Merge branch 'develop' into pr/wizrds/7303

config_examples/config_freqai.example.json

@@ -77,7 +77,8 @@
             "indicator_periods_candles": [
                 10,
                 20
-            ]
+            ],
+            "plot_feature_importance": false
         },
         "data_split_parameters": {
             "test_size": 0.33,
@@ -93,4 +94,4 @@
     "internals": {
         "process_throttle_secs": 5
     }
-}
+}

docs/advanced-hyperopt.md

@@ -17,6 +17,7 @@ from typing import Any, Dict
 
 from pandas import DataFrame
 
+from freqtrade.constants import Config
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 TARGET_TRADES = 600
@@ -31,7 +32,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               config: Dict, processed: Dict[str, DataFrame],
+                               config: Config, processed: Dict[str, DataFrame],
                                backtest_stats: Dict[str, Any],
                                *args, **kwargs) -> float:
         """

docs/exchanges.md

@@ -57,7 +57,8 @@ This configuration enables kraken, as well as rate-limiting to avoid bans from t
 Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
 
 !!! Tip "Stoploss on Exchange"
-    Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
+    Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
+    On futures, Binance supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
 
 ### Binance Blacklist
 

docs/freqai.md

@@ -109,11 +109,12 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `indicator_max_period_candles` | **No longer used**. User must use the strategy set `startup_candle_count` which defines the maximum *period* used in `populate_any_indicators()` for indicator creation (timeframe independent). FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
 | `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set. <br> **Datatype:** List of positive integers.
 | `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](#stratifying-the-data-for-training-and-testing-the-model) <br> **Datatype:** Positive integer.
-| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean.
+| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
+| `plot_feature_importance` | Create an interactive feature importance plot for each model.<br> **Datatype:** Boolean.<br> **Datatype:** Boolean, defaults to `False`
 | `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** Positive float (typically < 1).
 | `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
 | `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
-| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan). <br> **Datatype:** Boolean. 
+| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan). <br> **Datatype:** Boolean.
 | `inlier_metric_window` | If set, FreqAI will add the `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`. Details of how the `inlier_metric` is computed can be found [here](#using-the-inliermetric) <br> **Datatype:** int. Default: 0
 | `noise_standard_deviation` | If > 0, FreqAI adds noise to the training features. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. Value should be kept relative to the normalized space between -1 and 1). In other words, since data is always normalized between -1 and 1 in FreqAI, the user can expect a `noise_standard_deviation: 0.05` to see 32% of data randomly increased/decreased by more than 2.5% (i.e. the percent of data falling within the first standard deviation). Good for preventing overfitting. <br> **Datatype:** int. Default: 0
 | `outlier_protection_percentage` | If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection while keeping the original dataset intact. If the outlier protection is triggered, no predictions will be made based on the training data. <br> **Datatype:** Float. Default: `30`
@@ -190,19 +191,6 @@ The FreqAI strategy requires the user to include the following lines of code in
     # passed to any single indicator)
     startup_candle_count: int = 20
 
-    def informative_pairs(self):
-        whitelist_pairs = self.dp.current_whitelist()
-        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
-        informative_pairs = []
-        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
-            for pair in whitelist_pairs:
-                informative_pairs.append((pair, tf))
-            for pair in corr_pairs:
-                if pair in whitelist_pairs:
-                    continue  # avoid duplication
-                informative_pairs.append((pair, tf))
-        return informative_pairs
-
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
         # the model will return all labels created by user in `populate_any_indicators`
@@ -523,7 +511,7 @@ The FreqAI backtesting module can be executed with the following command:
 freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
 ```
 
-Backtesting mode requires the user to have the data [pre-downloaded](#downloading-data-for-backtesting) (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration). 
+Backtesting mode requires the user to have the data [pre-downloaded](#downloading-data-for-backtesting) (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
 
 If this command has never been executed with the existing config file, it will train a new model
 for each pair, for each backtesting window within the expanded `--timerange`.
@@ -551,7 +539,7 @@ Users need to have the data pre-downloaded in the same fashion as if they were d
 - It's not possible to hyperopt indicators in `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
 - The [Backtesting](#backtesting) instructions also apply to Hyperopt.
 
-The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. 
+The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
 
 A good example of a hyperoptable parameter in FreqAI is a value for `DI_values` beyond which we consider outliers and below which we consider inliers:
 
@@ -576,7 +564,7 @@ FreqAI will train have trained 8 separate models at the end of `--timerange` (be
     Although fractional `backtest_period_days` is allowed, the user should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
 
 ### Downloading data for backtesting
-Live/dry instances will download the data automatically for the user, but users who wish to use backtesting functionality still need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). FreqAI users need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that they have a sufficient amount of training data *before* the start of their backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` ([details](#setting-the-startupcandlecount)) from the beginning of the desired backtesting timerange. 
+Live/dry instances will download the data automatically for the user, but users who wish to use backtesting functionality still need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). FreqAI users need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that they have a sufficient amount of training data *before* the start of their backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` ([details](#setting-the-startupcandlecount)) from the beginning of the desired backtesting timerange.
 
 As an example, if we wish to backtest the `--timerange` above of `20210501-20210701`, and we use the example config which sets `train_period_days` to 15. The startup candle count is 40 on a maximum `include_timeframes` of 1h. We would need 20210501 - 15 days - 40 * 1h / 24 hours = 20210414 (16.7 days earlier than the start of the desired training timerange).
 
@@ -675,13 +663,13 @@ The test data is used to evaluate the performance of the model after training. I
 
 ### Using the `inlier_metric`
 
-The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points. 
+The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points.
 
-User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points). 
+User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points).
 
-This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`. 
+This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`.
 
-FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels. 
+FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels.
 
 This function does **not** remove outliers from the data set.
 

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
 mkdocs==1.3.1
-mkdocs-material==8.4.3
+mkdocs-material==8.5.2
 mdx_truly_sane_lists==1.3
 pymdown-extensions==9.5
 jinja2==3.1.2

freqtrade/commands/arguments.py

@@ -62,9 +62,9 @@ ARGS_BUILD_CONFIG = ["config"]
 
 ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
 
-ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
+ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase", "exchange"]
 
-ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes", "exchange", "trading_mode",
+ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes", "trading_mode",
                                                "candle_types"]
 
 ARGS_CONVERT_TRADES = ["pairs", "timeframes", "exchange", "dataformat_ohlcv", "dataformat_trades"]

freqtrade/commands/cli_options.py

@@ -69,7 +69,7 @@ AVAILABLE_CLI_OPTIONS = {
         metavar='PATH',
     ),
     "datadir": Arg(
-        '-d', '--datadir',
+        '-d', '--datadir', '--data-dir',
         help='Path to directory with historical backtesting data.',
         metavar='PATH',
     ),

freqtrade/commands/deploy_commands.py

@@ -36,24 +36,24 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
     """
     fallback = 'full'
     indicators = render_template_with_fallback(
-        templatefile=f"subtemplates/indicators_{subtemplate}.j2",
+        templatefile=f"strategy_subtemplates/indicators_{subtemplate}.j2",
-        templatefallbackfile=f"subtemplates/indicators_{fallback}.j2",
+        templatefallbackfile=f"strategy_subtemplates/indicators_{fallback}.j2",
     )
     buy_trend = render_template_with_fallback(
-        templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",
+        templatefile=f"strategy_subtemplates/buy_trend_{subtemplate}.j2",
-        templatefallbackfile=f"subtemplates/buy_trend_{fallback}.j2",
+        templatefallbackfile=f"strategy_subtemplates/buy_trend_{fallback}.j2",
     )
     sell_trend = render_template_with_fallback(
-        templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",
+        templatefile=f"strategy_subtemplates/sell_trend_{subtemplate}.j2",
-        templatefallbackfile=f"subtemplates/sell_trend_{fallback}.j2",
+        templatefallbackfile=f"strategy_subtemplates/sell_trend_{fallback}.j2",
     )
     plot_config = render_template_with_fallback(
-        templatefile=f"subtemplates/plot_config_{subtemplate}.j2",
+        templatefile=f"strategy_subtemplates/plot_config_{subtemplate}.j2",
-        templatefallbackfile=f"subtemplates/plot_config_{fallback}.j2",
+        templatefallbackfile=f"strategy_subtemplates/plot_config_{fallback}.j2",
     )
     additional_methods = render_template_with_fallback(
-        templatefile=f"subtemplates/strategy_methods_{subtemplate}.j2",
+        templatefile=f"strategy_subtemplates/strategy_methods_{subtemplate}.j2",
-        templatefallbackfile="subtemplates/strategy_methods_empty.j2",
+        templatefallbackfile="strategy_subtemplates/strategy_methods_empty.j2",
     )
 
     strategy_text = render_template(templatefile='base_strategy.py.j2',

freqtrade/configuration/check_exchange.py

@@ -1,6 +1,6 @@
 import logging
-from typing import Any, Dict
 
+from freqtrade.constants import Config
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
@@ -10,7 +10,7 @@ from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
 logger = logging.getLogger(__name__)
 
 
-def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
+def check_exchange(config: Config, check_for_bad: bool = True) -> bool:
     """
     Check if the exchange name in the config file is supported by Freqtrade
     :param check_for_bad: if True, check the exchange against the list of known 'bad'

freqtrade/configuration/configuration.py

@@ -13,6 +13,7 @@ from freqtrade.configuration.deprecated_settings import process_temporary_deprec
 from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
 from freqtrade.configuration.environment_vars import enironment_vars_to_dict
 from freqtrade.configuration.load_config import load_file, load_from_files
+from freqtrade.constants import Config
 from freqtrade.enums import NON_UTIL_MODES, TRADING_MODES, CandleType, RunMode, TradingMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.loggers import setup_logging
@@ -30,10 +31,10 @@ class Configuration:
 
     def __init__(self, args: Dict[str, Any], runmode: RunMode = None) -> None:
         self.args = args
-        self.config: Optional[Dict[str, Any]] = None
+        self.config: Optional[Config] = None
         self.runmode = runmode
 
-    def get_config(self) -> Dict[str, Any]:
+    def get_config(self) -> Config:
         """
         Return the config. Use this method to get the bot config
         :return: Dict: Bot config
@@ -65,7 +66,7 @@ class Configuration:
         :return: Configuration dictionary
         """
         # Load all configs
-        config: Dict[str, Any] = load_from_files(self.args.get("config", []))
+        config: Config = load_from_files(self.args.get("config", []))
 
         # Load environment variables
         env_data = enironment_vars_to_dict()
@@ -108,7 +109,7 @@ class Configuration:
 
         return config
 
-    def _process_logging_options(self, config: Dict[str, Any]) -> None:
+    def _process_logging_options(self, config: Config) -> None:
         """
         Extract information for sys.argv and load logging configuration:
         the -v/--verbose, --logfile options
@@ -121,7 +122,7 @@ class Configuration:
 
         setup_logging(config)
 
-    def _process_trading_options(self, config: Dict[str, Any]) -> None:
+    def _process_trading_options(self, config: Config) -> None:
         if config['runmode'] not in TRADING_MODES:
             return
 
@@ -137,7 +138,7 @@ class Configuration:
 
         logger.info(f'Using DB: "{parse_db_uri_for_logging(config["db_url"])}"')
 
-    def _process_common_options(self, config: Dict[str, Any]) -> None:
+    def _process_common_options(self, config: Config) -> None:
 
         # Set strategy if not specified in config and or if it's non default
         if self.args.get('strategy') or not config.get('strategy'):
@@ -161,7 +162,7 @@ class Configuration:
         if 'sd_notify' in self.args and self.args['sd_notify']:
             config['internals'].update({'sd_notify': True})
 
-    def _process_datadir_options(self, config: Dict[str, Any]) -> None:
+    def _process_datadir_options(self, config: Config) -> None:
         """
         Extract information for sys.argv and load directory configurations
         --user-data, --datadir
@@ -195,7 +196,7 @@ class Configuration:
             config['exportfilename'] = (config['user_data_dir']
                                         / 'backtest_results')
 
-    def _process_optimize_options(self, config: Dict[str, Any]) -> None:
+    def _process_optimize_options(self, config: Config) -> None:
 
         # This will override the strategy configuration
         self._args_to_config(config, argname='timeframe',
@@ -380,7 +381,7 @@ class Configuration:
         self._args_to_config(config, argname="hyperopt_ignore_missing_space",
                              logstring="Paramter --ignore-missing-space detected: {}")
 
-    def _process_plot_options(self, config: Dict[str, Any]) -> None:
+    def _process_plot_options(self, config: Config) -> None:
 
         self._args_to_config(config, argname='pairs',
                              logstring='Using pairs {}')
@@ -432,7 +433,7 @@ class Configuration:
         self._args_to_config(config, argname='show_timerange',
                              logstring='Detected --show-timerange')
 
-    def _process_data_options(self, config: Dict[str, Any]) -> None:
+    def _process_data_options(self, config: Config) -> None:
         self._args_to_config(config, argname='new_pairs_days',
                              logstring='Detected --new-pairs-days: {}')
         self._args_to_config(config, argname='trading_mode',
@@ -443,7 +444,7 @@ class Configuration:
         self._args_to_config(config, argname='candle_types',
                              logstring='Detected --candle-types: {}')
 
-    def _process_analyze_options(self, config: Dict[str, Any]) -> None:
+    def _process_analyze_options(self, config: Config) -> None:
         self._args_to_config(config, argname='analysis_groups',
                              logstring='Analysis reason groups: {}')
 
@@ -456,7 +457,7 @@ class Configuration:
         self._args_to_config(config, argname='indicator_list',
                              logstring='Analysis indicator list: {}')
 
-    def _process_runmode(self, config: Dict[str, Any]) -> None:
+    def _process_runmode(self, config: Config) -> None:
 
         self._args_to_config(config, argname='dry_run',
                              logstring='Parameter --dry-run detected, '
@@ -469,7 +470,7 @@ class Configuration:
 
         config.update({'runmode': self.runmode})
 
-    def _process_freqai_options(self, config: Dict[str, Any]) -> None:
+    def _process_freqai_options(self, config: Config) -> None:
 
         self._args_to_config(config, argname='freqaimodel',
                              logstring='Using freqaimodel class name: {}')
@@ -479,7 +480,7 @@ class Configuration:
 
         return
 
-    def _args_to_config(self, config: Dict[str, Any], argname: str,
+    def _args_to_config(self, config: Config, argname: str,
                         logstring: str, logfun: Optional[Callable] = None,
                         deprecated_msg: Optional[str] = None) -> None:
         """
@@ -502,7 +503,7 @@ class Configuration:
             if deprecated_msg:
                 warnings.warn(f"DEPRECATED: {deprecated_msg}", DeprecationWarning)
 
-    def _resolve_pairs_list(self, config: Dict[str, Any]) -> None:
+    def _resolve_pairs_list(self, config: Config) -> None:
         """
         Helper for download script.
