Merge pull request #7313 from freqtrade/new_release

New release 2022.8

.pre-commit-config.yaml

@@ -15,7 +15,7 @@ repos:
         additional_dependencies:
           - types-cachetools==5.2.1
           - types-filelock==3.2.7
-          - types-requests==2.28.8
+          - types-requests==2.28.9
           - types-tabulate==0.8.11
           - types-python-dateutil==2.8.19
         # stages: [push]

Dockerfile

@@ -11,7 +11,7 @@ ENV FT_APP_ENV="docker"
 # Prepare environment
 RUN mkdir /freqtrade \
   && apt-get update \
-  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev  \
+  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev libgomp1 \
   && apt-get clean \
   && useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \
   && chown ftuser:ftuser /freqtrade \

README.md

@@ -130,7 +130,7 @@ Telegram is not mandatory. However, this is a great way to control your bot. Mor
 
 - `/start`: Starts the trader.
 - `/stop`: Stops the trader.
-- `/stopbuy`: Stop entering new trades.
+- `/stopentry`: Stop entering new trades.
 - `/status <trade_id>|[table]`: Lists all or specific open trades.
 - `/profit [<n>]`: Lists cumulative profit from all finished trades, over the last n days.
 - `/forceexit <trade_id>|all`: Instantly exits the given trade (Ignoring `minimum_roi`).

docs/backtesting.md

@@ -561,6 +561,14 @@ BTC trades at 22.000\$ today (0.001 BTC is related to this) - but the backtestin
 Today's minimum would be `0.001 * 22_000` - or 22\$.  
 However the limit could also be 50$ - based on `0.001 * 50_000` in some historic setting.
 
+#### Trading precision limits
+
+Most exchanges pose precision limits on both price and amounts, so you cannot buy 1.0020401 of a pair, or at a price of 1.24567123123.  
+Instead, these prices and amounts will be rounded or truncated (based on the exchange definition) to the defined trading precision.
+The above values may for example be rounded to an amount of 1.002, and a price of 1.24567.
+
+These precision values are based on current exchange limits (as described in the [above section](#trading-limits-in-backtesting)), as historic precision limits are not available.
+
 ## Improved backtest accuracy
 
 One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).

docs/bot-basics.md

@@ -70,7 +70,7 @@ This loop will be repeated again and again until the bot is stopped.
   * Determine stake size by calling the `custom_stake_amount()` callback.
   * Check position adjustments for open trades if enabled and call `adjust_trade_position()` to determine if an additional order is requested.
   * Call `custom_stoploss()` and `custom_exit()` to find custom exit points.
-  * For exits based on exit-signal and custom-exit: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
+  * For exits based on exit-signal, custom-exit and partial exits: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
 * Generate backtest report output
 
 !!! Note

docs/data-download.md

@@ -63,7 +63,7 @@ optional arguments:
                         `jsongz`).
   --trading-mode {spot,margin,futures}
                         Select Trading mode
-  --prepend             Allow data prepending.
+  --prepend             Allow data prepending. (Data-appending is disabled)
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -186,7 +186,7 @@ Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
 By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
 
 This can be changed via the `--data-format-ohlcv` and `--data-format-trades` command line arguments respectively.
-To persist this change, you can should also add the following snippet to your configuration, so you don't have to insert the above arguments each time:
+To persist this change, you should also add the following snippet to your configuration, so you don't have to insert the above arguments each time:
 
 ``` jsonc
     // ...
@@ -374,6 +374,7 @@ usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                            [--data-format-ohlcv {json,jsongz,hdf5}]
                            [-p PAIRS [PAIRS ...]]
                            [--trading-mode {spot,margin,futures}]
+                           [--show-timerange]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -387,6 +388,8 @@ optional arguments:
                         separated.
   --trading-mode {spot,margin,futures}
                         Select Trading mode
+  --show-timerange      Show timerange available for available data. (May take
+                        a while to calculate).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).

docs/faq.md

@@ -77,9 +77,9 @@ Freqtrade will not provide incomplete candles to strategies. Using incomplete ca
 
 You can use "current" market data by using the [dataprovider](strategy-customization.md#orderbookpair-maximum)'s orderbook or ticker methods - which however cannot be used during backtesting.
 
-### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
+### Is there a setting to only Exit the trades being held and not perform any new Entries?
 
-You can use the `/stopbuy` command in Telegram to prevent future buys, followed by `/forceexit all` (sell all open trades).
+You can use the `/stopentry` command in Telegram to prevent future trade entry, followed by `/forceexit all` (sell all open trades).
 
 ### I want to run multiple bots on the same machine
 

docs/hyperopt.md

@@ -40,7 +40,8 @@ pip install -r requirements-hyperopt.txt
 ```
 usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                           [--userdir PATH] [-s NAME] [--strategy-path PATH]
-                          [--recursive-strategy-search] [-i TIMEFRAME]
+                          [--recursive-strategy-search] [--freqaimodel NAME]
+                          [--freqaimodel-path PATH] [-i TIMEFRAME]
                           [--timerange TIMERANGE]
                           [--data-format-ohlcv {json,jsongz,hdf5}]
                           [--max-open-trades INT]
@@ -53,7 +54,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                           [--print-all] [--no-color] [--print-json] [-j JOBS]
                           [--random-state INT] [--min-trades INT]
                           [--hyperopt-loss NAME] [--disable-param-export]
-                          [--ignore-missing-spaces]
+                          [--ignore-missing-spaces] [--analyze-per-epoch]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -129,6 +130,7 @@ optional arguments:
   --ignore-missing-spaces, --ignore-unparameterized-spaces
                         Suppress errors for any requested Hyperopt spaces that
                         do not contain any parameters.
+  --analyze-per-epoch   Run populate_indicators once per epoch.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -154,6 +156,10 @@ Strategy arguments:
   --recursive-strategy-search
                         Recursively search for a strategy in the strategies
                         folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+
 ```
 
 ### Hyperopt checklist
@@ -185,7 +191,7 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid
 
 ### Hyperopt execution logic
 
-Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators.
+Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators, unless `--analyze-per-epoch` is specified.
 
 Hyperopt will then spawn into different processes (number of processors, or `-j <n>`), and run backtesting over and over again, changing the parameters that are part of the `--spaces` defined.
 
@@ -426,9 +432,10 @@ While this strategy is most likely too simple to provide consistent profit, it s
     `range` property may also be used with `DecimalParameter` and `CategoricalParameter`. `RealParameter` does not provide this property due to infinite search space.
 
 ??? Hint "Performance tip"
-    By doing the calculation of all possible indicators in `populate_indicators()`, the calculation of the indicator happens only once for every parameter.  
-    While this may slow down the hyperopt startup speed, the overall performance will increase as the Hyperopt execution itself may pick the same value for multiple epochs (changing other values).
-    You should however try to use space ranges as small as possible. Every new column will require more memory, and every possibility hyperopt can try will increase the search space.
+    During normal hyperopting, indicators are calculated once and supplied to each epoch, linearly increasing RAM usage as a factor of increasing cores. As this also has performance implications, hyperopt provides `--analyze-per-epoch` which will move the execution of `populate_indicators()` to the epoch process, calculating a single value per parameter per epoch instead of using the `.range` functionality. In this case, `.range` functionality will only return the actually used value. This will reduce RAM usage, but increase CPU usage. However, your hyperopting run will be less likely to fail due to Out Of Memory (OOM) issues.
+
+    In either case, you should try to use space ranges as small as possible this will improve CPU/RAM usage in both scenarios.
+
 
 ## Optimizing protections
 
@@ -879,6 +886,7 @@ To combat these, you have multiple options:
 * Avoid using `--timeframe-detail` (this loads a lot of additional data into memory).
 * Reduce the number of parallel processes (`-j <n>`).
 * Increase the memory of your machine.
+* Use `--analyze-per-epoch` if you're using a lot of parameters with `.range` functionality.
 
 
 ## The objective has been evaluated at this point before.

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
 mkdocs==1.3.1
-mkdocs-material==8.4.0
+mkdocs-material==8.4.1
 mdx_truly_sane_lists==1.3
 pymdown-extensions==9.5
 jinja2==3.1.2

docs/rest-api.md

@@ -163,6 +163,8 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `strategy <strategy>` | Get specific Strategy content. **Alpha**
 | `available_pairs` | List available backtest data. **Alpha**
 | `version` | Show version.
+| `sysinfo` | Show informations about the system load.
+| `health` | Show bot health (last bot loop).
 
 !!! Warning "Alpha status"
     Endpoints labeled with *Alpha status* above may change at any time without notice.
@@ -227,6 +229,11 @@ forceexit
 	Force-exit a trade.
 
         :param tradeid: Id of the trade (can be received via status command)
+        :param ordertype: Order type to use (must be market or limit)
+        :param amount: Amount to sell. Full sell if not given
+
+health
+	Provides a quick health check of the running bot.
 
 locks
 	Return current locks
@@ -312,12 +319,13 @@ version
 
 whitelist
 	Show the current whitelist.
+
 ```
 
 ### OpenAPI interface
 
 To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.
-This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs/ - but it'll depend on your settings.
+This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs - but it'll depend on your settings.
 
