -552,6 +552,7 @@ def test__analyze_ticker_internal_skip_analyze(ohlcv_history, mocker, caplog) ->
 def test_is_pair_locked(default_conf):
     default_conf.update({'strategy': 'DefaultStrategy'})
     PairLocks.timeframe = default_conf['timeframe']
+    PairLocks.use_db = True
     strategy = StrategyResolver.load_strategy(default_conf)
     # No lock should be present
     assert len(PairLocks.get_pair_locks(None)) == 0
@@ -717,6 +718,17 @@ def test_hyperopt_parameters():
     assert len(list(catpar.range)) == 3
     assert list(catpar.range) == ['buy_rsi', 'buy_macd', 'buy_none']
 
+    boolpar = BooleanParameter(default=True, space='buy')
+    assert boolpar.value is True
+    assert isinstance(boolpar.get_space(''), Categorical)
+    assert isinstance(boolpar.range, list)
+    assert len(list(boolpar.range)) == 1
+
+    boolpar.in_space = True
+    assert len(list(boolpar.range)) == 2
+
+    assert list(boolpar.range) == [True, False]
+
 
 def test_auto_hyperopt_interface(default_conf):
     default_conf.update({'strategy': 'HyperoptableStrategy'})
@@ -734,7 +746,8 @@ def test_auto_hyperopt_interface(default_conf):
     assert isinstance(all_params, dict)
     assert len(all_params['buy']) == 2
     assert len(all_params['sell']) == 2
-    assert all_params['count'] == 4
+    # Number of Hyperoptable parameters
+    assert all_params['count'] == 6
 
     strategy.__class__.sell_rsi = IntParameter([0, 10], default=5, space='buy')
 

tests/test_arguments.py

@@ -125,7 +125,7 @@ def test_parse_args_backtesting_custom() -> None:
         '--strategy-list',
         'DefaultStrategy',
         'SampleStrategy'
-        ]
+    ]
     call_args = Arguments(args).get_parsed_arg()
     assert call_args['config'] == ['test_conf.json']
     assert call_args['verbosity'] == 0

tests/test_configuration.py

@@ -1130,17 +1130,17 @@ def test_pairlist_resolving_fallback(mocker):
 
 
 @pytest.mark.parametrize("setting", [
-        ("ask_strategy", "use_sell_signal", True,
-         None, "use_sell_signal", False),
-        ("ask_strategy", "sell_profit_only", True,
-         None, "sell_profit_only", False),
-        ("ask_strategy", "sell_profit_offset", 0.1,
-         None, "sell_profit_offset", 0.01),
-        ("ask_strategy", "ignore_roi_if_buy_signal", True,
-         None, "ignore_roi_if_buy_signal", False),
-        ("ask_strategy", "ignore_buying_expired_candle_after", 5,
-         None, "ignore_buying_expired_candle_after", 6),
-    ])
+    ("ask_strategy", "use_sell_signal", True,
+     None, "use_sell_signal", False),
+    ("ask_strategy", "sell_profit_only", True,
+     None, "sell_profit_only", False),
+    ("ask_strategy", "sell_profit_offset", 0.1,
+     None, "sell_profit_offset", 0.01),
+    ("ask_strategy", "ignore_roi_if_buy_signal", True,
+     None, "ignore_roi_if_buy_signal", False),
+    ("ask_strategy", "ignore_buying_expired_candle_after", 5,
+     None, "ignore_buying_expired_candle_after", 6),
+])
 def test_process_temporary_deprecated_settings(mocker, default_conf, setting, caplog):
     patched_configuration_load_config_file(mocker, default_conf)
 
@@ -1180,10 +1180,10 @@ def test_process_temporary_deprecated_settings(mocker, default_conf, setting, ca
 
 
 @pytest.mark.parametrize("setting", [
-        ("experimental", "use_sell_signal", False),
-        ("experimental", "sell_profit_only", True),
-        ("experimental", "ignore_roi_if_buy_signal", True),
-    ])
+    ("experimental", "use_sell_signal", False),
+    ("experimental", "sell_profit_only", True),
+    ("experimental", "ignore_roi_if_buy_signal", True),
+])
 def test_process_removed_settings(mocker, default_conf, setting):
     patched_configuration_load_config_file(mocker, default_conf)
 
@@ -1330,7 +1330,7 @@ def test_process_removed_setting(mocker, default_conf, caplog):
                                 'sectionB', 'somesetting')
 
 
-def test_process_deprecated_ticker_interval(mocker, default_conf, caplog):
+def test_process_deprecated_ticker_interval(default_conf, caplog):
     message = "DEPRECATED: Please use 'timeframe' instead of 'ticker_interval."
     config = deepcopy(default_conf)
     process_temporary_deprecated_settings(config)
@@ -1352,6 +1352,17 @@ def test_process_deprecated_ticker_interval(mocker, default_conf, caplog):
         process_temporary_deprecated_settings(config)
 
 
+def test_process_deprecated_protections(default_conf, caplog):
+    message = "DEPRECATED: Setting 'protections' in the configuration is deprecated."
+    config = deepcopy(default_conf)
+    process_temporary_deprecated_settings(config)
+    assert not log_has(message, caplog)
+
+    config['protections'] = []
+    process_temporary_deprecated_settings(config)
+    assert log_has(message, caplog)
+
+
 def test_flat_vars_to_nested_dict(caplog):
 
     test_args = {

tests/test_persistence.py

@@ -799,25 +799,30 @@ def test_adjust_min_max_rates(fee):
         open_rate=1,
     )
 
-    trade.adjust_min_max_rates(trade.open_rate)
+    trade.adjust_min_max_rates(trade.open_rate, trade.open_rate)
     assert trade.max_rate == 1
     assert trade.min_rate == 1
 
     # check min adjusted, max remained
-    trade.adjust_min_max_rates(0.96)
+    trade.adjust_min_max_rates(0.96, 0.96)
     assert trade.max_rate == 1
     assert trade.min_rate == 0.96
 
     # check max adjusted, min remains
-    trade.adjust_min_max_rates(1.05)
+    trade.adjust_min_max_rates(1.05, 1.05)
     assert trade.max_rate == 1.05
     assert trade.min_rate == 0.96
 
     # current rate "in the middle" - no adjustment
-    trade.adjust_min_max_rates(1.03)
+    trade.adjust_min_max_rates(1.03, 1.03)
     assert trade.max_rate == 1.05
     assert trade.min_rate == 0.96
 
+    # current rate "in the middle" - no adjustment
+    trade.adjust_min_max_rates(1.10, 0.91)
+    assert trade.max_rate == 1.10
+    assert trade.min_rate == 0.91
+
 
 @pytest.mark.usefixtures("init_persistence")
 @pytest.mark.parametrize('use_db', [True, False])
@@ -1219,6 +1224,11 @@ def test_update_order_from_ccxt(caplog):
     assert o.ft_is_open
     assert o.order_filled_date is None
 
+    # Order is unfilled, "filled" not set
+    # https://github.com/freqtrade/freqtrade/issues/5404
+    ccxt_order.update({'filled': None, 'remaining': 20.0, 'status': 'canceled'})
+    o.update_from_ccxt_object(ccxt_order)
+
     # Order has been closed
     ccxt_order.update({'filled': 20.0, 'remaining': 0.0, 'status': 'closed'})
     o.update_from_ccxt_object(ccxt_order)