         Takes first found:

freqtrade/configuration/deprecated_settings.py

@@ -3,15 +3,16 @@ Functions to handle deprecated settings
 """
 
 import logging
-from typing import Any, Dict, Optional
+from typing import Optional
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 
 
 logger = logging.getLogger(__name__)
 
 
-def check_conflicting_settings(config: Dict[str, Any],
+def check_conflicting_settings(config: Config,
                                section_old: Optional[str], name_old: str,
                                section_new: Optional[str], name_new: str) -> None:
     section_new_config = config.get(section_new, {}) if section_new else config
@@ -28,7 +29,7 @@ def check_conflicting_settings(config: Dict[str, Any],
         )
 
 
-def process_removed_setting(config: Dict[str, Any],
+def process_removed_setting(config: Config,
                             section1: str, name1: str,
                             section2: Optional[str], name2: str) -> None:
     """
@@ -47,7 +48,7 @@ def process_removed_setting(config: Dict[str, Any],
         )
 
 
-def process_deprecated_setting(config: Dict[str, Any],
+def process_deprecated_setting(config: Config,
                                section_old: Optional[str], name_old: str,
                                section_new: Optional[str], name_new: str
                                ) -> None:
@@ -69,7 +70,7 @@ def process_deprecated_setting(config: Dict[str, Any],
         del section_old_config[name_old]
 
 
-def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
+def process_temporary_deprecated_settings(config: Config) -> None:
 
     # Kept for future deprecated / moved settings
     # check_conflicting_settings(config, 'ask_strategy', 'use_sell_signal',

freqtrade/configuration/directory_operations.py

@@ -1,16 +1,16 @@
 import logging
 import shutil
 from pathlib import Path
-from typing import Any, Dict, Optional
+from typing import Optional
 
-from freqtrade.constants import USER_DATA_FILES
+from freqtrade.constants import USER_DATA_FILES, Config
 from freqtrade.exceptions import OperationalException
 
 
 logger = logging.getLogger(__name__)
 
 
-def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Path:
+def create_datadir(config: Config, datadir: Optional[str] = None) -> Path:
 
     folder = Path(datadir) if datadir else Path(f"{config['user_data_dir']}/data")
     if not datadir:

freqtrade/configuration/load_config.py

@@ -10,7 +10,7 @@ from typing import Any, Dict, List
 
 import rapidjson
 
-from freqtrade.constants import MINIMAL_CONFIG
+from freqtrade.constants import MINIMAL_CONFIG, Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts
 
@@ -80,7 +80,7 @@ def load_from_files(files: List[str], base_path: Path = None, level: int = 0) ->
     Recursively load configuration files if specified.
     Sub-files are assumed to be relative to the initial config.
     """
-    config: Dict[str, Any] = {}
+    config: Config = {}
     if level > 5:
         raise OperationalException("Config loop detected.")
 

freqtrade/constants.py

@@ -3,7 +3,7 @@
 """
 bot constants
 """
-from typing import List, Literal, Tuple
+from typing import Any, Dict, List, Literal, Tuple
 
 from freqtrade.enums import CandleType
 
@@ -547,6 +547,7 @@ CONF_SCHEMA = {
                         "weight_factor": {"type": "number", "default": 0},
                         "principal_component_analysis": {"type": "boolean", "default": False},
                         "use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
+                        "plot_feature_importance": {"type": "boolean", "default": False},
                         "svm_params": {"type": "object",
                                        "properties": {
                                            "shuffle": {"type": "boolean", "default": False},
@@ -646,3 +647,5 @@ LongShort = Literal['long', 'short']
 EntryExit = Literal['entry', 'exit']
 BuySell = Literal['buy', 'sell']
 MakerTaker = Literal['maker', 'taker']
+
+Config = Dict[str, Any]

freqtrade/data/converter.py

@@ -5,12 +5,12 @@ import itertools
 import logging
 from datetime import datetime, timezone
 from operator import itemgetter
-from typing import Any, Dict, List
+from typing import Dict, List
 
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, Config, TradeList
 from freqtrade.enums import CandleType
 
 
@@ -237,7 +237,7 @@ def trades_to_ohlcv(trades: TradeList, timeframe: str) -> DataFrame:
     return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
 
 
-def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
+def convert_trades_format(config: Config, convert_from: str, convert_to: str, erase: bool):
     """
     Convert trades from one format to another format.
     :param config: Config dictionary
@@ -263,7 +263,7 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
 
 
 def convert_ohlcv_format(
-    config: Dict[str, Any],
+    config: Config,
     convert_from: str,
     convert_to: str,
     erase: bool,

freqtrade/data/dataprovider.py

@@ -12,7 +12,7 @@ from typing import Any, Dict, List, Optional, Tuple
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
+from freqtrade.constants import Config, ListPairsWithTimeframes, PairWithTimeframe
 from freqtrade.data.history import load_pair_history
 from freqtrade.enums import CandleType, RPCMessageType, RunMode
 from freqtrade.exceptions import ExchangeError, OperationalException
@@ -31,7 +31,7 @@ class DataProvider:
 
     def __init__(
         self,
-        config: dict,
+        config: Config,
         exchange: Optional[Exchange],
         pairlists=None,
         rpc: Optional[RPCManager] = None

freqtrade/data/history/hdf5datahandler.py

@@ -1,7 +1,5 @@
 import logging
-import re
+from typing import Optional
-from pathlib import Path
-from typing import List, Optional
 
 import numpy as np
 import pandas as pd
@@ -20,26 +18,6 @@ class HDF5DataHandler(IDataHandler):
 
     _columns = DEFAULT_DATAFRAME_COLUMNS
 
-    @classmethod
-    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
-        """
-        Returns a list of all pairs with ohlcv data available in this datadir
-        for the specified timeframe
-        :param datadir: Directory to search for ohlcv files
-        :param timeframe: Timeframe to search pairs for
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
-        :return: List of Pairs
-        """
-        candle = ""
-        if candle_type != CandleType.SPOT:
-            datadir = datadir.joinpath('futures')
-            candle = f"-{candle_type}"
-
-        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.h5)', p.name)
-                for p in datadir.glob(f"*{timeframe}{candle}.h5")]
-        # Check if regex found something and only return these results
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
-
     def ohlcv_store(
             self, pair: str, timeframe: str, data: pd.DataFrame, candle_type: CandleType) -> None:
         """
@@ -121,18 +99,6 @@ class HDF5DataHandler(IDataHandler):
         """
         raise NotImplementedError()
 
-    @classmethod
-    def trades_get_pairs(cls, datadir: Path) -> List[str]:
-        """
-        Returns a list of all pairs for which trade data is available in this
-        :param datadir: Directory to search for ohlcv files
-        :return: List of Pairs
-        """
-        _tmp = [re.search(r'^(\S+)(?=\-trades.h5)', p.name)
-                for p in datadir.glob("*trades.h5")]
-        # Check if regex found something and only return these results to avoid exceptions.
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
-
     def trades_store(self, pair: str, data: TradeList) -> None:
         """
         Store trades data (list of Dicts) to file

freqtrade/data/history/idatahandler.py

@@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
 
 class IDataHandler(ABC):
 
-    _OHLCV_REGEX = r'^([a-zA-Z_-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
+    _OHLCV_REGEX = r'^([a-zA-Z_\d-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
 
     def __init__(self, datadir: Path) -> None:
         self._datadir = datadir
@@ -61,7 +61,6 @@ class IDataHandler(ABC):
             ) for match in _tmp if match and len(match.groups()) > 1]
 
     @classmethod
-    @abstractmethod
     def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
         """
         Returns a list of all pairs with ohlcv data available in this datadir
@@ -71,6 +70,15 @@ class IDataHandler(ABC):
         :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: List of Pairs
         """
+        candle = ""
+        if candle_type != CandleType.SPOT:
+            datadir = datadir.joinpath('futures')
+            candle = f"-{candle_type}"
+        ext = cls._get_file_extension()
+        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + f'.{ext})', p.name)
+                for p in datadir.glob(f"*{timeframe}{candle}.{ext}")]
+        # Check if regex found something and only return these results
+        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
 
     @abstractmethod
     def ohlcv_store(
@@ -144,13 +152,17 @@ class IDataHandler(ABC):
         """
 
     @classmethod
-    @abstractmethod
     def trades_get_pairs(cls, datadir: Path) -> List[str]:
         """
         Returns a list of all pairs for which trade data is available in this
         :param datadir: Directory to search for ohlcv files
         :return: List of Pairs
         """
+        _ext = cls._get_file_extension()
+        _tmp = [re.search(r'^(\S+)(?=\-trades.' + _ext + ')', p.name)
+                for p in datadir.glob(f"*trades.{_ext}")]
+        # Check if regex found something and only return these results to avoid exceptions.
+        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
 
     @abstractmethod
     def trades_store(self, pair: str, data: TradeList) -> None:
@@ -255,7 +267,7 @@ class IDataHandler(ABC):
         Rebuild pair name from filename
         Assumes a asset name of max. 7 length to also support BTC-PERP and BTC-PERP:USD names.
         """
-        res = re.sub(r'^(([A-Za-z]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
+        res = re.sub(r'^(([A-Za-z\d]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
         res = re.sub('_', ':', res, 1)
         return res
 

freqtrade/data/history/jsondatahandler.py

@@ -1,7 +1,5 @@
 import logging
-import re
+from typing import Optional
-from pathlib import Path
-from typing import List, Optional
 
 import numpy as np
 from pandas import DataFrame, read_json, to_datetime
@@ -23,26 +21,6 @@ class JsonDataHandler(IDataHandler):
     _use_zip = False
     _columns = DEFAULT_DATAFRAME_COLUMNS
 
-    @classmethod
-    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
-        """
-        Returns a list of all pairs with ohlcv data available in this datadir
-        for the specified timeframe
-        :param datadir: Directory to search for ohlcv files
-        :param timeframe: Timeframe to search pairs for
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
-        :return: List of Pairs
-        """
-        candle = ""
-        if candle_type != CandleType.SPOT:
-            datadir = datadir.joinpath('futures')
-            candle = f"-{candle_type}"
-
-        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.json)', p.name)
-                for p in datadir.glob(f"*{timeframe}{candle}.{cls._get_file_extension()}")]
-        # Check if regex found something and only return these results
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
-
     def ohlcv_store(
             self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
         """
@@ -119,18 +97,6 @@ class JsonDataHandler(IDataHandler):
         """
         raise NotImplementedError()
 
-    @classmethod
-    def trades_get_pairs(cls, datadir: Path) -> List[str]:
-        """
-        Returns a list of all pairs for which trade data is available in this
-        :param datadir: Directory to search for ohlcv files
-        :return: List of Pairs
-        """
-        _tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name)
-                for p in datadir.glob(f"*trades.{cls._get_file_extension()}")]
-        # Check if regex found something and only return these results to avoid exceptions.