 ### Advanced API usage using JWT tokens
 

docs/strategy-callbacks.md

@@ -75,7 +75,7 @@ class AwesomeStrategy(IStrategy):
 
 ```
 
-### Stake size management
+## Stake size management
 
 Called before entering a trade, makes it possible to manage your position size when placing a new trade.
 
@@ -423,7 +423,7 @@ class AwesomeStrategy(IStrategy):
 !!! Warning "Backtesting"
     Custom prices are supported in backtesting (starting with 2021.12), and orders will fill if the price falls within the candle's low/high range.
     Orders that don't fill immediately are subject to regular timeout handling, which happens once per (detail) candle.
-    `custom_exit_price()` is only called for sells of type exit_signal and Custom exit. All other exit-types will use regular backtesting prices.
+    `custom_exit_price()` is only called for sells of type exit_signal, Custom exit and partial exits. All other exit-types will use regular backtesting prices.
 
 ## Custom order timeout rules
 
@@ -654,7 +654,7 @@ Position adjustments will always be applied in the direction of the trade, so a
     Stoploss is still calculated from the initial opening price, not averaged price.
     Regular stoploss rules still apply (cannot move down).
 
-    While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
+    While `/stopentry` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
 
 !!! Warning "Backtesting"
     During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.

docs/telegram-usage.md

@@ -149,7 +149,7 @@ You can create your own keyboard in `config.json`:
 !!! Note "Supported Commands"
     Only the following commands are allowed. Command arguments are not supported!
 
-    `/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopbuy`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version`
+    `/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopentry`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version`
 
 ## Telegram commands
 
@@ -161,7 +161,7 @@ official commands. You can ask at any moment for help with `/help`.
 |----------|-------------|
 | `/start` | Starts the trader
 | `/stop` | Stops the trader
-| `/stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
+| `/stopbuy | /stopentry` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
 | `/reload_config` | Reloads the configuration file
 | `/show_config` | Shows part of the current configuration with relevant settings to operation
 | `/logs [limit]` | Show last log messages.

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2022.8.dev'
+__version__ = '2022.8'
 
 if 'dev' in __version__:
     try:

freqtrade/commands/arguments.py

@@ -34,7 +34,7 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
                                         "print_colorized", "print_json", "hyperopt_jobs",
                                         "hyperopt_random_state", "hyperopt_min_trades",
                                         "hyperopt_loss", "disableparamexport",
-                                        "hyperopt_ignore_missing_space"]
+                                        "hyperopt_ignore_missing_space", "analyze_per_epoch"]
 
 ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
 
@@ -69,7 +69,7 @@ ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes", "exchange", "tradin
 
 ARGS_CONVERT_TRADES = ["pairs", "timeframes", "exchange", "dataformat_ohlcv", "dataformat_trades"]
 
-ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs", "trading_mode"]
+ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs", "trading_mode", "show_timerange"]
 
 ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "include_inactive",
                       "timerange", "download_trades", "exchange", "timeframes",

freqtrade/commands/cli_options.py

@@ -255,6 +255,13 @@ AVAILABLE_CLI_OPTIONS = {
         nargs='+',
         default='default',
     ),
+    "analyze_per_epoch": Arg(
+        '--analyze-per-epoch',
+        help='Run populate_indicators once per epoch.',
+        action='store_true',
+        default=False,
+    ),
+
     "print_all": Arg(
         '--print-all',
         help='Print all results, not only the best ones.',
@@ -367,7 +374,7 @@ AVAILABLE_CLI_OPTIONS = {
         metavar='BASE_CURRENCY',
     ),
     "trading_mode": Arg(
-        '--trading-mode',
+        '--trading-mode', '--tradingmode',
         help='Select Trading mode',
         choices=constants.TRADING_MODES,
     ),
@@ -434,6 +441,11 @@ AVAILABLE_CLI_OPTIONS = {
         help='Storage format for downloaded trades data. (default: `jsongz`).',
         choices=constants.AVAILABLE_DATAHANDLERS,
     ),
+    "show_timerange": Arg(
+        '--show-timerange',
+        help='Show timerange available for available data. (May take a while to calculate).',
+        action='store_true',
+    ),
     "exchange": Arg(
         '--exchange',
         help=f'Exchange name (default: `{constants.DEFAULT_EXCHANGE}`). '
@@ -450,7 +462,7 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "prepend_data": Arg(
         '--prepend',
-        help='Allow data prepending.',
+        help='Allow data prepending. (Data-appending is disabled)',
         action='store_true',
     ),
     "erase": Arg(

freqtrade/commands/data_commands.py

@@ -5,13 +5,13 @@ from datetime import datetime, timedelta
 from typing import Any, Dict, List
 
 from freqtrade.configuration import TimeRange, setup_utils_configuration
+from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
 from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data,
                                     refresh_backtest_trades_data)
 from freqtrade.enums import CandleType, RunMode, TradingMode
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange import timeframe_to_minutes
-from freqtrade.exchange.exchange import market_is_active
+from freqtrade.exchange import market_is_active, timeframe_to_minutes
 from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
 from freqtrade.resolvers import ExchangeResolver
 
@@ -80,7 +80,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
                 data_format_trades=config['dataformat_trades'],
             )
         else:
-            if not exchange._ft_has.get('ohlcv_has_history', True):
+            if not exchange.get_option('ohlcv_has_history', True):
                 raise OperationalException(
                     f"Historic klines not available for {exchange.name}. "
                     "Please use `--dl-trades` instead for this exchange "
@@ -177,17 +177,31 @@ def start_list_data(args: Dict[str, Any]) -> None:
         paircombs = [comb for comb in paircombs if comb[0] in args['pairs']]
 
     print(f"Found {len(paircombs)} pair / timeframe combinations.")
-    groupedpair = defaultdict(list)
-    for pair, timeframe, candle_type in sorted(
-        paircombs,
-        key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
-    ):
-        groupedpair[(pair, candle_type)].append(timeframe)
+    if not config.get('show_timerange'):
+        groupedpair = defaultdict(list)
+        for pair, timeframe, candle_type in sorted(
+            paircombs,
+            key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
+        ):
+            groupedpair[(pair, candle_type)].append(timeframe)
 
-    if groupedpair:
+        if groupedpair:
+            print(tabulate([
+                (pair, ', '.join(timeframes), candle_type)
+                for (pair, candle_type), timeframes in groupedpair.items()
+            ],
+                headers=("Pair", "Timeframe", "Type"),
+                tablefmt='psql', stralign='right'))
+    else:
+        paircombs1 = [(
+            pair, timeframe, candle_type,
+            *dhc.ohlcv_data_min_max(pair, timeframe, candle_type)
+        ) for pair, timeframe, candle_type in paircombs]
         print(tabulate([
-            (pair, ', '.join(timeframes), candle_type)
-            for (pair, candle_type), timeframes in groupedpair.items()
-        ],
-            headers=("Pair", "Timeframe", "Type"),
+            (pair, timeframe, candle_type,
+                start.strftime(DATETIME_PRINT_FORMAT),
+                end.strftime(DATETIME_PRINT_FORMAT))
+            for pair, timeframe, candle_type, start, end in paircombs1
+            ],
+            headers=("Pair", "Timeframe", "Type", 'From', 'To'),
             tablefmt='psql', stralign='right'))

freqtrade/configuration/configuration.py

@@ -302,6 +302,9 @@ class Configuration:
         self._args_to_config(config, argname='spaces',
                              logstring='Parameter -s/--spaces detected: {}')
 
+        self._args_to_config(config, argname='analyze_per_epoch',
+                             logstring='Parameter --analyze-per-epoch detected.')
+
         self._args_to_config(config, argname='print_all',
                              logstring='Parameter --print-all detected ...')
 
@@ -426,6 +429,9 @@ class Configuration:
         self._args_to_config(config, argname='dataformat_trades',
                              logstring='Using "{}" to store trades data.')
 
+        self._args_to_config(config, argname='show_timerange',
+                             logstring='Detected --show-timerange')
+
     def _process_data_options(self, config: Dict[str, Any]) -> None:
         self._args_to_config(config, argname='new_pairs_days',
                              logstring='Detected --new-pairs-days: {}')

freqtrade/data/history/history_utils.py

@@ -302,8 +302,8 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
         if trading_mode == 'futures':
             # Predefined candletype (and timeframe) depending on exchange
             # Downloads what is necessary to backtest based on futures data.
-            tf_mark = exchange._ft_has['mark_ohlcv_timeframe']
-            fr_candle_type = CandleType.from_string(exchange._ft_has['mark_ohlcv_price'])
+            tf_mark = exchange.get_option('mark_ohlcv_timeframe')
+            fr_candle_type = CandleType.from_string(exchange.get_option('mark_ohlcv_price'))
             # All exchanges need FundingRate for futures trading.
             # The timeframe is aligned to the mark-price timeframe.
             for funding_candle_type in (CandleType.FUNDING_RATE, fr_candle_type):
@@ -330,13 +330,12 @@ def _download_trades_history(exchange: Exchange,
     try:
 
         until = None
+        since = 0
         if timerange:
             if timerange.starttype == 'date':
                 since = timerange.startts * 1000
             if timerange.stoptype == 'date':
                 until = timerange.stopts * 1000
-        else:
-            since = arrow.utcnow().shift(days=-new_pairs_days).int_timestamp * 1000
 
         trades = data_handler.trades_load(pair)
 
@@ -349,6 +348,9 @@ def _download_trades_history(exchange: Exchange,
             logger.info(f"Start earlier than available data. Redownloading trades for {pair}...")
             trades = []
 
+        if not since:
+            since = arrow.utcnow().shift(days=-new_pairs_days).int_timestamp * 1000
+
         from_id = trades[-1][1] if trades else None
         if trades and since < trades[-1][0]:
             # Reset since to the last available point

freqtrade/data/history/idatahandler.py

@@ -9,7 +9,7 @@ from abc import ABC, abstractmethod
 from copy import deepcopy
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import List, Optional, Type
+from typing import List, Optional, Tuple, Type
 
 from pandas import DataFrame
 
@@ -84,6 +84,18 @@ class IDataHandler(ABC):
         :return: None
         """
 
+    def ohlcv_data_min_max(self, pair: str, timeframe: str,
+                           candle_type: CandleType) -> Tuple[datetime, datetime]:
+        """
+        Returns the min and max timestamp for the given pair and timeframe.
+        :param pair: Pair to get min/max for
+        :param timeframe: Timeframe to get min/max for
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: (min, max)
+        """
+        data = self._ohlcv_load(pair, timeframe, None, candle_type)
+        return data.iloc[0]['date'].to_pydatetime(), data.iloc[-1]['date'].to_pydatetime()
+
     @abstractmethod
     def _ohlcv_load(self, pair: str, timeframe: str, timerange: Optional[TimeRange],
                     candle_type: CandleType

freqtrade/edge/edge_positioning.py

@@ -15,7 +15,7 @@ from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
 from freqtrade.data.history import get_timerange, load_data, refresh_data
 from freqtrade.enums import CandleType, ExitType, RunMode