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
-
     def trades_store(self, pair: str, data: TradeList) -> None:
         """
         Store trades data (list of Dicts) to file

freqtrade/edge/edge_positioning.py

@@ -11,7 +11,7 @@ import utils_find_1st as utf1st
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
+from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT, Config
 from freqtrade.data.history import get_timerange, load_data, refresh_data
 from freqtrade.enums import CandleType, ExitType, RunMode
 from freqtrade.exceptions import OperationalException
@@ -42,10 +42,9 @@ class Edge:
     Author: https://github.com/mishaker
     """
 
-    config: Dict = {}
     _cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
 
-    def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
+    def __init__(self, config: Config, exchange, strategy) -> None:
 
         self.config = config
         self.exchange = exchange

freqtrade/exchange/binance.py

@@ -1,5 +1,4 @@
 """ Binance exchange subclass """
-import json
 import logging
 from datetime import datetime
 from pathlib import Path
@@ -12,7 +11,7 @@ from freqtrade.enums import CandleType, MarginMode, TradingMode
 from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.common import retrier
-from freqtrade.misc import deep_merge_dicts
+from freqtrade.misc import deep_merge_dicts, json_load
 
 
 logger = logging.getLogger(__name__)
@@ -31,7 +30,7 @@ class Binance(Exchange):
         "ccxt_futures_name": "future"
     }
     _ft_has_futures: Dict = {
-        "stoploss_order_types": {"limit": "stop"},
+        "stoploss_order_types": {"limit": "limit", "market": "market"},
         "tickers_have_price": False,
     }
 
@@ -48,13 +47,12 @@ class Binance(Exchange):
         Returns True if adjustment is necessary.
         :param side: "buy" or "sell"
         """
-
+        order_types = ('stop_loss_limit', 'stop', 'stop_market')
-        ordertype = 'stop' if self.trading_mode == TradingMode.FUTURES else 'stop_loss_limit'
 
         return (
             order.get('stopPrice', None) is None
             or (
-                order['type'] == ordertype
+                order['type'] in order_types
                 and (
                     (side == "sell" and stop_loss > float(order['stopPrice'])) or
                     (side == "buy" and stop_loss < float(order['stopPrice']))
@@ -201,7 +199,7 @@ class Binance(Exchange):
                     Path(__file__).parent / 'binance_leverage_tiers.json'
                 )
                 with open(leverage_tiers_path) as json_file:
-                    return json.load(json_file)
+                    return json_load(json_file)
             else:
                 try:
                     return self._api.fetch_leverage_tiers()

freqtrade/exchange/binance_leverage_tiers.json

@@ -81,6 +81,104 @@
       }
     }
   ],
+  "1000LUNC/USDT": [
+    {
+      "tier": 1.0,
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 25.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "25",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 25000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "20",
+        "notionalCap": "25000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.025",
+        "cum": "75.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "currency": "USDT",
+      "minNotional": 25000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "10",
+        "notionalCap": "100000",
+        "notionalFloor": "25000",
+        "maintMarginRatio": "0.05",
+        "cum": "700.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "5",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1",
+        "cum": "5700.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "currency": "USDT",
+      "minNotional": 250000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "2",
+        "notionalCap": "1000000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.125",
+        "cum": "11950.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "currency": "USDT",
+      "minNotional": 1000000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.5",
+        "cum": "386950.0"
+      }
+    }
+  ],
   "1000SHIB/BUSD": [
     {
       "tier": 1.0,
@@ -1109,6 +1207,88 @@
       }
     }
   ],
+  "AMB/BUSD": [
+    {
+      "tier": 1.0,
+      "currency": "BUSD",
+      "minNotional": 0.0,
+      "maxNotional": 25000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "20",
+        "notionalCap": "25000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.025",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "currency": "BUSD",
+      "minNotional": 25000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "10",
+        "notionalCap": "100000",
+        "notionalFloor": "25000",
+        "maintMarginRatio": "0.05",
+        "cum": "625.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "currency": "BUSD",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "5",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1",
+        "cum": "5625.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "currency": "BUSD",
+      "minNotional": 250000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "2",
+        "notionalCap": "1000000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.125",
+        "cum": "11875.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "currency": "BUSD",
+      "minNotional": 1000000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.5",
+        "cum": "386875.0"
+      }
+    }
+  ],
   "ANC/BUSD": [
     {
       "tier": 1.0,
@@ -3300,13 +3480,13 @@
       "tier": 6.0,
       "currency": "USDT",
       "minNotional": 1000000.0,
-      "maxNotional": 30000000.0,
+      "maxNotional": 5000000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
         "bracket": "6",
         "initialLeverage": "1",
-        "notionalCap": "30000000",
+        "notionalCap": "5000000",
         "notionalFloor": "1000000",
         "maintMarginRatio": "0.5",
         "cum": "386950.0"
@@ -4880,13 +5060,13 @@
       "tier": 6.0,
       "currency": "USDT",
       "minNotional": 1000000.0,
-      "maxNotional": 30000000.0,
+      "maxNotional": 5000000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
         "bracket": "6",
         "initialLeverage": "1",
-        "notionalCap": "30000000",
+        "notionalCap": "5000000",
         "notionalFloor": "1000000",
         "maintMarginRatio": "0.5",
         "cum": "386940.0"
@@ -8333,6 +8513,104 @@
       }
     }
   ],
+  "FOOTBALL/USDT": [
+    {
+      "tier": 1.0,
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 25.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "25",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 25000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "20",
+        "notionalCap": "25000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.025",
+        "cum": "75.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "currency": "USDT",
+      "minNotional": 25000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "10",
+        "notionalCap": "100000",
+        "notionalFloor": "25000",
+        "maintMarginRatio": "0.05",
+        "cum": "700.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "5",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1",
+        "cum": "5700.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "currency": "USDT",
+      "minNotional": 250000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "2",
+        "notionalCap": "1000000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.125",
+        "cum": "11950.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "currency": "USDT",
+      "minNotional": 1000000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.5",
+        "cum": "386950.0"
+      }
+    }
+  ],
   "FTM/BUSD": [
     {
       "tier": 1.0,
@@ -12123,6 +12401,104 @@
       }
     }
   ],
+  "LUNA2/USDT": [
+    {
+      "tier": 1.0,
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.015,
+      "maxLeverage": 25.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "25",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.015",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 25000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "20",
+        "notionalCap": "25000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.025",
+        "cum": "50.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "currency": "USDT",
+      "minNotional": 25000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "10",
+        "notionalCap": "100000",
+        "notionalFloor": "25000",
+        "maintMarginRatio": "0.05",
+        "cum": "675.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "5",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1",
+        "cum": "5675.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "currency": "USDT",
+      "minNotional": 250000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "2",
+        "notionalCap": "1000000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.125",
+        "cum": "11925.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "currency": "USDT",
+      "minNotional": 1000000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.5",
+        "cum": "386925.0"
+      }
+    }
+  ],
   "MANA/USDT": [
     {
       "tier": 1.0,
@@ -13028,10 +13404,10 @@
       "minNotional": 0.0,
       "maxNotional": 5000.0,
       "maintenanceMarginRate": 0.01,
-      "maxLeverage": 50.0,
+      "maxLeverage": 25.0,
       "info": {
         "bracket": "1",
-        "initialLeverage": "50",
+        "initialLeverage": "25",
         "notionalCap": "5000",
         "notionalFloor": "0",
         "maintMarginRatio": "0.01",
@@ -13805,6 +14181,88 @@
       }
     }
   ],
+  "PHB/BUSD": [
+    {
+      "tier": 1.0,
+      "currency": "BUSD",
+      "minNotional": 0.0,
+      "maxNotional": 25000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "20",
+        "notionalCap": "25000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.025",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "currency": "BUSD",
+      "minNotional": 25000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "10",
+        "notionalCap": "100000",
+        "notionalFloor": "25000",
+        "maintMarginRatio": "0.05",
+        "cum": "625.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "currency": "BUSD",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "5",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1",
+        "cum": "5625.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "currency": "BUSD",
+      "minNotional": 250000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "2",
+        "notionalCap": "1000000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.125",
+        "cum": "11875.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "currency": "BUSD",
+      "minNotional": 1000000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.5",
+        "cum": "386875.0"
+      }
+    }
+  ],
   "QTUM/USDT": [
     {
       "tier": 1.0,
@@ -14008,10 +14466,10 @@
       "minNotional": 0.0,
       "maxNotional": 5000.0,
       "maintenanceMarginRate": 0.01,
-      "maxLeverage": 50.0,
+      "maxLeverage": 25.0,
       "info": {
         "bracket": "1",
-        "initialLeverage": "50",
+        "initialLeverage": "25",
         "notionalCap": "5000",
         "notionalFloor": "0",
         "maintMarginRatio": "0.01",
@@ -14478,13 +14936,13 @@
       "tier": 6.0,
       "currency": "USDT",
       "minNotional": 1000000.0,
-      "maxNotional": 30000000.0,
+      "maxNotional": 5000000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
         "bracket": "6",
         "initialLeverage": "1",
-        "notionalCap": "30000000",
+        "notionalCap": "5000000",
         "notionalFloor": "1000000",
         "maintMarginRatio": "0.5",
         "cum": "386950.0"
@@ -14576,13 +15034,13 @@
       "tier": 6.0,
       "currency": "USDT",
       "minNotional": 1000000.0,
-      "maxNotional": 30000000.0,
+      "maxNotional": 5000000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
         "bracket": "6",
         "initialLeverage": "1",
-        "notionalCap": "30000000",
+        "notionalCap": "5000000",
         "notionalFloor": "1000000",
         "maintMarginRatio": "0.5",
         "cum": "386950.0"
@@ -15487,6 +15945,104 @@
       }
     }
   ],
+  "SPELL/USDT": [
+    {
+      "tier": 1.0,
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 25.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "25",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 25000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "20",
+        "notionalCap": "25000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.025",
+        "cum": "75.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "currency": "USDT",
+      "minNotional": 25000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "10",
+        "notionalCap": "100000",
+        "notionalFloor": "25000",
+        "maintMarginRatio": "0.05",
+        "cum": "700.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "5",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1",
+        "cum": "5700.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "currency": "USDT",
+      "minNotional": 250000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "2",
+        "notionalCap": "1000000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.125",
+        "cum": "11950.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "currency": "USDT",
+      "minNotional": 1000000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.5",
+        "cum": "386950.0"
+      }
+    }
+  ],
   "SRM/USDT": [
     {
       "tier": 1.0,
@@ -15585,6 +16141,104 @@
       }
     }
   ],
+  "STG/USDT": [
+    {
+      "tier": 1.0,
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 25.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "25",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 25000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "20",
+        "notionalCap": "25000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.025",
+        "cum": "75.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "currency": "USDT",
+      "minNotional": 25000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "10",
+        "notionalCap": "100000",
+        "notionalFloor": "25000",
+        "maintMarginRatio": "0.05",
+        "cum": "700.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "5",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1",
+        "cum": "5700.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "currency": "USDT",
+      "minNotional": 250000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "2",
+        "notionalCap": "1000000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.125",
+        "cum": "11950.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "currency": "USDT",
+      "minNotional": 1000000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.5",
+        "cum": "386950.0"
+      }
+    }
+  ],
   "STMX/USDT": [
     {
       "tier": 1.0,
@@ -16176,13 +16830,13 @@
       "tier": 5.0,
       "currency": "BUSD",
       "minNotional": 1000000.0,
-      "maxNotional": 30000000.0,
+      "maxNotional": 5000000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
         "bracket": "5",
         "initialLeverage": "1",
-        "notionalCap": "30000000",
+        "notionalCap": "5000000",
         "notionalFloor": "1000000",
         "maintMarginRatio": "0.5",
         "cum": "386875.0"
@@ -16470,13 +17124,13 @@
       "tier": 6.0,
       "currency": "USDT",
       "minNotional": 1000000.0,
-      "maxNotional": 30000000.0,
+      "maxNotional": 5000000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
         "bracket": "6",
         "initialLeverage": "1",
-        "notionalCap": "30000000",
+        "notionalCap": "5000000",
         "notionalFloor": "1000000",
         "maintMarginRatio": "0.5",
         "cum": "386950.0"

freqtrade/exchange/exchange.py

@@ -21,7 +21,8 @@ from dateutil import parser
 from pandas import DataFrame
 
 from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
-                                 EntryExit, ListPairsWithTimeframes, MakerTaker, PairWithTimeframe)
+                                 Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
+                                 PairWithTimeframe)
 from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
 from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
@@ -91,7 +92,7 @@ class Exchange:
         # TradingMode.SPOT always supported and not required in this list
     ]
 
-    def __init__(self, config: Dict[str, Any], validate: bool = True,
+    def __init__(self, config: Config, validate: bool = True,
                  load_leverage_tiers: bool = False) -> None:
         """
         Initializes this module with the given config,
@@ -108,7 +109,7 @@ class Exchange:
         self._loop_lock = Lock()
         self.loop = asyncio.new_event_loop()
         asyncio.set_event_loop(self.loop)
-        self._config: Dict = {}
+        self._config: Config = {}
 
         self._config.update(config)
 
@@ -2304,7 +2305,7 @@ class Exchange:
             updated = tiers.get('updated')
             if updated:
                 updated_dt = parser.parse(updated)
-                if updated_dt < datetime.now(timezone.utc) - timedelta(days=1):
+                if updated_dt < datetime.now(timezone.utc) - timedelta(weeks=4):
                     logger.info("Cached leverage tiers are outdated. Will update.")
                     return None
             return tiers['data']

freqtrade/freqai/data_drawer.py

@@ -16,6 +16,7 @@ from numpy.typing import NDArray
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
 from freqtrade.data.history import load_pair_history
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@@ -27,9 +28,7 @@ logger = logging.getLogger(__name__)
 
 class pair_info(TypedDict):
     model_filename: str
-    first: bool
     trained_timestamp: int
-    priority: int
     data_path: str
     extras: dict
 
@@ -58,7 +57,7 @@ class FreqaiDataDrawer:
     Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
     """
 
-    def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
+    def __init__(self, full_path: Path, config: Config, follow_mode: bool = False):
 
         self.config = config
         self.freqai_info = config.get("freqai", {})
@@ -91,7 +90,7 @@ class FreqaiDataDrawer:
         self.old_DBSCAN_eps: Dict[str, float] = {}
         self.empty_pair_dict: pair_info = {
                 "model_filename": "", "trained_timestamp": 0,
-                "priority": 1, "first": True, "data_path": "", "extras": {}}
+                "data_path": "", "extras": {}}
 
     def load_drawer_from_disk(self):
         """
@@ -216,7 +215,6 @@ class FreqaiDataDrawer:
             self.pair_dict[pair] = self.empty_pair_dict.copy()
             model_filename = ""
             trained_timestamp = 0
-            self.pair_dict[pair]["priority"] = len(self.pair_dict)
 
         if not data_path_set and self.follow_mode:
             logger.warning(
@@ -236,18 +234,9 @@ class FreqaiDataDrawer:
             return
         else:
             self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
-            self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
 
             return
 
-    def pair_to_end_of_training_queue(self, pair: str) -> None:
-        # march all pairs up in the queue
-        with self.pair_dict_lock:
-            for p in self.pair_dict:
-                self.pair_dict[p]["priority"] -= 1
-            # send pair to end of queue
-            self.pair_dict[pair]["priority"] = len(self.pair_dict)
-
     def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
         """
         Set the initial return values to the historical predictions dataframe. This avoids needing

freqtrade/freqai/data_kitchen.py

@@ -18,6 +18,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.neighbors import NearestNeighbors
 
 from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.strategy.interface import IStrategy
@@ -57,7 +58,7 @@ class FreqaiDataKitchen:
 
     def __init__(
         self,
-        config: Dict[str, Any],
+        config: Config,
         live: bool = False,
         pair: str = "",
     ):

freqtrade/freqai/freqai_interface.py

@@ -3,6 +3,7 @@ import shutil
 import threading
 import time
 from abc import ABC, abstractmethod
+from collections import deque
 from datetime import datetime, timezone
 from pathlib import Path
 from threading import Lock
@@ -14,12 +15,13 @@ from numpy.typing import NDArray
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DATETIME_PRINT_FORMAT
+from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.freqai.data_drawer import FreqaiDataDrawer
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.utils import plot_feature_importance
 from freqtrade.strategy.interface import IStrategy
 
 
@@ -50,7 +52,7 @@ class IFreqaiModel(ABC):
     Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
     """
 
-    def __init__(self, config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config) -> None:
 
         self.config = config
         self.assert_config(self.config)
@@ -80,6 +82,7 @@ class IFreqaiModel(ABC):
         self.pair_it = 0
         self.pair_it_train = 0
         self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
+        self.train_queue = self._set_train_queue()
         self.last_trade_database_summary: DataFrame = {}
         self.current_trade_database_summary: DataFrame = {}
         self.analysis_lock = Lock()
@@ -99,7 +102,7 @@ class IFreqaiModel(ABC):
         """
         return ({})
 
-    def assert_config(self, config: Dict[str, Any]) -> None:
+    def assert_config(self, config: Config) -> None:
 
         if not config.get("freqai", {}):
             raise OperationalException("No freqai parameters found in configuration file.")
@@ -181,29 +184,36 @@ class IFreqaiModel(ABC):
         """
         while not self._stop_event.is_set():
             time.sleep(1)
-            for pair in self.config.get("exchange", {}).get("pair_whitelist"):
+            pair = self.train_queue[0]
 
-                (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
+            # ensure pair is avaialble in dp
+            if pair not in strategy.dp.current_whitelist():
+                self.train_queue.popleft()
+                logger.warning(f'{pair} not in current whitelist, removing from train queue.')
+                continue
 
-                if self.dd.pair_dict[pair]["priority"] != 1:
+            (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
-                    continue
-                dk = FreqaiDataKitchen(self.config, self.live, pair)
-                dk.set_paths(pair, trained_timestamp)
-                (
-                    retrain,
-                    new_trained_timerange,
-                    data_load_timerange,
-                ) = dk.check_if_new_training_required(trained_timestamp)
-                dk.set_paths(pair, new_trained_timerange.stopts)
 
-                if retrain:
+            dk = FreqaiDataKitchen(self.config, self.live, pair)
-                    self.train_timer('start')
+            dk.set_paths(pair, trained_timestamp)
-                    self.extract_data_and_train_model(
+            (
-                        new_trained_timerange, pair, strategy, dk, data_load_timerange
+                retrain,
-                    )
+                new_trained_timerange,
-                    self.train_timer('stop')
+                data_load_timerange,
+            ) = dk.check_if_new_training_required(trained_timestamp)
+            dk.set_paths(pair, new_trained_timerange.stopts)
 
-            self.dd.save_historic_predictions_to_disk()
+            if retrain:
+                self.train_timer('start')
+                self.extract_data_and_train_model(
+                    new_trained_timerange, pair, strategy, dk, data_load_timerange
+                )
+                self.train_timer('stop')
+
+                # only rotate the queue after the first has been trained.
+                self.train_queue.rotate(-1)
+
+                self.dd.save_historic_predictions_to_disk()
 
     def start_backtesting(
         self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
@@ -557,11 +567,11 @@ class IFreqaiModel(ABC):
 
         self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
         dk.set_new_model_names(pair, new_trained_timerange)
-        self.dd.pair_dict[pair]["first"] = False
-        if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
-            self.dd.pair_to_end_of_training_queue(pair)
         self.dd.save_data(model, pair, dk)
 
+        if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
+            plot_feature_importance(model, pair, dk)
+
         if self.freqai_info.get("purge_old_models", False):
             self.dd.purge_old_models()
 
@@ -685,6 +695,32 @@ class IFreqaiModel(ABC):
 
         return init_model
 
+    def _set_train_queue(self):
+        """
+        Sets train queue from existing train timestamps if they exist
+        otherwise it sets the train queue based on the provided whitelist.
+        """
+        current_pairlist = self.config.get("exchange", {}).get("pair_whitelist")
+        if not self.dd.pair_dict:
+            logger.info('Set fresh train queue from whitelist. '
+                        f'Queue: {current_pairlist}')
+            return deque(current_pairlist)
+
+        best_queue = deque()
+
+        pair_dict_sorted = sorted(self.dd.pair_dict.items(),
+                                  key=lambda k: k[1]['trained_timestamp'])
+        for pair in pair_dict_sorted:
+            if pair[0] in current_pairlist:
+                best_queue.append(pair[0])
+        for pair in current_pairlist:
+            if pair not in best_queue:
+                best_queue.appendleft(pair)
+
+        logger.info('Set existing queue from trained timestamps. '
+                    f'Best approximation queue: {best_queue}')
+        return best_queue
+
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.
 

freqtrade/freqai/utils.py

@@ -1,19 +1,25 @@
 import logging
 from datetime import datetime, timezone
+from typing import Any
+
+import numpy as np
+import pandas as pd
 
 from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.exchange.exchange import market_is_active
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
 
 
 logger = logging.getLogger(__name__)
 
 
-def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
+def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
     """
     Called only once upon start of bot to download the necessary data for
     populating indicators and training the model.
@@ -47,9 +53,7 @@ def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
     )
 
 
-def get_required_data_timerange(
+def get_required_data_timerange(config: Config) -> TimeRange:
-    config: dict
-) -> TimeRange:
     """
     Used to compute the required data download time range
     for auto data-download in FreqAI
@@ -86,7 +90,7 @@ def get_required_data_timerange(
 
 
 # Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
-# def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
+# def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
 #     """
 #     Called only once upon start of bot to download the necessary data for
 #     populating indicators and training a FreqAI model.
@@ -132,3 +136,58 @@ def get_required_data_timerange(
 #             trading_mode=config.get("trading_mode", "spot"),
 #             prepend=config.get("prepend_data", False),
 #         )
+
+
+def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
+                            count_max: int = 25) -> None:
+    """
+        Plot Best and worst features by importance for a single sub-train.
+        :param model: Any = A model which was `fit` using a common library
+                            such as catboost or lightgbm
+        :param pair: str = pair e.g. BTC/USD
+        :param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
+        :param count_max: int = the amount of features to be loaded per column
+    """
+    from freqtrade.plot.plotting import go, make_subplots, store_plot_file
+
+    # Extract feature importance from model
+    models = {}
+    if 'FreqaiMultiOutputRegressor' in str(model.__class__):
+        for estimator, label in zip(model.estimators_, dk.label_list):
+            models[label] = estimator
+    else:
+        models[dk.label_list[0]] = model
+
+    for label in models:
+        mdl = models[label]
+        if "catboost.core" in str(mdl.__class__):
+            feature_importance = mdl.get_feature_importance()
+        elif "lightgbm.sklearn" or "xgb" in str(mdl.__class__):
+            feature_importance = mdl.feature_importances_
+        else:
+            logger.info('Model type not support for generating feature importances.')
+            return
+
+        # Data preparation
+        fi_df = pd.DataFrame({
+            "feature_names": np.array(dk.training_features_list),
+            "feature_importance": np.array(feature_importance)
+        })
+        fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
+        fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
+
+        # Plotting
+        def add_feature_trace(fig, fi_df, col):
+            return fig.add_trace(
+                go.Bar(
+                    x=fi_df["feature_importance"],
+                    y=fi_df["feature_names"],
+                    orientation='h', showlegend=False
+                ), row=1, col=col
+            )
+        fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
+        fig = add_feature_trace(fig, fi_df_top, 1)
+        fig = add_feature_trace(fig, fi_df_worst, 2)
+        fig.update_layout(title_text=f"Best and worst features by importance {pair}")
+        label = label.replace('&', '').replace('%', '')  # escape two FreqAI specific characters
+        store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)

freqtrade/freqtradebot.py

@@ -13,7 +13,7 @@ from schedule import Scheduler
 
 from freqtrade import __version__, constants
 from freqtrade.configuration import validate_config_consistency
-from freqtrade.constants import BuySell, LongShort
+from freqtrade.constants import BuySell, Config, LongShort
 from freqtrade.data.converter import order_book_to_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
@@ -45,7 +45,7 @@ class FreqtradeBot(LoggingMixin):
     This is from here the bot start its logic.