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange.exchange import timeframe_to_seconds
+from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.strategy.interface import IStrategy
 

freqtrade/enums/__init__.py

@@ -3,6 +3,7 @@ from freqtrade.enums.backteststate import BacktestState
 from freqtrade.enums.candletype import CandleType
 from freqtrade.enums.exitchecktuple import ExitCheckTuple
 from freqtrade.enums.exittype import ExitType
+from freqtrade.enums.hyperoptstate import HyperoptState
 from freqtrade.enums.marginmode import MarginMode
 from freqtrade.enums.ordertypevalue import OrderTypeValues
 from freqtrade.enums.rpcmessagetype import RPCMessageType

freqtrade/enums/hyperoptstate.py

@@ -0,0 +1,12 @@
+from enum import Enum
+
+
+class HyperoptState(Enum):
+    """ Hyperopt states """
+    STARTUP = 1
+    DATALOAD = 2
+    INDICATORS = 3
+    OPTIMIZE = 4
+
+    def __str__(self):
+        return f"{self.name.lower()}"

freqtrade/exchange/__init__.py

@@ -9,10 +9,11 @@ from freqtrade.exchange.bitpanda import Bitpanda
 from freqtrade.exchange.bittrex import Bittrex
 from freqtrade.exchange.bybit import Bybit
 from freqtrade.exchange.coinbasepro import Coinbasepro
-from freqtrade.exchange.exchange import (amount_to_precision, available_exchanges, ccxt_exchanges,
-                                         date_minus_candles, is_exchange_known_ccxt,
-                                         is_exchange_officially_supported, market_is_active,
-                                         price_to_precision, timeframe_to_minutes,
+from freqtrade.exchange.exchange import (amount_to_contract_precision, amount_to_contracts,
+                                         amount_to_precision, available_exchanges, ccxt_exchanges,
+                                         contracts_to_amount, date_minus_candles,
+                                         is_exchange_known_ccxt, is_exchange_officially_supported,
+                                         market_is_active, price_to_precision, timeframe_to_minutes,
                                          timeframe_to_msecs, timeframe_to_next_date,
                                          timeframe_to_prev_date, timeframe_to_seconds,
                                          validate_exchange, validate_exchanges)

freqtrade/exchange/binance.py

@@ -137,23 +137,27 @@ class Binance(Exchange):
         pair: str,
         open_rate: float,   # Entry price of position
         is_short: bool,
-        position: float,  # Absolute value of position size
+        amount: float,
+        stake_amount: float,
         wallet_balance: float,  # Or margin balance
         mm_ex_1: float = 0.0,  # (Binance) Cross only
         upnl_ex_1: float = 0.0,  # (Binance) Cross only
     ) -> Optional[float]:
         """
+        Important: Must be fetching data from cached values as this is used by backtesting!
         MARGIN: https://www.binance.com/en/support/faq/f6b010588e55413aa58b7d63ee0125ed
         PERPETUAL: https://www.binance.com/en/support/faq/b3c689c1f50a44cabb3a84e663b81d93
 
         :param exchange_name:
-        :param open_rate: (EP1) Entry price of position
+        :param open_rate: Entry price of position
         :param is_short: True if the trade is a short, false otherwise
-        :param position: Absolute value of position size (in base currency)
-        :param wallet_balance: (WB)
+        :param amount: Absolute value of position size incl. leverage (in base currency)
+        :param stake_amount: Stake amount - Collateral in settle currency.
+        :param trading_mode: SPOT, MARGIN, FUTURES, etc.
+        :param margin_mode: Either ISOLATED or CROSS
+        :param wallet_balance: Amount of margin_mode in the wallet being used to trade
             Cross-Margin Mode: crossWalletBalance
             Isolated-Margin Mode: isolatedWalletBalance
-        :param maintenance_amt:
 
         # * Only required for Cross
         :param mm_ex_1: (TMM)
@@ -165,12 +169,11 @@ class Binance(Exchange):
         """
 
         side_1 = -1 if is_short else 1
-        position = abs(position)
         cross_vars = upnl_ex_1 - mm_ex_1 if self.margin_mode == MarginMode.CROSS else 0.0
 
         # mm_ratio: Binance's formula specifies maintenance margin rate which is mm_ratio * 100%
         # maintenance_amt: (CUM) Maintenance Amount of position
-        mm_ratio, maintenance_amt = self.get_maintenance_ratio_and_amt(pair, position)
+        mm_ratio, maintenance_amt = self.get_maintenance_ratio_and_amt(pair, stake_amount)
 
         if (maintenance_amt is None):
             raise OperationalException(
@@ -182,9 +185,9 @@ class Binance(Exchange):
             return (
                 (
                     (wallet_balance + cross_vars + maintenance_amt) -
-                    (side_1 * position * open_rate)
+                    (side_1 * amount * open_rate)
                 ) / (
-                    (position * mm_ratio) - (side_1 * position)
+                    (amount * mm_ratio) - (side_1 * amount)
                 )
             )
         else:

freqtrade/exchange/exchange.py

@@ -17,6 +17,7 @@ import ccxt
 import ccxt.async_support as ccxt_async
 from cachetools import TTLCache
 from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
+from dateutil import parser
 from pandas import DataFrame
 
 from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
@@ -30,7 +31,8 @@ from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGE
                                        EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
                                        SUPPORTED_EXCHANGES, remove_credentials, retrier,
                                        retrier_async)
-from freqtrade.misc import chunks, deep_merge_dicts, safe_value_fallback2
+from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
+                            safe_value_fallback2)
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.util import FtPrecise
 
@@ -52,8 +54,8 @@ class Exchange:
     # Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
     _params: Dict = {}
 
-    # Additional headers - added to the ccxt object
-    _headers: Dict = {}
+    # Additional parameters - added to the ccxt object
+    _ccxt_params: Dict = {}
 
     # Dict to specify which options each exchange implements
     # This defines defaults, which can be selectively overridden by subclasses using _ft_has
@@ -240,9 +242,9 @@ class Exchange:
         }
         if ccxt_kwargs:
             logger.info('Applying additional ccxt config: %s', ccxt_kwargs)
-        if self._headers:
-            # Inject static headers after the above output to not confuse users.
-            ccxt_kwargs = deep_merge_dicts({'headers': self._headers}, ccxt_kwargs)
+        if self._ccxt_params:
+            # Inject static options after the above output to not confuse users.
+            ccxt_kwargs = deep_merge_dicts(self._ccxt_params, ccxt_kwargs)
         if ccxt_kwargs:
             ex_config.update(ccxt_kwargs)
         try:
@@ -406,7 +408,7 @@ class Exchange:
         else:
             return DataFrame()
 
-    def _get_contract_size(self, pair: str) -> float:
+    def get_contract_size(self, pair: str) -> float:
         if self.trading_mode == TradingMode.FUTURES:
             market = self.markets[pair]
             contract_size: float = 1.0
@@ -419,7 +421,7 @@ class Exchange:
 
     def _trades_contracts_to_amount(self, trades: List) -> List:
         if len(trades) > 0 and 'symbol' in trades[0]:
-            contract_size = self._get_contract_size(trades[0]['symbol'])
+            contract_size = self.get_contract_size(trades[0]['symbol'])
             if contract_size != 1:
                 for trade in trades:
                     trade['amount'] = trade['amount'] * contract_size
@@ -427,7 +429,7 @@ class Exchange:
 
     def _order_contracts_to_amount(self, order: Dict) -> Dict:
         if 'symbol' in order and order['symbol'] is not None:
-            contract_size = self._get_contract_size(order['symbol'])
+            contract_size = self.get_contract_size(order['symbol'])
             if contract_size != 1:
                 for prop in self._ft_has.get('order_props_in_contracts', []):
                     if prop in order and order[prop] is not None:
@@ -436,19 +438,13 @@ class Exchange:
 
     def _amount_to_contracts(self, pair: str, amount: float) -> float:
 
-        contract_size = self._get_contract_size(pair)
-        if contract_size and contract_size != 1:
-            return amount / contract_size
-        else:
-            return amount
+        contract_size = self.get_contract_size(pair)
+        return amount_to_contracts(amount, contract_size)
 
     def _contracts_to_amount(self, pair: str, num_contracts: float) -> float:
 
-        contract_size = self._get_contract_size(pair)
-        if contract_size and contract_size != 1:
-            return num_contracts * contract_size
-        else:
-            return num_contracts
+        contract_size = self.get_contract_size(pair)
+        return contracts_to_amount(num_contracts, contract_size)
 
     def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None:
         if exchange_config.get('sandbox'):
@@ -672,6 +668,12 @@ class Exchange:
                 f"Freqtrade does not support {mm_value} {trading_mode.value} on {self.name}"
             )
 
+    def get_option(self, param: str, default: Any = None) -> Any:
+        """
+        Get parameter value from _ft_has
+        """
+        return self._ft_has.get(param, default)
+
     def exchange_has(self, endpoint: str) -> bool:
         """
         Checks if exchange implements a specific API endpoint.
@@ -2207,6 +2209,7 @@ class Exchange:
 
     @retrier_async
     async def get_market_leverage_tiers(self, symbol: str) -> Tuple[str, List[Dict]]:
+        """ Leverage tiers per symbol """
         try:
             tier = await self._api_async.fetch_market_leverage_tiers(symbol)
             return symbol, tier
@@ -2238,12 +2241,21 @@ class Exchange:
 
                 tiers: Dict[str, List[Dict]] = {}
 
-                # Be verbose here, as this delays startup by ~1 minute.
-                logger.info(
-                    f"Initializing leverage_tiers for {len(symbols)} markets. "
-                    "This will take about a minute.")
+                tiers_cached = self.load_cached_leverage_tiers(self._config['stake_currency'])
+                if tiers_cached:
+                    tiers = tiers_cached
 
-                coros = [self.get_market_leverage_tiers(symbol) for symbol in sorted(symbols)]
+                coros = [
+                    self.get_market_leverage_tiers(symbol)
+                    for symbol in sorted(symbols) if symbol not in tiers]
+
+                # Be verbose here, as this delays startup by ~1 minute.
+                if coros:
+                    logger.info(
+                        f"Initializing leverage_tiers for {len(symbols)} markets. "
+                        "This will take about a minute.")
+                else:
+                    logger.info("Using cached leverage_tiers.")
 
                 async def gather_results():
                     return await asyncio.gather(*input_coro, return_exceptions=True)
@@ -2255,7 +2267,8 @@ class Exchange:
 
                     for symbol, res in results:
                         tiers[symbol] = res
-
+                if len(coros) > 0:
+                    self.cache_leverage_tiers(tiers, self._config['stake_currency'])
                 logger.info(f"Done initializing {len(symbols)} markets.")
 
                 return tiers
@@ -2264,6 +2277,30 @@ class Exchange:
         else:
             return {}
 
+    def cache_leverage_tiers(self, tiers: Dict[str, List[Dict]], stake_currency: str) -> None:
+
+        filename = self._config['datadir'] / "futures" / f"leverage_tiers_{stake_currency}.json"
+        if not filename.parent.is_dir():
+            filename.parent.mkdir(parents=True)
+        data = {
+            "updated": datetime.now(timezone.utc),
+            "data": tiers,
+        }
+        file_dump_json(filename, data)
+
+    def load_cached_leverage_tiers(self, stake_currency: str) -> Optional[Dict[str, List[Dict]]]:
+        filename = self._config['datadir'] / "futures" / f"leverage_tiers_{stake_currency}.json"
+        if filename.is_file():
+            tiers = file_load_json(filename)
+            updated = tiers.get('updated')
+            if updated:
+                updated_dt = parser.parse(updated)
+                if updated_dt < datetime.now(timezone.utc) - timedelta(days=1):
+                    logger.info("Cached leverage tiers are outdated. Will update.")
+                    return None
+            return tiers['data']
+        return None
+
     def fill_leverage_tiers(self) -> None:
         """
         Assigns property _leverage_tiers to a dictionary of information about the leverage
@@ -2279,10 +2316,10 @@ class Exchange:
     def parse_leverage_tier(self, tier) -> Dict:
         info = tier.get('info', {})
         return {
-            'min': tier['minNotional'],
-            'max': tier['maxNotional'],
-            'mmr': tier['maintenanceMarginRate'],
-            'lev': tier['maxLeverage'],
+            'minNotional': tier['minNotional'],
+            'maxNotional': tier['maxNotional'],
+            'maintenanceMarginRate': tier['maintenanceMarginRate'],
+            'maxLeverage': tier['maxLeverage'],
             'maintAmt': float(info['cum']) if 'cum' in info else None,
         }
 
@@ -2311,18 +2348,18 @@ class Exchange:
             pair_tiers = self._leverage_tiers[pair]
 
             if stake_amount == 0:
-                return self._leverage_tiers[pair][0]['lev']  # Max lev for lowest amount
+                return self._leverage_tiers[pair][0]['maxLeverage']  # Max lev for lowest amount
 
             for tier_index in range(len(pair_tiers)):
 
                 tier = pair_tiers[tier_index]
-                lev = tier['lev']
+                lev = tier['maxLeverage']
 
                 if tier_index < len(pair_tiers) - 1:
                     next_tier = pair_tiers[tier_index + 1]
-                    next_floor = next_tier['min'] / next_tier['lev']
+                    next_floor = next_tier['minNotional'] / next_tier['maxLeverage']
                     if next_floor > stake_amount:  # Next tier min too high for stake amount
-                        return min((tier['max'] / stake_amount), lev)
+                        return min((tier['maxNotional'] / stake_amount), lev)
                         #
                         # With the two leverage tiers below,
                         # - a stake amount of 150 would mean a max leverage of (10000 / 150) = 66.66
@@ -2343,10 +2380,11 @@ class Exchange:
                         #
 
                 else:  # if on the last tier
-                    if stake_amount > tier['max']:  # If stake is > than max tradeable amount
+                    if stake_amount > tier['maxNotional']:
+                        # If stake is > than max tradeable amount
                         raise InvalidOrderException(f'Amount {stake_amount} too high for {pair}')
                     else:
-                        return tier['lev']
+                        return tier['maxLeverage']
 
             raise OperationalException(
                 'Looped through all tiers without finding a max leverage. Should never be reached'
@@ -2399,6 +2437,7 @@ class Exchange:
             pair: str,
             open_rate: float,
             amount: float,  # quote currency, includes leverage
+            stake_amount: float,
             leverage: float,
             is_short: bool
     ) -> Optional[float]:
@@ -2408,13 +2447,13 @@ class Exchange:
         elif (
             self.trading_mode == TradingMode.FUTURES
         ):
-            wallet_balance = (amount * open_rate) / leverage
             isolated_liq = self.get_or_calculate_liquidation_price(
                 pair=pair,
                 open_rate=open_rate,
                 is_short=is_short,
-                position=amount,
-                wallet_balance=wallet_balance,
+                amount=amount,
+                stake_amount=stake_amount,
+                wallet_balance=stake_amount,  # In isolated mode, stake-amount = wallet size
                 mm_ex_1=0.0,
                 upnl_ex_1=0.0,
             )
@@ -2589,14 +2628,14 @@ class Exchange:
         # Dry-run
         open_rate: float,   # Entry price of position
         is_short: bool,
-        position: float,  # Absolute value of position size
+        amount: float,  # Absolute value of position size
+        stake_amount: float,
         wallet_balance: float,  # Or margin balance
         mm_ex_1: float = 0.0,  # (Binance) Cross only
         upnl_ex_1: float = 0.0,  # (Binance) Cross only
     ) -> Optional[float]:
         """
         Set's the margin mode on the exchange to cross or isolated for a specific pair
-        :param pair: base/quote currency pair (e.g. "ADA/USDT")
         """
         if self.trading_mode == TradingMode.SPOT:
             return None
@@ -2610,7 +2649,8 @@ class Exchange:
                 pair=pair,
                 open_rate=open_rate,
                 is_short=is_short,
-                position=position,
+                amount=amount,
+                stake_amount=stake_amount,
                 wallet_balance=wallet_balance,
                 mm_ex_1=mm_ex_1,
                 upnl_ex_1=upnl_ex_1
@@ -2639,22 +2679,24 @@ class Exchange:
         pair: str,
         open_rate: float,   # Entry price of position
         is_short: bool,
-        position: float,  # Absolute value of position size
+        amount: float,
+        stake_amount: float,
         wallet_balance: float,  # Or margin balance
         mm_ex_1: float = 0.0,  # (Binance) Cross only
         upnl_ex_1: float = 0.0,  # (Binance) Cross only
     ) -> Optional[float]:
         """
+        Important: Must be fetching data from cached values as this is used by backtesting!
         PERPETUAL:
          gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-price
          okex: https://www.okex.com/support/hc/en-us/articles/
             360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
-        Important: Must be fetching data from cached values as this is used by backtesting!
 
         :param exchange_name:
         :param open_rate: Entry price of position
         :param is_short: True if the trade is a short, false otherwise
-        :param position: Absolute value of position size incl. leverage (in base currency)
+        :param amount: Absolute value of position size incl. leverage (in base currency)
+        :param stake_amount: Stake amount - Collateral in settle currency.
         :param trading_mode: SPOT, MARGIN, FUTURES, etc.
         :param margin_mode: Either ISOLATED or CROSS
         :param wallet_balance: Amount of margin_mode in the wallet being used to trade
@@ -2668,7 +2710,7 @@ class Exchange:
 
         market = self.markets[pair]
         taker_fee_rate = market['taker']
-        mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, position)
+        mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, stake_amount)
 
         if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.ISOLATED:
 
@@ -2676,7 +2718,7 @@ class Exchange:
                 raise OperationalException(
                     "Freqtrade does not yet support inverse contracts")
 
-            value = wallet_balance / position
+            value = wallet_balance / amount
 
             mm_ratio_taker = (mm_ratio + taker_fee_rate)
             if is_short:
@@ -2712,8 +2754,8 @@ class Exchange:
             pair_tiers = self._leverage_tiers[pair]
 
             for tier in reversed(pair_tiers):
-                if nominal_value >= tier['min']:
-                    return (tier['mmr'], tier['maintAmt'])
+                if nominal_value >= tier['minNotional']:
+                    return (tier['maintenanceMarginRate'], tier['maintAmt'])
 
             raise OperationalException("nominal value can not be lower than 0")
             # The lowest notional_floor for any pair in fetch_leverage_tiers is always 0 because it
@@ -2855,6 +2897,33 @@ def market_is_active(market: Dict) -> bool:
     return market.get('active', True) is not False
 
 
+def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float:
+    """
+    Convert amount to contracts.
+    :param amount: amount to convert
+    :param contract_size: contract size - taken from exchange.get_contract_size(pair)
+    :return: num-contracts
+    """
+    if contract_size and contract_size != 1:
+        return amount / contract_size
+    else:
+        return amount
+
+
+def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) -> float:
+    """
+    Takes num-contracts and converts it to contract size
+    :param num_contracts: number of contracts
+    :param contract_size: contract size - taken from exchange.get_contract_size(pair)
+    :return: Amount
+    """
+
+    if contract_size and contract_size != 1:
+        return num_contracts * contract_size
+    else:
+        return num_contracts
+
+
 def amount_to_precision(amount: float, amount_precision: Optional[float],
                         precisionMode: Optional[int]) -> float:
     """
@@ -2879,6 +2948,29 @@ def amount_to_precision(amount: float, amount_precision: Optional[float],
     return amount
 
 
+def amount_to_contract_precision(
+        amount, amount_precision: Optional[float], precisionMode: Optional[int],
+        contract_size: Optional[float]) -> float:
+    """
+    Returns the amount to buy or sell to a precision the Exchange accepts
+    including calculation to and from contracts.
+    Re-implementation of ccxt internal methods - ensuring we can test the result is correct
+    based on our definitions.
+    :param amount: amount to truncate
+    :param amount_precision: amount precision to use.
+                             should be retrieved from markets[pair]['precision']['amount']
+    :param precisionMode: precision mode to use. Should be used from precisionMode
+                          one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
+    :param contract_size: contract size - taken from exchange.get_contract_size(pair)
+    :return: truncated amount
+    """
+    if amount_precision is not None and precisionMode is not None:
+        contracts = amount_to_contracts(amount, contract_size)
+        amount_p = amount_to_precision(contracts, amount_precision, precisionMode)
+        return contracts_to_amount(amount_p, contract_size)
+    return amount
+
+
 def price_to_precision(price: float, price_precision: Optional[float],
                        precisionMode: Optional[int]) -> float:
     """

freqtrade/exchange/gateio.py

@@ -25,7 +25,6 @@ class Gateio(Exchange):
 
     _ft_has: Dict = {
         "ohlcv_candle_limit": 1000,
-        "ohlcv_volume_currency": "quote",
         "time_in_force_parameter": "timeInForce",
         "order_time_in_force": ['gtc', 'ioc'],
         "stoploss_order_types": {"limit": "limit"},
@@ -34,7 +33,6 @@ class Gateio(Exchange):
 
     _ft_has_futures: Dict = {
         "needs_trading_fees": True,
-        "ohlcv_volume_currency": "base",
         "fee_cost_in_contracts": False,  # Set explicitly to false for clarity
         "order_props_in_contracts": ['amount', 'filled', 'remaining'],
     }

freqtrade/exchange/okx.py

@@ -39,6 +39,8 @@ class Okx(Exchange):
 
     net_only = True
 
+    _ccxt_params: Dict = {'options': {'brokerId': 'ffb5405ad327SUDE'}}
+
     def ohlcv_candle_limit(
             self, timeframe: str, candle_type: CandleType, since_ms: Optional[int] = None) -> int:
         """
@@ -144,4 +146,4 @@ class Okx(Exchange):
             return float('inf')
 
         pair_tiers = self._leverage_tiers[pair]
-        return pair_tiers[-1]['max'] / leverage
+        return pair_tiers[-1]['maxNotional'] / leverage

freqtrade/freqai/data_drawer.py

@@ -421,7 +421,7 @@ class FreqaiDataDrawer:
             )
 
         # if self.live:
-        self.model_dictionary[dk.model_filename] = model
+        self.model_dictionary[coin] = model
         self.pair_dict[coin]["model_filename"] = dk.model_filename
         self.pair_dict[coin]["data_path"] = str(dk.data_path)
         self.save_drawer_to_disk()
@@ -460,8 +460,8 @@ class FreqaiDataDrawer:
         )
 
         # try to access model in memory instead of loading object from disk to save time
-        if dk.live and dk.model_filename in self.model_dictionary:
-            model = self.model_dictionary[dk.model_filename]
+        if dk.live and coin in self.model_dictionary:
+            model = self.model_dictionary[coin]
         elif not dk.keras:
             model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
         else:
@@ -566,7 +566,6 @@ class FreqaiDataDrawer:
         for training according to user defined train_period_days