     """
 
-    def __init__(self, config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config) -> None:
         """
         Init all variables and objects the bot needs to work
         :param config: configuration dict, you can use Configuration.get_config()
@@ -1085,6 +1085,7 @@ class FreqtradeBot(LoggingMixin):
             order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
             trade.orders.append(order_obj)
             trade.stoploss_order_id = str(stoploss_order['id'])
+            trade.stoploss_last_update = datetime.now(timezone.utc)
             return True
         except InsufficientFundsError as e:
             logger.warning(f"Unable to place stoploss order {e}.")
@@ -1158,10 +1159,9 @@ class FreqtradeBot(LoggingMixin):
             if self.create_stoploss_order(trade=trade, stop_price=stop_price):
                 # The above will return False if the placement failed and the trade was force-sold.
                 # in which case the trade will be closed - which we must check below.
-                trade.stoploss_last_update = datetime.utcnow()
                 return False
 
-        # If stoploss order is canceled for some reason we add it
+        # If stoploss order is canceled for some reason we add it again
         if (trade.is_open
                 and stoploss_order
                 and stoploss_order['status'] in ('canceled', 'cancelled')):
@@ -1199,7 +1199,8 @@ class FreqtradeBot(LoggingMixin):
         if self.exchange.stoploss_adjust(stoploss_norm, order, side=trade.exit_side):
             # we check if the update is necessary
             update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
-            if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
+            upd_req = datetime.now(timezone.utc) - timedelta(seconds=update_beat)
+            if trade.stoploss_last_update_utc and upd_req >= trade.stoploss_last_update_utc:
                 # cancelling the current stoploss on exchange first
                 logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
                             f"(orderid:{order['id']}) in order to add another one ...")

freqtrade/loggers.py

@@ -2,8 +2,8 @@ import logging
 import sys
 from logging import Formatter
 from logging.handlers import BufferingHandler, RotatingFileHandler, SysLogHandler
-from typing import Any, Dict
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 
 
@@ -73,7 +73,7 @@ def setup_logging_pre() -> None:
     )
 
 
-def setup_logging(config: Dict[str, Any]) -> None:
+def setup_logging(config: Config) -> None:
     """
     Process -v/--verbose, --logfile options
     """

freqtrade/optimize/backtesting.py

@@ -15,7 +15,7 @@ from pandas import DataFrame
 
 from freqtrade import constants
 from freqtrade.configuration import TimeRange, validate_config_consistency
-from freqtrade.constants import DATETIME_PRINT_FORMAT, LongShort
+from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, LongShort
 from freqtrade.data import history
 from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
@@ -70,7 +70,7 @@ class Backtesting:
     backtesting.start()
     """
 
-    def __init__(self, config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config) -> None:
 
         LoggingMixin.show_output = False
         self.config = config
@@ -812,14 +812,6 @@ class Backtesting:
             return trade
         time_in_force = self.strategy.order_time_in_force['entry']
 
-        if not pos_adjust:
-            # Confirm trade entry:
-            if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
-                    pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
-                    time_in_force=time_in_force, current_time=current_time,
-                    entry_tag=entry_tag, side=direction):
-                return trade
-
         if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
             self.order_id_counter += 1
             base_currency = self.exchange.get_pair_base_currency(pair)
@@ -834,6 +826,15 @@ class Backtesting:
             # Backcalculate actual stake amount.
             stake_amount = amount * propose_rate / leverage
 
+            if not pos_adjust:
+                # Confirm trade entry:
+                if not strategy_safe_wrapper(
+                        self.strategy.confirm_trade_entry, default_retval=True)(
+                            pair=pair, order_type=order_type, amount=amount, rate=propose_rate,
+                            time_in_force=time_in_force, current_time=current_time,
+                            entry_tag=entry_tag, side=direction):
+                    return trade
+
             is_short = (direction == 'short')
             # Necessary for Margin trading. Disabled until support is enabled.
             # interest_rate = self.exchange.get_interest_rate()

freqtrade/optimize/edge_cli.py

@@ -4,10 +4,10 @@
 This module contains the edge backtesting interface
 """
 import logging
-from typing import Any, Dict
 
 from freqtrade import constants
 from freqtrade.configuration import TimeRange, validate_config_consistency
+from freqtrade.constants import Config
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
 from freqtrade.optimize.optimize_reports import generate_edge_table
@@ -26,7 +26,7 @@ class EdgeCli:
     edge.start()
     """
 
-    def __init__(self, config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config) -> None:
         self.config = config
 
         # Ensure using dry-run

freqtrade/optimize/hyperopt.py

@@ -21,7 +21,7 @@ from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_
 from joblib.externals import cloudpickle
 from pandas import DataFrame
 
-from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
+from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
 from freqtrade.data.converter import trim_dataframes
 from freqtrade.data.history import get_timerange
 from freqtrade.enums import HyperoptState
@@ -66,7 +66,7 @@ class Hyperopt:
     hyperopt.start()
     """
 
-    def __init__(self, config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config) -> None:
         self.buy_space: List[Dimension] = []
         self.sell_space: List[Dimension] = []
         self.protection_space: List[Dimension] = []
@@ -132,7 +132,7 @@ class Hyperopt:
         self.print_json = self.config.get('print_json', False)
 
     @staticmethod
-    def get_lock_filename(config: Dict[str, Any]) -> str:
+    def get_lock_filename(config: Config) -> str:
 
         return str(config['user_data_dir'] / 'hyperopt.lock')
 

freqtrade/optimize/hyperopt_interface.py

@@ -10,6 +10,7 @@ from typing import Dict, List, Union
 from sklearn.base import RegressorMixin
 from skopt.space import Categorical, Dimension, Integer
 
+from freqtrade.constants import Config
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.misc import round_dict
 from freqtrade.optimize.space import SKDecimal
@@ -32,7 +33,7 @@ class IHyperOpt(ABC):
     timeframe: str
     strategy: IStrategy
 
-    def __init__(self, config: dict) -> None:
+    def __init__(self, config: Config) -> None:
         self.config = config
 
         # Assign timeframe to be used in hyperopt

freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py

@@ -10,6 +10,7 @@ from typing import Any, Dict
 
 from pandas import DataFrame
 
+from freqtrade.constants import Config
 from freqtrade.data.metrics import calculate_max_drawdown
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
@@ -27,7 +28,7 @@ class CalmarHyperOptLoss(IHyperOptLoss):
         trade_count: int,
         min_date: datetime,
         max_date: datetime,
-        config: Dict,
+        config: Config,
         processed: Dict[str, DataFrame],
         backtest_stats: Dict[str, Any],
         *args,

freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py

@@ -4,10 +4,9 @@ MaxDrawDownRelativeHyperOptLoss
 This module defines the alternative HyperOptLoss class which can be used for
 Hyperoptimization.
 """
-from typing import Dict
-
 from pandas import DataFrame
 
+from freqtrade.constants import Config
 from freqtrade.data.metrics import calculate_underwater
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
@@ -22,7 +21,7 @@ class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss):
     """
 
     @staticmethod
-    def hyperopt_loss_function(results: DataFrame, config: Dict,
+    def hyperopt_loss_function(results: DataFrame, config: Config,
                                *args, **kwargs) -> float:
 
         """

freqtrade/optimize/hyperopt_loss_interface.py

@@ -9,6 +9,8 @@ from typing import Any, Dict
 
 from pandas import DataFrame
 
+from freqtrade.constants import Config
+
 
 class IHyperOptLoss(ABC):
     """
@@ -21,7 +23,7 @@ class IHyperOptLoss(ABC):
     @abstractmethod
     def hyperopt_loss_function(*, results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               config: Dict, processed: Dict[str, DataFrame],
+                               config: Config, processed: Dict[str, DataFrame],
                                backtest_stats: Dict[str, Any],
                                **kwargs) -> float:
         """

freqtrade/optimize/hyperopt_tools.py

@@ -12,7 +12,7 @@ import tabulate
 from colorama import Fore, Style
 from pandas import isna, json_normalize
 
-from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES
+from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES, Config
 from freqtrade.enums import HyperoptState
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
@@ -45,7 +45,7 @@ class HyperoptStateContainer():
 class HyperoptTools():
 
     @staticmethod
-    def get_strategy_filename(config: Dict, strategy_name: str) -> Optional[Path]:
+    def get_strategy_filename(config: Config, strategy_name: str) -> Optional[Path]:
         """
         Get Strategy-location (filename) from strategy_name
         """
@@ -81,7 +81,7 @@ class HyperoptTools():
                            )
 
     @staticmethod
-    def try_export_params(config: Dict[str, Any], strategy_name: str, params: Dict):
+    def try_export_params(config: Config, strategy_name: str, params: Dict):
         if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get('disableparamexport', False):
             # Export parameters ...
             fn = HyperoptTools.get_strategy_filename(config, strategy_name)
@@ -91,7 +91,7 @@ class HyperoptTools():
                 logger.warning("Strategy not found, not exporting parameter file.")
 
     @staticmethod
-    def has_space(config: Dict[str, Any], space: str) -> bool:
+    def has_space(config: Config, space: str) -> bool:
         """
         Tell if the space value is contained in the configuration
         """
@@ -131,7 +131,7 @@ class HyperoptTools():
             return False
 
     @staticmethod
-    def load_filtered_results(results_file: Path, config: Dict[str, Any]) -> Tuple[List, int]:
+    def load_filtered_results(results_file: Path, config: Config) -> Tuple[List, int]:
         filteroptions = {
             'only_best': config.get('hyperopt_list_best', False),
             'only_profitable': config.get('hyperopt_list_profitable', False),
@@ -346,7 +346,7 @@ class HyperoptTools():
         return trials
 
     @staticmethod
-    def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
+    def get_result_table(config: Config, results: list, total_epochs: int, highlight_best: bool,
                          print_colorized: bool, remove_header: int) -> str:
         """
         Log result table
@@ -444,7 +444,7 @@ class HyperoptTools():
         return table
 
     @staticmethod
-    def export_csv_file(config: dict, results: list, csv_file: str) -> None:
+    def export_csv_file(config: Config, results: list, csv_file: str) -> None:
         """
         Log result to csv-file
         """

freqtrade/optimize/optimize_reports.py

@@ -7,7 +7,8 @@ from typing import Any, Dict, List, Union
 from pandas import DataFrame, to_datetime
 from tabulate import tabulate
 
-from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
+from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
+                                 Config)
 from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
                                     calculate_max_drawdown)
 from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
@@ -898,7 +899,7 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
     print()
 
 
-def show_backtest_results(config: Dict, backtest_stats: Dict):
+def show_backtest_results(config: Config, backtest_stats: Dict):
     stake_currency = config['stake_currency']
 
     for strategy, results in backtest_stats['strategy'].items():
@@ -918,7 +919,7 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
         print('\nFor more details, please look at the detail tables above')
 
 
-def show_sorted_pairlist(config: Dict, backtest_stats: Dict):
+def show_sorted_pairlist(config: Config, backtest_stats: Dict):
     if config.get('backtest_show_pair_list', False):
         for strategy, results in backtest_stats['strategy'].items():
             print(f"Pairs for Strategy {strategy}: \n[")

freqtrade/persistence/trade_model.py

@@ -83,7 +83,7 @@ class Order(_DECL_BASE):
 
     @property
     def safe_price(self) -> float:
-        return self.average or self.price
+        return self.average or self.price or self.stop_price
 
     @property
     def safe_filled(self) -> float:
@@ -376,6 +376,12 @@ class LocalTrade():
     def open_date_utc(self):
         return self.open_date.replace(tzinfo=timezone.utc)
 
+    @property
+    def stoploss_last_update_utc(self):
+        if self.stoploss_last_update:
+            return self.stoploss_last_update.replace(tzinfo=timezone.utc)
+        return None
+
     @property
     def close_date_utc(self):
         return self.close_date.replace(tzinfo=timezone.utc)
@@ -560,7 +566,6 @@ class LocalTrade():
         self.stop_loss = stop_loss_norm
 
         self.stop_loss_pct = -1 * abs(percent)
-        self.stoploss_last_update = datetime.utcnow()
 
     def adjust_stop_loss(self, current_price: float, stoploss: float,
                          initial: bool = False, refresh: bool = False) -> None:

freqtrade/plot/plotting.py

@@ -1,10 +1,11 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Dict, List, Optional
 
 import pandas as pd
 
 from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
 from freqtrade.data.btanalysis import (analyze_trade_parallelism, extract_trades_of_period,
                                        load_trades)
 from freqtrade.data.converter import trim_dataframe
@@ -618,7 +619,7 @@ def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False
     logger.info(f"Stored plot as {_filename}")
 
 
-def load_and_plot_trades(config: Dict[str, Any]):
+def load_and_plot_trades(config: Config):
     """
     From configuration provided
     - Initializes plot-script
@@ -666,7 +667,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
     logger.info('End of plotting process. %s plots generated', pair_counter)
 
 
-def plot_profit(config: Dict[str, Any]) -> None:
+def plot_profit(config: Config) -> None:
     """
     Plots the total profit for all pairs.
     Note, the profit calculation isn't realistic.

freqtrade/plugins/pairlist/AgeFilter.py

@@ -8,7 +8,7 @@ from typing import Any, Dict, List, Optional
 import arrow
 from pandas import DataFrame
 
-from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 class AgeFilter(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/IPairList.py

@@ -6,6 +6,7 @@ from abc import ABC, abstractmethod, abstractproperty
 from copy import deepcopy
 from typing import Any, Dict, List
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange, market_is_active
 from freqtrade.mixins import LoggingMixin
@@ -17,7 +18,7 @@ logger = logging.getLogger(__name__)
 class IPairList(LoggingMixin, ABC):
 
     def __init__(self, exchange: Exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         """
         :param exchange: Exchange instance

freqtrade/plugins/pairlist/OffsetFilter.py

@@ -4,6 +4,7 @@ Offset pair list filter
 import logging
 from typing import Any, Dict, List
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -14,7 +15,7 @@ logger = logging.getLogger(__name__)
 class OffsetFilter(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/PerformanceFilter.py

@@ -6,6 +6,7 @@ from typing import Any, Dict, List
 
 import pandas as pd
 
+from freqtrade.constants import Config
 from freqtrade.persistence import Trade
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -16,7 +17,7 @@ logger = logging.getLogger(__name__)
 class PerformanceFilter(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/PrecisionFilter.py

@@ -4,6 +4,7 @@ Precision pair list filter
 import logging
 from typing import Any, Dict
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -14,7 +15,7 @@ logger = logging.getLogger(__name__)
 class PrecisionFilter(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/PriceFilter.py

@@ -4,6 +4,7 @@ Price pair list filter
 import logging
 from typing import Any, Dict
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -14,7 +15,7 @@ logger = logging.getLogger(__name__)
 class PriceFilter(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/ShuffleFilter.py

@@ -5,6 +5,7 @@ import logging
 import random
 from typing import Any, Dict, List
 
+from freqtrade.constants import Config
 from freqtrade.enums import RunMode
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -15,7 +16,7 @@ logger = logging.getLogger(__name__)
 class ShuffleFilter(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/SpreadFilter.py

@@ -4,6 +4,7 @@ Spread pair list filter
 import logging
 from typing import Any, Dict
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -14,7 +15,7 @@ logger = logging.getLogger(__name__)
 class SpreadFilter(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/StaticPairList.py

@@ -7,6 +7,7 @@ import logging
 from copy import deepcopy
 from typing import Any, Dict, List
 
+from freqtrade.constants import Config
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -16,7 +17,7 @@ logger = logging.getLogger(__name__)
 class StaticPairList(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/VolatilityFilter.py

@@ -11,7 +11,7 @@ import numpy as np
 from cachetools import TTLCache
 from pandas import DataFrame
 
-from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
@@ -26,7 +26,7 @@ class VolatilityFilter(IPairList):
     """
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/VolumePairList.py

@@ -9,7 +9,7 @@ from typing import Any, Dict, List
 
 from cachetools import TTLCache
 
-from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
 from freqtrade.misc import format_ms_time
@@ -25,7 +25,7 @@ SORT_VALUES = ['quoteVolume']
 class VolumePairList(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlist/pairlist_helpers.py

@@ -1,5 +1,7 @@
 import re
-from typing import Any, Dict, List
+from typing import List
+
+from freqtrade.constants import Config
 
 
 def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
@@ -42,7 +44,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
     return result
 
 
-def dynamic_expand_pairlist(config: Dict[str, Any], markets: List[str]) -> List[str]:
+def dynamic_expand_pairlist(config: Config, markets: List[str]) -> List[str]:
     expanded_pairs = expand_pairlist(config['pairs'], markets)
     if config.get('freqai', {}).get('enabled', False):
         corr_pairlist = config['freqai']['feature_parameters']['include_corr_pairlist']

freqtrade/plugins/pairlist/rangestabilityfilter.py

@@ -9,7 +9,7 @@ import arrow
 from cachetools import TTLCache
 from pandas import DataFrame
 
-from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 class RangeStabilityFilter(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 

freqtrade/plugins/pairlistmanager.py

@@ -7,7 +7,7 @@ from typing import Dict, List
 
 from cachetools import TTLCache, cached
 
-from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.enums import CandleType
 from freqtrade.exceptions import OperationalException
 from freqtrade.mixins import LoggingMixin
@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 
 class PairListManager(LoggingMixin):
 
-    def __init__(self, exchange, config: dict) -> None:
+    def __init__(self, exchange, config: Config) -> None:
         self._exchange = exchange
         self._config = config
         self._whitelist = self._config['exchange'].get('pair_whitelist')

freqtrade/plugins/protectionmanager.py

@@ -5,7 +5,7 @@ import logging
 from datetime import datetime, timezone
 from typing import Dict, List, Optional
 
-from freqtrade.constants import LongShort
+from freqtrade.constants import Config, LongShort
 from freqtrade.persistence import PairLocks
 from freqtrade.persistence.models import PairLock
 from freqtrade.plugins.protections import IProtection
@@ -17,7 +17,7 @@ logger = logging.getLogger(__name__)
 
 class ProtectionManager():
 
-    def __init__(self, config: Dict, protections: List) -> None:
+    def __init__(self, config: Config, protections: List) -> None:
         self._config = config
 
         self._protection_handlers: List[IProtection] = []

freqtrade/plugins/protections/iprotection.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass
 from datetime import datetime, timedelta, timezone
 from typing import Any, Dict, List, Optional
 
-from freqtrade.constants import LongShort
+from freqtrade.constants import Config, LongShort
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.misc import plural
 from freqtrade.mixins import LoggingMixin
@@ -30,7 +30,7 @@ class IProtection(LoggingMixin, ABC):
     # Can stop trading for one pair
     has_local_stop: bool = False
 
-    def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config, protection_config: Dict[str, Any]) -> None:
         self._config = config
         self._protection_config = protection_config
         self._stop_duration_candles: Optional[int] = None

freqtrade/plugins/protections/low_profit_pairs.py

@@ -3,7 +3,7 @@ import logging
 from datetime import datetime, timedelta
 from typing import Any, Dict, Optional
 
-from freqtrade.constants import LongShort
+from freqtrade.constants import Config, LongShort
 from freqtrade.persistence import Trade
 from freqtrade.plugins.protections import IProtection, ProtectionReturn
 
@@ -16,7 +16,7 @@ class LowProfitPairs(IProtection):
     has_global_stop: bool = False
     has_local_stop: bool = True
 
-    def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config, protection_config: Dict[str, Any]) -> None:
         super().__init__(config, protection_config)
 
         self._trade_limit = protection_config.get('trade_limit', 1)

freqtrade/plugins/protections/max_drawdown_protection.py

@@ -5,7 +5,7 @@ from typing import Any, Dict, Optional
 
 import pandas as pd
 
-from freqtrade.constants import LongShort
+from freqtrade.constants import Config, LongShort
 from freqtrade.data.metrics import calculate_max_drawdown
 from freqtrade.persistence import Trade
 from freqtrade.plugins.protections import IProtection, ProtectionReturn
@@ -19,7 +19,7 @@ class MaxDrawdown(IProtection):
     has_global_stop: bool = True
     has_local_stop: bool = False
 
-    def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config, protection_config: Dict[str, Any]) -> None:
         super().__init__(config, protection_config)
 
         self._trade_limit = protection_config.get('trade_limit', 1)

freqtrade/plugins/protections/stoploss_guard.py

@@ -3,7 +3,7 @@ import logging
 from datetime import datetime, timedelta
 from typing import Any, Dict, Optional
 
-from freqtrade.constants import LongShort
+from freqtrade.constants import Config, LongShort
 from freqtrade.enums import ExitType
 from freqtrade.persistence import Trade
 from freqtrade.plugins.protections import IProtection, ProtectionReturn
@@ -17,7 +17,7 @@ class StoplossGuard(IProtection):
     has_global_stop: bool = True
     has_local_stop: bool = True
 
-    def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config, protection_config: Dict[str, Any]) -> None:
         super().__init__(config, protection_config)
 
         self._trade_limit = protection_config.get('trade_limit', 10)

freqtrade/resolvers/exchange_resolver.py

@@ -4,6 +4,7 @@ This module loads custom exchanges
 import logging
 
 import freqtrade.exchange as exchanges
+from freqtrade.constants import Config
 from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, Exchange
 from freqtrade.resolvers import IResolver
 
@@ -18,7 +19,7 @@ class ExchangeResolver(IResolver):
     object_type = Exchange
 
     @staticmethod
-    def load_exchange(exchange_name: str, config: dict, validate: bool = True,
+    def load_exchange(exchange_name: str, config: Config, validate: bool = True,
                       load_leverage_tiers: bool = False) -> Exchange:
         """
         Load the custom class from config parameter

freqtrade/resolvers/freqaimodel_resolver.py

@@ -5,9 +5,8 @@ This module load a custom model for freqai
 """
 import logging
 from pathlib import Path
-from typing import Dict
 
-from freqtrade.constants import USERPATH_FREQAIMODELS
+from freqtrade.constants import USERPATH_FREQAIMODELS, Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.freqai_interface import IFreqaiModel
 from freqtrade.resolvers import IResolver
@@ -29,7 +28,7 @@ class FreqaiModelResolver(IResolver):
     )
 
     @staticmethod
-    def load_freqaimodel(config: Dict) -> IFreqaiModel:
+    def load_freqaimodel(config: Config) -> IFreqaiModel:
         """
         Load the custom class from config parameter
         :param config: configuration dictionary

freqtrade/resolvers/hyperopt_resolver.py

@@ -5,9 +5,8 @@ This module load custom hyperopt
 """
 import logging
 from pathlib import Path
-from typing import Dict
 
-from freqtrade.constants import HYPEROPT_LOSS_BUILTIN, USERPATH_HYPEROPTS
+from freqtrade.constants import HYPEROPT_LOSS_BUILTIN, USERPATH_HYPEROPTS, Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
 from freqtrade.resolvers import IResolver
@@ -26,7 +25,7 @@ class HyperOptLossResolver(IResolver):
     initial_search_path = Path(__file__).parent.parent.joinpath('optimize/hyperopt_loss').resolve()
 
     @staticmethod
-    def load_hyperoptloss(config: Dict) -> IHyperOptLoss:
+    def load_hyperoptloss(config: Config) -> IHyperOptLoss:
         """
         Load the custom class from config parameter
         :param config: configuration dictionary

freqtrade/resolvers/iresolver.py

@@ -10,6 +10,7 @@ import sys
 from pathlib import Path
 from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 
 
@@ -43,7 +44,7 @@ class IResolver:
     initial_search_path: Optional[Path]
 
     @classmethod
-    def build_search_paths(cls, config: Dict[str, Any], user_subdir: Optional[str] = None,
+    def build_search_paths(cls, config: Config, user_subdir: Optional[str] = None,
                            extra_dirs: List[str] = []) -> List[Path]:
 
         abs_paths: List[Path] = []
@@ -153,7 +154,7 @@ class IResolver:
         return None
 
     @classmethod
-    def load_object(cls, object_name: str, config: dict, *, kwargs: dict,
+    def load_object(cls, object_name: str, config: Config, *, kwargs: dict,
                     extra_dir: Optional[str] = None) -> Any:
         """
         Search and loads the specified object as configured in hte child class.