         metadata: dict = strategy furnished pair metadata
         """
-
         with self.history_lock:
             corr_dataframes: Dict[Any, Any] = {}
             base_dataframes: Dict[Any, Any] = {}

freqtrade/freqai/data_kitchen.py

@@ -454,7 +454,6 @@ class FreqaiDataKitchen:
         logger.info("reduced feature dimension by %s", n_components - n_keep_components)
         logger.info("explained variance %f", np.sum(pca2.explained_variance_ratio_))
         train_components = pca2.transform(self.data_dictionary["train_features"])
-        test_components = pca2.transform(self.data_dictionary["test_features"])
 
         self.data_dictionary["train_features"] = pd.DataFrame(
             data=train_components,
@@ -468,6 +467,7 @@ class FreqaiDataKitchen:
         self.training_features_list = self.data_dictionary["train_features"].columns
 
         if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
+            test_components = pca2.transform(self.data_dictionary["test_features"])
             self.data_dictionary["test_features"] = pd.DataFrame(
                 data=test_components,
                 columns=["PC" + str(i) for i in range(0, n_keep_components)],
@@ -506,10 +506,26 @@ class FreqaiDataKitchen:
         # logger.info("computing average mean distance for all training points")
         pairwise = pairwise_distances(
             self.data_dictionary["train_features"], n_jobs=self.thread_count)
-        avg_mean_dist = pairwise.mean(axis=1).mean()
+        # remove the diagonal distances which are itself distances ~0
+        np.fill_diagonal(pairwise, np.NaN)
+        pairwise = pairwise.reshape(-1, 1)
+        avg_mean_dist = pairwise[~np.isnan(pairwise)].mean()
 
         return avg_mean_dist
 
+    def get_outlier_percentage(self, dropped_pts: npt.NDArray) -> float:
+        """
+        Check if more than X% of points werer dropped during outlier detection.
+        """
+        outlier_protection_pct = self.freqai_config["feature_parameters"].get(
+            "outlier_protection_percentage", 30)
+        outlier_pct = (dropped_pts.sum() / len(dropped_pts)) * 100
+        if outlier_pct >= outlier_protection_pct:
+            self.svm_model = None
+            return outlier_pct
+        else:
+            return 0.0
+
     def use_SVM_to_remove_outliers(self, predict: bool) -> None:
         """
         Build/inference a Support Vector Machine to detect outliers
@@ -547,8 +563,16 @@ class FreqaiDataKitchen:
                 self.data_dictionary["train_features"]
             )
             y_pred = self.svm_model.predict(self.data_dictionary["train_features"])
-            dropped_points = np.where(y_pred == -1, 0, y_pred)
+            kept_points = np.where(y_pred == -1, 0, y_pred)
             # keep_index = np.where(y_pred == 1)
+            outlier_pct = self.get_outlier_percentage(1 - kept_points)
+            if outlier_pct:
+                logger.warning(
+                        f"SVM detected {outlier_pct:.2f}% of the points as outliers. "
+                        f"Keeping original dataset."
+                )
+                return
+
             self.data_dictionary["train_features"] = self.data_dictionary["train_features"][
                 (y_pred == 1)
             ]
@@ -560,7 +584,7 @@ class FreqaiDataKitchen:
             ]
 
             logger.info(
-                f"SVM tossed {len(y_pred) - dropped_points.sum()}"
+                f"SVM tossed {len(y_pred) - kept_points.sum()}"
                 f" train points from {len(y_pred)} total points."
             )
 
@@ -569,7 +593,7 @@ class FreqaiDataKitchen:
             # to reduce code duplication
             if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0:
                 y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
-                dropped_points = np.where(y_pred == -1, 0, y_pred)
+                kept_points = np.where(y_pred == -1, 0, y_pred)
                 self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
                     (y_pred == 1)
                 ]
@@ -580,7 +604,7 @@ class FreqaiDataKitchen:
                 ]
 
             logger.info(
-                f"SVM tossed {len(y_pred) - dropped_points.sum()}"
+                f"SVM tossed {len(y_pred) - kept_points.sum()}"
                 f" test points from {len(y_pred)} total points."
             )
 
@@ -598,6 +622,8 @@ class FreqaiDataKitchen:
         is an outlier.
         """
 
+        from math import cos, sin
+
         if predict:
             train_ft_df = self.data_dictionary['train_features']
             pred_ft_df = self.data_dictionary['prediction_features']
@@ -616,28 +642,60 @@ class FreqaiDataKitchen:
 
         else:
 
-            MinPts = len(self.data_dictionary['train_features'].columns) * 2
-            # measure pairwise distances to train_features.shape[1]*2 nearest neighbours
+            def normalise_distances(distances):
+                normalised_distances = (distances - distances.min()) / \
+                                        (distances.max() - distances.min())
+                return normalised_distances
+
+            def rotate_point(origin, point, angle):
+                # rotate a point counterclockwise by a given angle (in radians)
+                # around a given origin
+                x = origin[0] + cos(angle) * (point[0] - origin[0]) - \
+                                    sin(angle) * (point[1] - origin[1])
+                y = origin[1] + sin(angle) * (point[0] - origin[0]) + \
+                    cos(angle) * (point[1] - origin[1])
+                return (x, y)
+
+            MinPts = int(len(self.data_dictionary['train_features'].index) * 0.25)
+            # measure pairwise distances to nearest neighbours
             neighbors = NearestNeighbors(
                 n_neighbors=MinPts, n_jobs=self.thread_count)
             neighbors_fit = neighbors.fit(self.data_dictionary['train_features'])
             distances, _ = neighbors_fit.kneighbors(self.data_dictionary['train_features'])
-            distances = np.sort(distances, axis=0)
-            index_ten_pct = int(len(distances[:, 1]) * 0.1)
-            distances = distances[index_ten_pct:, 1]
-            epsilon = distances[-1]
+            distances = np.sort(distances, axis=0).mean(axis=1)
+
+            normalised_distances = normalise_distances(distances)
+            x_range = np.linspace(0, 1, len(distances))
+            line = np.linspace(normalised_distances[0],
+                               normalised_distances[-1], len(normalised_distances))
+            deflection = np.abs(normalised_distances - line)
+            max_deflection_loc = np.where(deflection == deflection.max())[0][0]
+            origin = x_range[max_deflection_loc], line[max_deflection_loc]
+            point = x_range[max_deflection_loc], normalised_distances[max_deflection_loc]
+            rot_angle = np.pi / 4
+            elbow_loc = rotate_point(origin, point, rot_angle)
+
+            epsilon = elbow_loc[1] * (distances[-1] - distances[0]) + distances[0]
 
             clustering = DBSCAN(eps=epsilon, min_samples=MinPts,
                                 n_jobs=int(self.thread_count)).fit(
                                                     self.data_dictionary['train_features']
                                                 )
 
-            logger.info(f'DBSCAN found eps of {epsilon}.')
+            logger.info(f'DBSCAN found eps of {epsilon:.2f}.')
 
             self.data['DBSCAN_eps'] = epsilon
             self.data['DBSCAN_min_samples'] = MinPts
             dropped_points = np.where(clustering.labels_ == -1, 1, 0)
 
+            outlier_pct = self.get_outlier_percentage(dropped_points)
+            if outlier_pct:
+                logger.warning(
+                        f"DBSCAN detected {outlier_pct:.2f}% of the points as outliers. "
+                        f"Keeping original dataset."
+                )
+                return
+
             self.data_dictionary['train_features'] = self.data_dictionary['train_features'][
                 (clustering.labels_ != -1)
             ]
@@ -655,114 +713,6 @@ class FreqaiDataKitchen:
 
         return
 
-    def compute_inlier_metric(self, set_='train') -> None:
-        """
-
-        Compute inlier metric from backwards distance distributions.
-        This metric defines how well features from a timepoint fit
-        into previous timepoints.
-        """
-
-        import scipy.stats as ss
-
-        no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"]
-        weib_pct = self.freqai_config["feature_parameters"]["inlier_metric_weibull_cutoff"]
-
-        if set_ == 'train':
-            compute_df = copy.deepcopy(self.data_dictionary['train_features'])
-        elif set_ == 'test':
-            compute_df = copy.deepcopy(self.data_dictionary['test_features'])
-        else:
-            compute_df = copy.deepcopy(self.data_dictionary['prediction_features'])
-
-        compute_df_reindexed = compute_df.reindex(
-            index=np.flip(compute_df.index)
-        )
-
-        pairwise = pd.DataFrame(
-            np.triu(
-                pairwise_distances(compute_df_reindexed, n_jobs=self.thread_count)
-            ),
-            columns=compute_df_reindexed.index,
-            index=compute_df_reindexed.index
-        )
-        pairwise = pairwise.round(5)
-
-        column_labels = [
-            '{}{}'.format('d', i) for i in range(1, no_prev_pts + 1)
-        ]
-        distances = pd.DataFrame(
-            columns=column_labels, index=compute_df.index
-        )
-
-        for index in compute_df.index[no_prev_pts:]:
-            current_row = pairwise.loc[[index]]
-            current_row_no_zeros = current_row.loc[
-                :, (current_row != 0).any(axis=0)
-            ]
-            distances.loc[[index]] = current_row_no_zeros.iloc[
-                :, :no_prev_pts
-            ]
-        distances = distances.replace([np.inf, -np.inf], np.nan)
-        drop_index = pd.isnull(distances).any(1)
-        distances = distances[drop_index == 0]
-
-        inliers = pd.DataFrame(index=distances.index)
-        for key in distances.keys():
-            current_distances = distances[key].dropna()
-            fit_params = ss.weibull_min.fit(current_distances)
-            cutoff = ss.weibull_min.ppf(weib_pct, *fit_params)
-            is_inlier = np.where(
-                current_distances <= cutoff, 1, 0
-            )
-            df_inlier = pd.DataFrame(
-                {key + '_IsInlier': is_inlier}, index=distances.index
-            )
-            inliers = pd.concat(
-                [inliers, df_inlier], axis=1
-            )
-
-        inlier_metric = pd.DataFrame(
-            data=inliers.sum(axis=1) / no_prev_pts,
-            columns=['inlier_metric'],
-            index=compute_df.index
-        )
-
-        inlier_metric = 2 * (inlier_metric - inlier_metric.min()) / \
-            (inlier_metric.max() - inlier_metric.min()) - 1
-
-        if set_ in ('train', 'test'):
-            inlier_metric = inlier_metric.iloc[no_prev_pts:]
-            compute_df = compute_df.iloc[no_prev_pts:]
-            self.remove_beginning_points_from_data_dict(set_, no_prev_pts)
-            self.data_dictionary[f'{set_}_features'] = pd.concat(
-                [compute_df, inlier_metric], axis=1)
-        else:
-            self.data_dictionary['prediction_features'] = pd.concat(
-                [compute_df, inlier_metric], axis=1)
-            self.data_dictionary['prediction_features'].fillna(0, inplace=True)
-
-        return None
-
-    def remove_beginning_points_from_data_dict(self, set_='train', no_prev_pts: int = 10):
-        features = self.data_dictionary[f'{set_}_features']
-        weights = self.data_dictionary[f'{set_}_weights']
-        labels = self.data_dictionary[f'{set_}_labels']
-        self.data_dictionary[f'{set_}_weights'] = weights[no_prev_pts:]
-        self.data_dictionary[f'{set_}_features'] = features.iloc[no_prev_pts:]
-        self.data_dictionary[f'{set_}_labels'] = labels.iloc[no_prev_pts:]
-
-    def add_noise_to_training_features(self) -> None:
-        """
-        Add noise to train features to reduce the risk of overfitting.
-        """
-        mu = 0  # no shift
-        sigma = self.freqai_config["feature_parameters"]["noise_standard_deviation"]
-        compute_df = self.data_dictionary['train_features']
-        noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]])
-        self.data_dictionary['train_features'] += noise
-        return
-
     def find_features(self, dataframe: DataFrame) -> None:
         """
         Find features in the strategy provided dataframe
@@ -801,9 +751,17 @@ class FreqaiDataKitchen:
             0,
         )
 
+        outlier_pct = self.get_outlier_percentage(1 - do_predict)
+        if outlier_pct:
+            logger.warning(
+                    f"DI detected {outlier_pct:.2f}% of the points as outliers. "
+                    f"Keeping original dataset."
+            )
+            return
+
         if (len(do_predict) - do_predict.sum()) > 0:
             logger.info(
-                f"DI tossed {len(do_predict) - do_predict.sum():.2f} predictions for "
+                f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
                 "being too far from training data"
             )
 
@@ -978,13 +936,6 @@ class FreqaiDataKitchen:
             data_load_timerange.stopts = int(time)
             retrain = True
 
-        # logger.info(
-        #     f"downloading data for "
-        #     f"{(data_load_timerange.stopts-data_load_timerange.startts)/SECONDS_IN_DAY:.2f} "
-        #     " days. "
-        #     f"Extension of {additional_seconds/SECONDS_IN_DAY:.2f} days"
-        # )
-
         return retrain, trained_timerange, data_load_timerange
 
     def set_new_model_names(self, pair: str, trained_timerange: TimeRange):

freqtrade/freqai/freqai_interface.py

@@ -66,6 +66,7 @@ class IFreqaiModel(ABC):
             "data_split_parameters", {})
         self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
             "model_training_parameters", {})
+        self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
         self.retrain = False
         self.first = True
         self.set_full_path()
@@ -73,21 +74,21 @@ class IFreqaiModel(ABC):
         self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
         self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
         self.scanning = False
-        self.ft_params = self.freqai_info["feature_parameters"]
         self.keras: bool = self.freqai_info.get("keras", False)
-        if self.keras and self.ft_params.get("DI_threshold", 0):
-            self.ft_params["DI_threshold"] = 0
+        if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
+            self.freqai_info["feature_parameters"]["DI_threshold"] = 0
             logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
         self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
-        if self.ft_params.get("inlier_metric_window", 0):
-            self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
         self.pair_it = 0
+        self.pair_it_train = 0
         self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
         self.last_trade_database_summary: DataFrame = {}
         self.current_trade_database_summary: DataFrame = {}
         self.analysis_lock = Lock()
         self.inference_time: float = 0
+        self.train_time: float = 0
         self.begin_time: float = 0
+        self.begin_time_train: float = 0
         self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
 
     def assert_config(self, config: Dict[str, Any]) -> None:
@@ -130,11 +131,20 @@ class IFreqaiModel(ABC):
             dk = self.start_backtesting(dataframe, metadata, self.dk)
 
         dataframe = dk.remove_features_from_df(dk.return_dataframe)
-        del dk
+        self.clean_up()
         if self.live:
             self.inference_timer('stop')
         return dataframe
 
+    def clean_up(self):
+        """
+        Objects that should be handled by GC already between coins, but
+        are explicitly shown here to help demonstrate the non-persistence of these
+        objects.
+        """
+        self.model = None
+        self.dk = None
+
     @threaded
     def start_scanning(self, strategy: IStrategy) -> None:
         """
@@ -161,9 +171,11 @@ class IFreqaiModel(ABC):
                 dk.set_paths(pair, new_trained_timerange.stopts)
 
                 if retrain:
+                    self.train_timer('start')
                     self.train_model_in_series(
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
                     )
+                    self.train_timer('stop')
 
             self.dd.save_historic_predictions_to_disk()
 
@@ -385,25 +397,24 @@ class IFreqaiModel(ABC):
 
     def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
         """
-        Base data cleaning method for train.
-        Functions here improve/modify the input data by identifying outliers,
-        computing additional metrics, adding noise, reducing dimensionality etc.
+        Base data cleaning method for train
+        Any function inside this method should drop training data points from the filtered_dataframe
+        based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an
+        example of how outlier data points are dropped from the dataframe used for training.
         """
 
-        ft_params = self.freqai_info["feature_parameters"]
-
-        if ft_params.get(
+        if self.freqai_info["feature_parameters"].get(
             "principal_component_analysis", False
         ):
             dk.principal_component_analysis()
 
-        if ft_params.get("use_SVM_to_remove_outliers", False):
+        if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
             dk.use_SVM_to_remove_outliers(predict=False)
 
-        if ft_params.get("DI_threshold", 0):
+        if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
             dk.data["avg_mean_dist"] = dk.compute_distances()
 
-        if ft_params.get("use_DBSCAN_to_remove_outliers", False):
+        if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
             if dk.pair in self.dd.old_DBSCAN_eps:
                 eps = self.dd.old_DBSCAN_eps[dk.pair]
             else:
@@ -411,36 +422,29 @@ class IFreqaiModel(ABC):
             dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
             self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
 
-        if ft_params.get('inlier_metric_window', 0):
-            dk.compute_inlier_metric(set_='train')
-            if self.freqai_info["data_split_parameters"]["test_size"] > 0:
-                dk.compute_inlier_metric(set_='test')
-
-        if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
-            dk.add_noise_to_training_features()
-
     def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
         """
         Base data cleaning method for predict.
-        Functions here are complementary to the functions of data_cleaning_train.
+        These functions each modify dk.do_predict, which is a dataframe with equal length
+        to the number of candles coming from and returning to the strategy. Inside do_predict,
+         1 allows prediction and < 0 signals to the strategy that the model is not confident in
+         the prediction.
+         See FreqaiDataKitchen::remove_outliers() for an example
+        of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
+        for buy signals.
         """
-        ft_params = self.freqai_info["feature_parameters"]
-
-        if ft_params.get('inlier_metric_window', 0):
-            dk.compute_inlier_metric(set_='predict')
-
-        if ft_params.get(
+        if self.freqai_info["feature_parameters"].get(
             "principal_component_analysis", False
         ):
             dk.pca_transform(dataframe)
 
-        if ft_params.get("use_SVM_to_remove_outliers", False):
+        if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
             dk.use_SVM_to_remove_outliers(predict=True)
 
-        if ft_params.get("DI_threshold", 0):
+        if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
             dk.check_if_pred_in_training_spaces()
 
-        if ft_params.get("use_DBSCAN_to_remove_outliers", False):
+        if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
             dk.use_DBSCAN_to_remove_outliers(predict=True)
 
     def model_exists(
@@ -490,8 +494,7 @@ class IFreqaiModel(ABC):
         data_load_timerange: TimeRange,
     ):
         """
-        Retrieve data and train model in single threaded mode (only used if model directory is empty
-        upon startup for dry/live )
+        Retrieve data and train model.
         :param new_trained_timerange: TimeRange = the timerange to train the model on
         :param metadata: dict = strategy provided metadata
         :param strategy: IStrategy = user defined strategy object
@@ -622,6 +625,24 @@ class IFreqaiModel(ABC):
                 self.inference_time = 0
         return
 
+    def train_timer(self, do='start'):
+        """
+        Timer designed to track the cumulative time spent training the full pairlist in
+        FreqAI.
+        """
+        if do == 'start':
+            self.pair_it_train += 1
+            self.begin_time_train = time.time()
+        elif do == 'stop':
+            end = time.time()
+            self.train_time += (end - self.begin_time_train)
+            if self.pair_it_train == self.total_pairs:
+                logger.info(
+                    f'Total time spent training pairlist {self.train_time:.2f} seconds')
+                self.pair_it_train = 0
+                self.train_time = 0
+        return
+
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.
 

freqtrade/freqtradebot.py

@@ -21,8 +21,7 @@ from freqtrade.enums import (ExitCheckTuple, ExitType, RPCMessageType, RunMode,
                              State, TradingMode)
 from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, PricingError)
-from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
-from freqtrade.exchange.exchange import timeframe_to_next_date
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
 from freqtrade.misc import safe_value_fallback, safe_value_fallback2
 from freqtrade.mixins import LoggingMixin
 from freqtrade.persistence import Order, PairLocks, Trade, init_db
@@ -240,7 +239,7 @@ class FreqtradeBot(LoggingMixin):
                 'status':
                     f"{len(open_trades)} open trades active.\n\n"
                     f"Handle these trades manually on {self.exchange.name}, "
-                    f"or '/start' the bot again and use '/stopbuy' "
+                    f"or '/start' the bot again and use '/stopentry' "
                     f"to handle open trades gracefully. \n"
                     f"{'Note: Trades are simulated (dry run).' if self.config['dry_run'] else ''}",
             }
@@ -271,7 +270,7 @@ class FreqtradeBot(LoggingMixin):
         Return the number of free open trades slots or 0 if
         max number of open trades reached
         """
-        open_trades = len(Trade.get_open_trades())
+        open_trades = Trade.get_open_trade_count()
         return max(0, self.config['max_open_trades'] - open_trades)
 
     def update_funding_fees(self):
@@ -290,13 +289,14 @@ class FreqtradeBot(LoggingMixin):
 
     def startup_backpopulate_precision(self):
 
-        trades = Trade.get_trades([Trade.precision_mode.is_(None)])
+        trades = Trade.get_trades([Trade.contract_size.is_(None)])
         for trade in trades:
             if trade.exchange != self.exchange.id:
                 continue
             trade.precision_mode = self.exchange.precisionMode
             trade.amount_precision = self.exchange.get_precision_amount(trade.pair)
             trade.price_precision = self.exchange.get_precision_price(trade.pair)
+            trade.contract_size = self.exchange.get_contract_size(trade.pair)
         Trade.commit()
 
     def startup_update_open_orders(self):
@@ -755,6 +755,7 @@ class FreqtradeBot(LoggingMixin):
                 amount_precision=self.exchange.get_precision_amount(pair),
                 price_precision=self.exchange.get_precision_price(pair),
                 precision_mode=self.exchange.precisionMode,
+                contract_size=self.exchange.get_contract_size(pair),
             )
         else:
             # This is additional buy, we reset fee_open_currency so timeout checking can work
@@ -1551,9 +1552,10 @@ class FreqtradeBot(LoggingMixin):
         trade.close_rate_requested = limit
         trade.exit_reason = exit_reason
 
-        # Lock pair for one candle to prevent immediate re-trading
-        self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
-                                reason='Auto lock')
+        if not sub_trade_amt:
+            # Lock pair for one candle to prevent immediate re-trading