freqtrade/resolvers/pairlist_resolver.py

@@ -6,6 +6,7 @@ This module load custom pairlists
 import logging
 from pathlib import Path
 
+from freqtrade.constants import Config
 from freqtrade.plugins.pairlist.IPairList import IPairList
 from freqtrade.resolvers import IResolver
 
@@ -24,7 +25,7 @@ class PairListResolver(IResolver):
 
     @staticmethod
     def load_pairlist(pairlist_name: str, exchange, pairlistmanager,
-                      config: dict, pairlistconfig: dict, pairlist_pos: int) -> IPairList:
+                      config: Config, pairlistconfig: dict, pairlist_pos: int) -> IPairList:
         """
         Load the pairlist with pairlist_name
         :param pairlist_name: Classname of the pairlist

freqtrade/resolvers/protection_resolver.py

@@ -5,6 +5,7 @@ import logging
 from pathlib import Path
 from typing import Dict
 
+from freqtrade.constants import Config
 from freqtrade.plugins.protections import IProtection
 from freqtrade.resolvers import IResolver
 
@@ -22,7 +23,8 @@ class ProtectionResolver(IResolver):
     initial_search_path = Path(__file__).parent.parent.joinpath('plugins/protections').resolve()
 
     @staticmethod
-    def load_protection(protection_name: str, config: Dict, protection_config: Dict) -> IProtection:
+    def load_protection(protection_name: str, config: Config,
+                        protection_config: Dict) -> IProtection:
         """
         Load the protection with protection_name
         :param protection_name: Classname of the pairlist

freqtrade/resolvers/strategy_resolver.py

@@ -9,10 +9,10 @@ from base64 import urlsafe_b64decode
 from inspect import getfullargspec
 from os import walk
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, List, Optional
 
 from freqtrade.configuration.config_validation import validate_migrated_strategy_settings
-from freqtrade.constants import REQUIRED_ORDERTIF, REQUIRED_ORDERTYPES, USERPATH_STRATEGIES
+from freqtrade.constants import REQUIRED_ORDERTIF, REQUIRED_ORDERTYPES, USERPATH_STRATEGIES, Config
 from freqtrade.enums import TradingMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.resolvers import IResolver
@@ -32,7 +32,7 @@ class StrategyResolver(IResolver):
     initial_search_path = None
 
     @staticmethod
-    def load_strategy(config: Dict[str, Any] = None) -> IStrategy:
+    def load_strategy(config: Config = None) -> IStrategy:
         """
         Load the custom class from config parameter
         :param config: configuration dictionary or None
@@ -91,8 +91,7 @@ class StrategyResolver(IResolver):
         return strategy
 
     @staticmethod
-    def _override_attribute_helper(strategy, config: Dict[str, Any],
+    def _override_attribute_helper(strategy, config: Config, attribute: str, default: Any):
-                                   attribute: str, default: Any):
         """
         Override attributes in the strategy.
         Prevalence:
@@ -215,7 +214,7 @@ class StrategyResolver(IResolver):
 
     @staticmethod
     def _load_strategy(strategy_name: str,
-                       config: dict, extra_dir: Optional[str] = None) -> IStrategy:
+                       config: Config, extra_dir: Optional[str] = None) -> IStrategy:
         """
         Search and loads the specified strategy.
         :param strategy_name: name of the module to import

freqtrade/rpc/api_server/webserver.py

@@ -12,6 +12,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from janus import Queue as ThreadedQueue
 from starlette.responses import JSONResponse
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
 from freqtrade.rpc.api_server.ws import ChannelManager
@@ -42,10 +43,10 @@ class ApiServer(RPCHandler):
     _bt = None
     _bt_data = None
     _bt_timerange = None
-    _bt_last_config: Dict[str, Any] = {}
+    _bt_last_config: Config = {}
     _has_rpc: bool = False
     _bgtask_running: bool = False
-    _config: Dict[str, Any] = {}
+    _config: Config = {}
     # Exchange - only available in webserver mode.
     _exchange = None
     # websocket message queue stuff
@@ -63,7 +64,7 @@ class ApiServer(RPCHandler):
             ApiServer.__initialized = False
         return ApiServer.__instance
 
-    def __init__(self, config: Dict[str, Any], standalone: bool = False) -> None:
+    def __init__(self, config: Config, standalone: bool = False) -> None:
         ApiServer._config = config
         if self.__initialized and (standalone or self._standalone):
             return

freqtrade/rpc/discord.py

@@ -1,6 +1,6 @@
 import logging
-from typing import Any, Dict
 
+from freqtrade.constants import Config
 from freqtrade.enums import RPCMessageType
 from freqtrade.rpc import RPC
 from freqtrade.rpc.webhook import Webhook
@@ -10,7 +10,7 @@ logger = logging.getLogger(__name__)
 
 
 class Discord(Webhook):
-    def __init__(self, rpc: 'RPC', config: Dict[str, Any]):
+    def __init__(self, rpc: 'RPC', config: Config):
         # super().__init__(rpc, config)
         self.rpc = rpc
         self.config = config

freqtrade/rpc/rpc.py

@@ -16,7 +16,7 @@ from pandas import DataFrame, NaT
 
 from freqtrade import __version__
 from freqtrade.configuration.timerange import TimeRange
-from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT
+from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT, Config
 from freqtrade.data.history import load_data
 from freqtrade.data.metrics import calculate_max_drawdown
 from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, SignalDirection, State,
@@ -58,7 +58,7 @@ class RPCException(Exception):
 
 class RPCHandler:
 
-    def __init__(self, rpc: 'RPC', config: Dict[str, Any]) -> None:
+    def __init__(self, rpc: 'RPC', config: Config) -> None:
         """
         Initializes RPCHandlers
         :param rpc: instance of RPC Helper class
@@ -66,7 +66,7 @@ class RPCHandler:
         :return: None
         """
         self._rpc = rpc
-        self._config: Dict[str, Any] = config
+        self._config: Config = config
 
     @property
     def name(self) -> str:
@@ -96,7 +96,7 @@ class RPC:
         :return: None
         """
         self._freqtrade = freqtrade
-        self._config: Dict[str, Any] = freqtrade.config
+        self._config: Config = freqtrade.config
         if self._config.get('fiat_display_currency'):
             self._fiat_converter = CryptoToFiatConverter()
 

freqtrade/rpc/rpc_manager.py

@@ -5,6 +5,7 @@ import logging
 from collections import deque
 from typing import Any, Dict, List
 
+from freqtrade.constants import Config
 from freqtrade.enums import RPCMessageType
 from freqtrade.rpc import RPC, RPCHandler
 
@@ -90,7 +91,7 @@ class RPCManager:
                 'msg': msg,
             })
 
-    def startup_messages(self, config: Dict[str, Any], pairlist, protections) -> None:
+    def startup_messages(self, config: Config, pairlist, protections) -> None:
         if config['dry_run']:
             self.send_msg({
                 'type': RPCMessageType.WARNING,

freqtrade/rpc/telegram.py

@@ -24,7 +24,7 @@ from telegram.ext import CallbackContext, CallbackQueryHandler, CommandHandler,
 from telegram.utils.helpers import escape_markdown
 
 from freqtrade.__init__ import __version__
-from freqtrade.constants import DUST_PER_COIN
+from freqtrade.constants import DUST_PER_COIN, Config
 from freqtrade.enums import RPCMessageType, SignalDirection, TradingMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import chunks, plural, round_coin_value
@@ -88,7 +88,7 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
 class Telegram(RPCHandler):
     """  This class handles all telegram communication """
 
-    def __init__(self, rpc: RPC, config: Dict[str, Any]) -> None:
+    def __init__(self, rpc: RPC, config: Config) -> None:
         """
         Init the Telegram call, and init the super class RPCHandler
         :param rpc: instance of RPC Helper class

freqtrade/rpc/webhook.py

@@ -7,6 +7,7 @@ from typing import Any, Dict
 
 from requests import RequestException, post
 
+from freqtrade.constants import Config
 from freqtrade.enums import RPCMessageType
 from freqtrade.rpc import RPC, RPCHandler
 
@@ -19,7 +20,7 @@ logger.debug('Included module rpc.webhook ...')
 class Webhook(RPCHandler):
     """  This class handles all webhook communication """
 
-    def __init__(self, rpc: RPC, config: Dict[str, Any]) -> None:
+    def __init__(self, rpc: RPC, config: Config) -> None:
         """
         Init the Webhook class, and init the super class RPCHandler
         :param rpc: instance of RPC Helper class

freqtrade/strategy/hyper.py

@@ -6,6 +6,7 @@ import logging
 from pathlib import Path
 from typing import Any, Dict, Iterator, List, Tuple, Type, Union
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, json_load
 from freqtrade.optimize.hyperopt_tools import HyperoptTools
@@ -21,7 +22,7 @@ class HyperStrategyMixin:
      strategy logic.
     """
 
-    def __init__(self, config: Dict[str, Any], *args, **kwargs):
+    def __init__(self, config: Config, *args, **kwargs):
         """
         Initialize hyperoptable strategy mixin.
         """

freqtrade/strategy/interface.py

@@ -10,7 +10,7 @@ from typing import Dict, List, Optional, Tuple, Union
 import arrow
 from pandas import DataFrame
 
-from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, SignalDirection,
                              SignalTagType, SignalType, TradingMode)
@@ -119,7 +119,7 @@ class IStrategy(ABC, HyperStrategyMixin):
     # Definition of plot_config. See plotting documentation for more details.
     plot_config: Dict = {}
 
-    def __init__(self, config: dict) -> None:
+    def __init__(self, config: Config) -> None:
         self.config = config
         # Dict to determine if analysis is necessary
         self._last_candle_seen_per_pair: Dict[str, datetime] = {}
@@ -614,6 +614,22 @@ class IStrategy(ABC, HyperStrategyMixin):
 # END - Intended to be overridden by strategy
 ###
 
+    def __informative_pairs_freqai(self) -> ListPairsWithTimeframes:
+        """
+        Create informative-pairs needed for FreqAI
+        """
+        if self.config.get('freqai', {}).get('enabled', False):
+            whitelist_pairs = self.dp.current_whitelist()
+            candle_type = self.config.get('candle_type_def', CandleType.SPOT)
+            corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
+            informative_pairs = []
+            for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
+                for pair in set(whitelist_pairs + corr_pairs):
+                    informative_pairs.append((pair, tf, candle_type))
+            return informative_pairs
+
+        return []
+
     def gather_informative_pairs(self) -> ListPairsWithTimeframes:
         """
         Internal method which gathers all informative pairs (user or automatically defined).
@@ -638,6 +654,7 @@ class IStrategy(ABC, HyperStrategyMixin):
             else:
                 for pair in self.dp.current_whitelist():
                     informative_pairs.append((pair, inf_data.timeframe, candle_type))
+        informative_pairs.extend(self.__informative_pairs_freqai())
         return list(set(informative_pairs))
 
     def get_strategy_name(self) -> str:

freqtrade/templates/FreqaiExampleStrategy.py

@@ -45,20 +45,7 @@ class FreqaiExampleStrategy(IStrategy):
     std_dev_multiplier_buy = CategoricalParameter(
         [0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
     std_dev_multiplier_sell = CategoricalParameter(
-        [0.1, 0.25, 0.4], space="sell", default=0.2, optimize=True)
+        [0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True)
-
-    def informative_pairs(self):
-        whitelist_pairs = self.dp.current_whitelist()
-        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
-        informative_pairs = []
-        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
-            for pair in whitelist_pairs:
-                informative_pairs.append((pair, tf))
-            for pair in corr_pairs:
-                if pair in whitelist_pairs:
-                    continue  # avoid duplication
-                informative_pairs.append((pair, tf))
-        return informative_pairs
 
     def populate_any_indicators(
         self, pair, df, tf, informative=None, set_generalized_indicators=False
@@ -183,25 +170,31 @@ class FreqaiExampleStrategy(IStrategy):
 
         dataframe = self.freqai.start(dataframe, metadata, self)
         for val in self.std_dev_multiplier_buy.range:
-            dataframe[f'target_roi_{val}'] = dataframe["&-s_close_mean"] + \
+            dataframe[f'target_roi_{val}'] = (
-                dataframe["&-s_close_std"] * val
+                dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * val
+                )
         for val in self.std_dev_multiplier_sell.range:
-            dataframe[f'sell_roi_{val}'] = dataframe["&-s_close_mean"] - \
+            dataframe[f'sell_roi_{val}'] = (
-                dataframe["&-s_close_std"] * val
+                dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * val
+                )
         return dataframe
 
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
 
-        enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"]
+        enter_long_conditions = [
-                                 > df[f"target_roi_{self.std_dev_multiplier_buy.value}"]]
+            df["do_predict"] == 1,
+            df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"],
+            ]
 
         if enter_long_conditions:
             df.loc[
                 reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
             ] = (1, "long")
 
-        enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"]
+        enter_short_conditions = [
-                                  < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"]]
+            df["do_predict"] == 1,
+            df["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"],
+            ]
 
         if enter_short_conditions:
             df.loc[
@@ -211,13 +204,17 @@ class FreqaiExampleStrategy(IStrategy):
         return df
 
     def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-        exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] <
+        exit_long_conditions = [
-                                df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25]
+            df["do_predict"] == 1,
+            df["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25,
+            ]
         if exit_long_conditions:
             df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
 
-        exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] >
+        exit_short_conditions = [
-                                 df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25]
+            df["do_predict"] == 1,
+            df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25,
+            ]
         if exit_short_conditions:
             df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
 

freqtrade/templates/FreqaiHybridExampleStrategy.py

@@ -95,20 +95,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
     short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
     exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
 
-    # FreqAI required function, leave as is or add additional informatives to existing structure.