+            self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
+                                    reason='Auto lock')
 
         self._notify_exit(trade, order_type, sub_trade=bool(sub_trade_amt), order=order_obj)
         # In case of market sell orders the order can be closed immediately
@@ -1733,6 +1735,7 @@ class FreqtradeBot(LoggingMixin):
                     leverage=trade.leverage,
                     pair=trade.pair,
                     amount=trade.amount,
+                    stake_amount=trade.stake_amount,
                     open_rate=trade.open_rate,
                     is_short=trade.is_short
                 ))

freqtrade/optimize/backtesting.py

@@ -23,7 +23,8 @@ from freqtrade.data.dataprovider import DataProvider
 from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType, RunMode,
                              TradingMode)
 from freqtrade.exceptions import DependencyException, OperationalException
-from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
+from freqtrade.exchange import (amount_to_contract_precision, price_to_precision,
+                                timeframe_to_minutes, timeframe_to_seconds)
 from freqtrade.mixins import LoggingMixin
 from freqtrade.optimize.backtest_caching import get_strategy_run_id
 from freqtrade.optimize.bt_progress import BTProgress
@@ -266,7 +267,7 @@ class Backtesting:
             funding_rates_dict = history.load_data(
                 datadir=self.config['datadir'],
                 pairs=self.pairlists.whitelist,
-                timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'],
+                timeframe=self.exchange.get_option('mark_ohlcv_timeframe'),
                 timerange=self.timerange,
                 startup_candles=0,
                 fail_without_data=True,
@@ -278,12 +279,12 @@ class Backtesting:
             mark_rates_dict = history.load_data(
                 datadir=self.config['datadir'],
                 pairs=self.pairlists.whitelist,
-                timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'],
+                timeframe=self.exchange.get_option('mark_ohlcv_timeframe'),
                 timerange=self.timerange,
                 startup_candles=0,
                 fail_without_data=True,
                 data_format=self.config.get('dataformat_ohlcv', 'json'),
-                candle_type=CandleType.from_string(self.exchange._ft_has["mark_ohlcv_price"])
+                candle_type=CandleType.from_string(self.exchange.get_option("mark_ohlcv_price"))
             )
             # Combine data to avoid combining the data per trade.
             unavailable_pairs = []
@@ -533,12 +534,16 @@ class Backtesting:
 
         # Check if we should increase our position
         if stake_amount is not None and stake_amount > 0.0:
-
-            pos_trade = self._enter_trade(
-                trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade)
-            if pos_trade is not None:
-                self.wallets.update()
-                return pos_trade
+            check_adjust_entry = True
+            if self.strategy.max_entry_position_adjustment > -1:
+                entry_count = trade.nr_of_successful_entries
+                check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment)
+            if check_adjust_entry:
+                pos_trade = self._enter_trade(
+                    trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade)
+                if pos_trade is not None:
+                    self.wallets.update()
+                    return pos_trade
 
         if stake_amount is not None and stake_amount < 0.0:
             amount = abs(stake_amount) / current_rate
@@ -549,7 +554,8 @@ class Backtesting:
             if remaining < min_stake:
                 # Remaining stake is too low to be sold.
                 return trade
-            pos_trade = self._exit_trade(trade, row, current_rate, amount)
+            exit_ = ExitCheckTuple(ExitType.PARTIAL_EXIT)
+            pos_trade = self._get_exit_for_signal(trade, row, exit_, amount)
             if pos_trade is not None:
                 order = pos_trade.orders[-1]
                 if self._get_order_filled(order.price, row):
@@ -569,12 +575,7 @@ class Backtesting:
 
         # Check if we need to adjust our current positions
         if self.strategy.position_adjustment_enable:
-            check_adjust_entry = True
-            if self.strategy.max_entry_position_adjustment > -1:
-                entry_count = trade.nr_of_successful_entries
-                check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment)
-            if check_adjust_entry:
-                trade = self._get_adjust_trade_entry_for_candle(trade, row)
+            trade = self._get_adjust_trade_entry_for_candle(trade, row)
 
         enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
         exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
@@ -589,14 +590,15 @@ class Backtesting:
                 return t
         return None
 
-    def _get_exit_for_signal(self, trade: LocalTrade, row: Tuple,
-                             exit_: ExitCheckTuple) -> Optional[LocalTrade]:
+    def _get_exit_for_signal(
+            self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple,
+            amount: Optional[float] = None) -> Optional[LocalTrade]:
 
         exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
         if exit_.exit_flag:
             trade.close_date = exit_candle_time
             exit_reason = exit_.exit_reason
-
+            amount_ = amount if amount is not None else trade.amount
             trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
             try:
                 close_rate = self._get_close_rate(row, trade, exit_, trade_dur)
@@ -605,7 +607,8 @@ class Backtesting:
             # call the custom exit price,with default value as previous close_rate
             current_profit = trade.calc_profit_ratio(close_rate)
             order_type = self.strategy.order_types['exit']
-            if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT):
+            if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT,
+                                   ExitType.PARTIAL_EXIT):
                 # Checks and adds an exit tag, after checking that the length of the
                 # row has the length for an exit tag column
                 if (
@@ -633,22 +636,23 @@ class Backtesting:
             # Confirm trade exit:
             time_in_force = self.strategy.order_time_in_force['exit']
 
-            if (exit_.exit_type != ExitType.LIQUIDATION and not strategy_safe_wrapper(
-                self.strategy.confirm_trade_exit, default_retval=True)(
-                    pair=trade.pair,
-                    trade=trade,  # type: ignore[arg-type]
-                    order_type=order_type,
-                    amount=trade.amount,
-                    rate=close_rate,
-                    time_in_force=time_in_force,
-                    sell_reason=exit_reason,  # deprecated
-                    exit_reason=exit_reason,
-                    current_time=exit_candle_time)):
+            if (exit_.exit_type not in (ExitType.LIQUIDATION, ExitType.PARTIAL_EXIT)
+                    and not strategy_safe_wrapper(
+                    self.strategy.confirm_trade_exit, default_retval=True)(
+                        pair=trade.pair,
+                        trade=trade,  # type: ignore[arg-type]
+                        order_type=order_type,
+                        amount=amount_,
+                        rate=close_rate,
+                        time_in_force=time_in_force,
+                        sell_reason=exit_reason,  # deprecated
+                        exit_reason=exit_reason,
+                        current_time=exit_candle_time)):
                 return None
 
             trade.exit_reason = exit_reason
 
-            return self._exit_trade(trade, row, close_rate, trade.amount)
+            return self._exit_trade(trade, row, close_rate, amount_)
         return None
 
     def _exit_trade(self, trade: LocalTrade, sell_row: Tuple,
@@ -656,7 +660,10 @@ class Backtesting:
         self.order_id_counter += 1
         exit_candle_time = sell_row[DATE_IDX].to_pydatetime()
         order_type = self.strategy.order_types['exit']
-        amount = amount or trade.amount
+        # amount = amount or trade.amount
+        amount = amount_to_contract_precision(amount or trade.amount, trade.amount_precision,
+                                              self.precision_mode, trade.contract_size)
+        rate = price_to_precision(close_rate, trade.price_precision, self.precision_mode)
         order = Order(
             id=self.order_id_counter,
             ft_trade_id=trade.id,
@@ -670,12 +677,12 @@ class Backtesting:
             side=trade.exit_side,
             order_type=order_type,
             status="open",
-            price=close_rate,
-            average=close_rate,
+            price=rate,
+            average=rate,
             amount=amount,
             filled=0,
             remaining=amount,
-            cost=amount * close_rate,
+            cost=amount * rate,
         )
         trade.orders.append(order)
         return trade
@@ -821,7 +828,17 @@ class Backtesting:
         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)
-            amount = round((stake_amount / propose_rate) * leverage, 8)
+            precision_price = self.exchange.get_precision_price(pair)
+            propose_rate = price_to_precision(propose_rate, precision_price, self.precision_mode)
+            amount_p = (stake_amount / propose_rate) * leverage
+
+            contract_size = self.exchange.get_contract_size(pair)
+            precision_amount = self.exchange.get_precision_amount(pair)
+            amount = amount_to_contract_precision(amount_p, precision_amount, self.precision_mode,
+                                                  contract_size)
+            # Backcalculate actual stake amount.
+            stake_amount = amount * propose_rate / leverage
+
             is_short = (direction == 'short')
             # Necessary for Margin trading. Disabled until support is enabled.
             # interest_rate = self.exchange.get_interest_rate()
@@ -850,9 +867,10 @@ class Backtesting:
                     trading_mode=self.trading_mode,
                     leverage=leverage,
                     # interest_rate=interest_rate,
-                    amount_precision=self.exchange.get_precision_amount(pair),
-                    price_precision=self.exchange.get_precision_price(pair),
+                    amount_precision=precision_amount,
+                    price_precision=precision_price,
                     precision_mode=self.precision_mode,
+                    contract_size=contract_size,
                     orders=[],
                 )
 
@@ -862,6 +880,7 @@ class Backtesting:
                 pair=pair,
                 open_rate=propose_rate,
                 amount=amount,
+                stake_amount=trade.stake_amount,
                 leverage=leverage,
                 is_short=is_short,
             ))

freqtrade/optimize/hyperopt.py

@@ -24,13 +24,15 @@ from pandas import DataFrame
 from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
 from freqtrade.data.converter import trim_dataframes
 from freqtrade.data.history import get_timerange
+from freqtrade.enums import HyperoptState
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
 from freqtrade.optimize.hyperopt_auto import HyperOptAuto
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
-from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
+from freqtrade.optimize.hyperopt_tools import (HyperoptStateContainer, HyperoptTools,
+                                               hyperopt_serializer)
 from freqtrade.optimize.optimize_reports import generate_strategy_stats
 from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
 
@@ -74,10 +76,14 @@ class Hyperopt:
         self.dimensions: List[Dimension] = []
 
         self.config = config
+        self.min_date: datetime
+        self.max_date: datetime
 
         self.backtesting = Backtesting(self.config)
         self.pairlist = self.backtesting.pairlists.whitelist
         self.custom_hyperopt: HyperOptAuto
+        self.analyze_per_epoch = self.config.get('analyze_per_epoch', False)
+        HyperoptStateContainer.set_state(HyperoptState.STARTUP)
 
         if not self.config.get('hyperopt'):