-    def informative_pairs(self):
-        whitelist_pairs = self.dp.current_whitelist()
-        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
-        informative_pairs = []
-        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
-            for pair in whitelist_pairs:
-                informative_pairs.append((pair, tf))
-            for pair in corr_pairs:
-                if pair in whitelist_pairs:
-                    continue  # avoid duplication
-                informative_pairs.append((pair, tf))
-        return informative_pairs
-
     # FreqAI required function, user can add or remove indicators, but general structure
     # must stay the same.
     def populate_any_indicators(

freqtrade/templates/base_strategy.py.j2

@@ -1,21 +1,21 @@
 # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
 # flake8: noqa: F401
-
+# isort: skip_file
 # --- Do not remove these libs ---
-import numpy as np  # noqa
+import numpy as np
-import pandas as pd  # noqa
+import pandas as pd
-from pandas import DataFrame  # noqa
+from pandas import DataFrame
-from datetime import datetime  # noqa
+from datetime import datetime
-from typing import Optional, Union  # noqa
+from typing import Optional, Union
 
 from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
-                                IStrategy, IntParameter)
+                                IntParameter, IStrategy, merge_informative_pair)
 
 # --------------------------------
 # Add your lib to import here
 import talib.abstract as ta
 import pandas_ta as pta
-import freqtrade.vendor.qtpylib.indicators as qtpylib
+from technical import qtpylib
 
 
 class {{ strategy }}(IStrategy):

freqtrade/templates/sample_hyperopt_loss.py

@@ -4,6 +4,7 @@ from typing import Dict
 
 from pandas import DataFrame
 
+from freqtrade.constants import Config
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -36,7 +37,7 @@ class SampleHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               config: Dict, processed: Dict[str, DataFrame],
+                               config: Config, processed: Dict[str, DataFrame],
                                *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for better results

freqtrade/wallets.py

@@ -7,7 +7,7 @@ from typing import Dict, NamedTuple, Optional
 
 import arrow
 
-from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config
 from freqtrade.enums import RunMode, TradingMode
 from freqtrade.exceptions import DependencyException
 from freqtrade.exchange import Exchange
@@ -35,7 +35,7 @@ class PositionWallet(NamedTuple):
 
 class Wallets:
 
-    def __init__(self, config: dict, exchange: Exchange, log: bool = True) -> None:
+    def __init__(self, config: Config, exchange: Exchange, log: bool = True) -> None:
         self._config = config
         self._log = log
         self._exchange = exchange

freqtrade/worker.py

@@ -9,8 +9,9 @@ from typing import Any, Callable, Dict, Optional
 
 import sdnotify
 
-from freqtrade import __version__, constants
+from freqtrade import __version__
 from freqtrade.configuration import Configuration
+from freqtrade.constants import PROCESS_THROTTLE_SECS, RETRY_TIMEOUT, Config
 from freqtrade.enums import State
 from freqtrade.exceptions import OperationalException, TemporaryError
 from freqtrade.freqtradebot import FreqtradeBot
@@ -24,7 +25,7 @@ class Worker:
     Freqtradebot worker class
     """
 
-    def __init__(self, args: Dict[str, Any], config: Dict[str, Any] = None) -> None:
+    def __init__(self, args: Dict[str, Any], config: Config = None) -> None:
         """
         Init all variables and objects the bot needs to work
         """
@@ -53,7 +54,7 @@ class Worker:
 
         internals_config = self._config.get('internals', {})
         self._throttle_secs = internals_config.get('process_throttle_secs',
-                                                   constants.PROCESS_THROTTLE_SECS)
+                                                   PROCESS_THROTTLE_SECS)
         self._heartbeat_interval = internals_config.get('heartbeat_interval', 60)
 
         self._sd_notify = sdnotify.SystemdNotifier() if \
@@ -151,8 +152,8 @@ class Worker:
         try:
             self.freqtrade.process()
         except TemporaryError as error:
-            logger.warning(f"Error: {error}, retrying in {constants.RETRY_TIMEOUT} seconds...")
+            logger.warning(f"Error: {error}, retrying in {RETRY_TIMEOUT} seconds...")
-            time.sleep(constants.RETRY_TIMEOUT)
+            time.sleep(RETRY_TIMEOUT)
         except OperationalException:
             tb = traceback.format_exc()
             hint = 'Issue `/start` if you think it is safe to restart.'

requirements-freqai.txt

@@ -3,7 +3,7 @@
 
 # Required for freqai
 scikit-learn==1.1.2
-joblib==1.1.0
+joblib==1.2.0
 catboost==1.0.6; platform_machine != 'aarch64'
 lightgbm==3.3.2
 xgboost==1.6.2

requirements.txt

@@ -2,7 +2,7 @@ numpy==1.23.3
 pandas==1.4.4
 pandas-ta==0.3.14b
 
-ccxt==1.93.35
+ccxt==1.93.66
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==38.0.1
 aiohttp==3.8.1
@@ -20,7 +20,7 @@ pycoingecko==3.0.0
 jinja2==3.1.2
 tables==3.7.0
 blosc==1.10.6
-joblib==1.1.0
+joblib==1.2.0
 
 # find first, C search in arrays
 py_find_1st==1.1.5
@@ -34,9 +34,9 @@ orjson==3.8.0
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.83.0
+fastapi==0.85.0
 uvicorn==0.18.3
-pyjwt==2.4.0
+pyjwt==2.5.0
 aiofiles==22.1.0
 psutil==5.9.2
 

tests/conftest.py

@@ -2287,7 +2287,7 @@ def tickers():
 
 
 @pytest.fixture
-def result(testdatadir):
+def dataframe_1m(testdatadir):
     with (testdatadir / 'UNITTEST_BTC-1m.json').open('r') as data_file:
         return ohlcv_to_dataframe(json.load(data_file), '1m', pair="UNITTEST/BTC",
                                   fill_missing=True)

tests/data/test_converter.py

@@ -18,8 +18,8 @@ from tests.conftest import log_has, log_has_re
 from tests.data.test_history import _clean_test_file
 
 
-def test_dataframe_correct_columns(result):
+def test_dataframe_correct_columns(dataframe_1m):
-    assert result.columns.tolist() == ['date', 'open', 'high', 'low', 'close', 'volume']
+    assert dataframe_1m.columns.tolist() == ['date', 'open', 'high', 'low', 'close', 'volume']
 
 
 def test_ohlcv_to_dataframe(ohlcv_history_list, caplog):

tests/exchange/test_binance.py

@@ -23,7 +23,7 @@ from tests.exchange.test_exchange import ccxt_exceptionhandlers
 def test_stoploss_order_binance(default_conf, mocker, limitratio, expected, side, trademode):
     api_mock = MagicMock()
     order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
-    order_type = 'stop_loss_limit' if trademode == TradingMode.SPOT else 'stop'
+    order_type = 'stop_loss_limit' if trademode == TradingMode.SPOT else 'limit'
 
     api_mock.create_order = MagicMock(return_value={
         'id': order_id,
@@ -45,12 +45,15 @@ def test_stoploss_order_binance(default_conf, mocker, limitratio, expected, side
             amount=1,
             stop_price=190,
             side=side,
-            order_types={'stoploss_on_exchange_limit_ratio': 1.05},
+            order_types={'stoploss': 'limit', 'stoploss_on_exchange_limit_ratio': 1.05},
             leverage=1.0
         )
 
     api_mock.create_order.reset_mock()
-    order_types = {} if limitratio is None else {'stoploss_on_exchange_limit_ratio': limitratio}
+    order_types = {'stoploss': 'limit'}
+    if limitratio is not None:
+        order_types.update({'stoploss_on_exchange_limit_ratio': limitratio})
+
     order = exchange.stoploss(
         pair='ETH/BTC',
         amount=1,

tests/exchange/test_okx.py

@@ -472,7 +472,7 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog,
 
     api_mock.fetch_market_leverage_tiers.call_count == 0
     # 2 day passes ...
-    time_machine.move_to(datetime.now() + timedelta(days=2))
+    time_machine.move_to(datetime.now() + timedelta(weeks=5))
     exchange.load_leverage_tiers()
 
     assert log_has(logmsg, caplog)

tests/freqai/test_freqai_interface.py

@@ -8,6 +8,7 @@ import pytest
 from freqtrade.configuration import TimeRange
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.plugins.pairlistmanager import PairListManager
 from tests.conftest import get_patched_exchange, log_has_re
 from tests.freqai.conftest import get_patched_freqai_strategy
 
@@ -315,3 +316,62 @@ def test_principal_component_analysis(mocker, freqai_conf):
     assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl")
 
     shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_plot_feature_importance(mocker, freqai_conf):
+
+    from freqtrade.freqai.utils import plot_feature_importance
+
+    freqai_conf.update({"timerange": "20180110-20180130"})
+    freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
+        {"princpial_component_analysis": "true"})
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    freqai.dd.pair_dict = MagicMock()
+
+    data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
+    new_timerange = TimeRange.parse_timerange("20180120-20180130")
+
+    freqai.extract_data_and_train_model(
+        new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
+
+    model = freqai.dd.load_data("ADA/BTC", freqai.dk)
+
+    plot_feature_importance(model, "ADA/BTC", freqai.dk)
+
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}.html")
+
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+@pytest.mark.parametrize('timeframes,corr_pairs', [
+    (['5m'], ['ADA/BTC', 'DASH/BTC']),
+    (['5m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),
+    (['5m', '15m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),
+])
+def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs):
+    freqai_conf['freqai']['feature_parameters'].update({
+        'include_timeframes': timeframes,
+        'include_corr_pairlist': corr_pairs,
+
+    })
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    pairlists = PairListManager(exchange, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange, pairlists)
+    pairlist = strategy.dp.current_whitelist()
+
+    pairs_a = strategy.informative_pairs()
+    assert len(pairs_a) == 0
+    pairs_b = strategy.gather_informative_pairs()
+    # we expect unique pairs * timeframes
+    assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes)

tests/strategy/strats/freqai_test_multimodel_strat.py

@@ -43,19 +43,6 @@ class freqai_test_multimodel_strat(IStrategy):
     )
     max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
 
-    def informative_pairs(self):
-        whitelist_pairs = self.dp.current_whitelist()
-        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
-        informative_pairs = []
-        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
-            for pair in whitelist_pairs:
-                informative_pairs.append((pair, tf))
-            for pair in corr_pairs:
-                if pair in whitelist_pairs:
-                    continue  # avoid duplication
-                informative_pairs.append((pair, tf))
-        return informative_pairs
-
     def populate_any_indicators(
         self, pair, df, tf, informative=None, set_generalized_indicators=False
     ):

tests/strategy/strats/freqai_test_strat.py

@@ -43,19 +43,6 @@ class freqai_test_strat(IStrategy):
     )
     max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
 
-    def informative_pairs(self):
-        whitelist_pairs = self.dp.current_whitelist()
-        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
-        informative_pairs = []
-        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
-            for pair in whitelist_pairs:
-                informative_pairs.append((pair, tf))
-            for pair in corr_pairs:
-                if pair in whitelist_pairs:
-                    continue  # avoid duplication
-                informative_pairs.append((pair, tf))
-        return informative_pairs
-
     def populate_any_indicators(
         self, pair, df, tf, informative=None, set_generalized_indicators=False
     ):

tests/strategy/test_default_strategy.py

@@ -21,14 +21,14 @@ def test_strategy_test_v3_structure():
     (True, 'short'),
     (False, 'long'),
 ])
-def test_strategy_test_v3(result, fee, is_short, side):
+def test_strategy_test_v3(dataframe_1m, fee, is_short, side):
     strategy = StrategyTestV3({})
 
     metadata = {'pair': 'ETH/BTC'}
     assert type(strategy.minimal_roi) is dict
     assert type(strategy.stoploss) is float
     assert type(strategy.timeframe) is str
-    indicators = strategy.populate_indicators(result, metadata)
+    indicators = strategy.populate_indicators(dataframe_1m, metadata)
     assert type(indicators) is DataFrame
     assert type(strategy.populate_buy_trend(indicators, metadata)) is DataFrame
     assert type(strategy.populate_sell_trend(indicators, metadata)) is DataFrame