             self.custom_hyperopt = HyperOptAuto(self.config)
@@ -290,6 +296,7 @@ class Hyperopt:
         Called once per epoch to optimize whatever is configured.
         Keep this function as optimized as possible!
         """
+        HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE)
         backtest_start_time = datetime.now(timezone.utc)
         params_dict = self._get_params_dict(self.dimensions, raw_params)
 
@@ -321,6 +328,10 @@ class Hyperopt:
 
         with self.data_pickle_file.open('rb') as f:
             processed = load(f, mmap_mode='r')
+            if self.analyze_per_epoch:
+                # Data is not yet analyzed, rerun populate_indicators.
+                processed = self.advise_and_trim(processed)
+
         bt_results = self.backtesting.backtest(
             processed=processed,
             start_date=self.min_date,
@@ -406,22 +417,33 @@ class Hyperopt:
     def _set_random_state(self, random_state: Optional[int]) -> int:
         return random_state or random.randint(1, 2**16 - 1)
 
-    def prepare_hyperopt_data(self) -> None:
-        data, timerange = self.backtesting.load_bt_data()
-        self.backtesting.load_bt_data_detail()
-        logger.info("Dataload complete. Calculating indicators")
-
+    def advise_and_trim(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
         preprocessed = self.backtesting.strategy.advise_all_indicators(data)
 
         # Trim startup period from analyzed dataframe to get correct dates for output.
-        processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
+        processed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
         self.min_date, self.max_date = get_timerange(processed)
+        return processed
 
-        logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
-                    f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
-                    f'({(self.max_date - self.min_date).days} days)..')
-        # Store non-trimmed data - will be trimmed after signal generation.
-        dump(preprocessed, self.data_pickle_file)
+    def prepare_hyperopt_data(self) -> None:
+        HyperoptStateContainer.set_state(HyperoptState.DATALOAD)
+        data, self.timerange = self.backtesting.load_bt_data()
+        self.backtesting.load_bt_data_detail()
+        logger.info("Dataload complete. Calculating indicators")
+
+        if not self.analyze_per_epoch:
+            HyperoptStateContainer.set_state(HyperoptState.INDICATORS)
+
+            preprocessed = self.advise_and_trim(data)
+
+            logger.info(f'Hyperopting with data from '
+                        f'{self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
+                        f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
+                        f'({(self.max_date - self.min_date).days} days)..')
+            # Store non-trimmed data - will be trimmed after signal generation.
+            dump(preprocessed, self.data_pickle_file)
+        else:
+            dump(data, self.data_pickle_file)
 
     def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
         """

freqtrade/optimize/hyperopt_tools.py

@@ -13,6 +13,7 @@ from colorama import Fore, Style
 from pandas import isna, json_normalize
 
 from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES
+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
 from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
@@ -32,6 +33,15 @@ def hyperopt_serializer(x):
     return str(x)
 
 
+class HyperoptStateContainer():
+    """ Singleton class to track state of hyperopt"""
+    state: HyperoptState = HyperoptState.OPTIMIZE
+
+    @classmethod
+    def set_state(cls, value: HyperoptState):
+        cls.state = value
+
+
 class HyperoptTools():
 
     @staticmethod

freqtrade/persistence/migrations.py

@@ -133,6 +133,7 @@ def migrate_trades_and_orders_table(
     amount_precision = get_column_def(cols, 'amount_precision', 'null')
     price_precision = get_column_def(cols, 'price_precision', 'null')
     precision_mode = get_column_def(cols, 'precision_mode', 'null')
+    contract_size = get_column_def(cols, 'contract_size', 'null')
 
     # Schema migration necessary
     with engine.begin() as connection:
@@ -161,7 +162,7 @@ def migrate_trades_and_orders_table(
             timeframe, open_trade_value, close_profit_abs,
             trading_mode, leverage, liquidation_price, is_short,
             interest_rate, funding_fees, realized_profit,
-            amount_precision, price_precision, precision_mode
+            amount_precision, price_precision, precision_mode, contract_size
             )
         select id, lower(exchange), pair, {base_currency} base_currency,
             {stake_currency} stake_currency,
@@ -189,7 +190,7 @@ def migrate_trades_and_orders_table(
             {is_short} is_short, {interest_rate} interest_rate,
             {funding_fees} funding_fees, {realized_profit} realized_profit,
             {amount_precision} amount_precision, {price_precision} price_precision,
-            {precision_mode} precision_mode
+            {precision_mode} precision_mode, {contract_size} contract_size
             from {trade_back_name}
             """))
 
@@ -308,7 +309,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
     # if ('orders' not in previous_tables
     # or not has_column(cols_orders, 'stop_price')):
     migrating = False
-    if not has_column(cols_trades, 'precision_mode'):
+    if not has_column(cols_trades, 'contract_size'):
         migrating = True
         logger.info(f"Running database migration for trades - "
                     f"backup: {table_back_name}, {order_table_bak_name}")

freqtrade/persistence/trade_model.py

@@ -14,7 +14,7 @@ from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPE
                                  BuySell, LongShort)
 from freqtrade.enums import ExitType, TradingMode
 from freqtrade.exceptions import DependencyException, OperationalException
-from freqtrade.exchange import amount_to_precision, price_to_precision
+from freqtrade.exchange import amount_to_contract_precision, price_to_precision
 from freqtrade.leverage import interest
 from freqtrade.persistence.base import _DECL_BASE
 from freqtrade.util import FtPrecise
@@ -296,6 +296,7 @@ class LocalTrade():
     amount_precision: Optional[float] = None
     price_precision: Optional[float] = None
     precision_mode: Optional[int] = None
+    contract_size: Optional[float] = None
 
     # Leverage trading properties
     liquidation_price: Optional[float] = None
@@ -623,7 +624,8 @@ class LocalTrade():
             else:
                 logger.warning(
                     f'Got different open_order_id {self.open_order_id} != {order.order_id}')
-            amount_tr = amount_to_precision(self.amount, self.amount_precision, self.precision_mode)
+            amount_tr = amount_to_contract_precision(self.amount, self.amount_precision,
+                                                     self.precision_mode, self.contract_size)
             if isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC):
                 self.close(order.safe_price)
             else:
@@ -841,7 +843,7 @@ class LocalTrade():
         avg_price = FtPrecise(0.0)
         close_profit = 0.0
         close_profit_abs = 0.0
-
+        profit = None
         for o in self.orders:
             if o.ft_is_open or not o.filled:
                 continue
@@ -868,8 +870,6 @@ class LocalTrade():
                 close_profit_abs += profit
                 close_profit = self.calc_profit_ratio(
                     exit_rate, amount=exit_amount, open_rate=avg_price)
-                if current_amount <= ZERO:
-                    profit = close_profit_abs
             else:
                 total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price)
 
@@ -878,8 +878,8 @@ class LocalTrade():
             self.realized_profit = close_profit_abs
             self.close_profit_abs = profit
 
-        current_amount_tr = amount_to_precision(float(current_amount),
-                                                self.amount_precision, self.precision_mode)
+        current_amount_tr = amount_to_contract_precision(
+            float(current_amount), self.amount_precision, self.precision_mode, self.contract_size)
         if current_amount_tr > 0.0:
             # Trade is still open
             # Leverage not updated, as we don't allow changing leverage through DCA at the moment.
@@ -894,6 +894,7 @@ class LocalTrade():
             # Close profit abs / maximum owned
             # Fees are considered as they are part of close_profit_abs
             self.close_profit = (close_profit_abs / total_stake) * self.leverage
+            self.close_profit_abs = close_profit_abs
 
     def select_order_by_order_id(self, order_id: str) -> Optional[Order]:
         """
@@ -1044,6 +1045,16 @@ class LocalTrade():
         """
         return Trade.get_trades_proxy(is_open=True)
 
+    @staticmethod
+    def get_open_trade_count() -> int:
+        """
+        get open trade count
+        """
+        if Trade.use_db:
+            return Trade.query.filter(Trade.is_open.is_(True)).count()
+        else:
+            return len(LocalTrade.trades_open)
+
     @staticmethod
     def stoploss_reinitialization(desired_stoploss):
         """
@@ -1132,6 +1143,7 @@ class Trade(_DECL_BASE, LocalTrade):
     amount_precision = Column(Float, nullable=True)
     price_precision = Column(Float, nullable=True)
     precision_mode = Column(Integer, nullable=True)
+    contract_size = Column(Float, nullable=True)
 
     # Leverage trading properties
     leverage = Column(Float, nullable=True, default=1.0)

freqtrade/plugins/pairlist/VolumePairList.py

@@ -73,7 +73,7 @@ class VolumePairList(IPairList):
 
         if (not self._use_range and not (
                 self._exchange.exchange_has('fetchTickers')
-                and self._exchange._ft_has["tickers_have_quoteVolume"])):
+                and self._exchange.get_option("tickers_have_quoteVolume"))):
             raise OperationalException(
                 "Exchange does not support dynamic whitelist in this configuration. "
                 "Please edit your config and either remove Volumepairlist, "
@@ -193,7 +193,7 @@ class VolumePairList(IPairList):
                     ) in candles else None
                 # in case of candle data calculate typical price and quoteVolume for candle
                 if pair_candles is not None and not pair_candles.empty:
-                    if self._exchange._ft_has["ohlcv_volume_currency"] == "base":
+                    if self._exchange.get_option("ohlcv_volume_currency") == "base":
                         pair_candles['typical_price'] = (pair_candles['high'] + pair_candles['low']
                                                          + pair_candles['close']) / 3
 

freqtrade/resolvers/iresolver.py

@@ -193,7 +193,10 @@ class IResolver:
         :return: List of dicts containing 'name', 'class' and 'location' entries
         """
         logger.debug(f"Searching for {cls.object_type.__name__} '{directory}'")
-        objects = []
+        objects: List[Dict[str, Any]] = []
+        if not directory.is_dir():
+            logger.info(f"'{directory}' is not a directory, skipping.")
+            return objects
         for entry in directory.iterdir():
             if (
                 recursive and entry.is_dir()

freqtrade/rpc/api_server/api_v1.py

@@ -216,9 +216,10 @@ def stop(rpc: RPC = Depends(get_rpc)):
     return rpc._rpc_stop()
 
 
+@router.post('/stopentry', response_model=StatusMsg, tags=['botcontrol'])
 @router.post('/stopbuy', response_model=StatusMsg, tags=['botcontrol'])
 def stop_buy(rpc: RPC = Depends(get_rpc)):
-    return rpc._rpc_stopbuy()
+    return rpc._rpc_stopentry()
 
 
 @router.post('/reload_config', response_model=StatusMsg, tags=['botcontrol'])

freqtrade/rpc/rpc.py

@@ -657,7 +657,7 @@ class RPC:
         self._freqtrade.state = State.RELOAD_CONFIG
         return {'status': 'Reloading config ...'}
 
-    def _rpc_stopbuy(self) -> Dict[str, str]:
+    def _rpc_stopentry(self) -> Dict[str, str]:
         """
         Handler to stop buying, but handle open trades gracefully.
         """
@@ -665,7 +665,7 @@ class RPC:
             # Set 'max_open_trades' to 0
             self._freqtrade.config['max_open_trades'] = 0
 
-        return {'status': 'No more buy will occur from now. Run /reload_config to reset.'}
+        return {'status': 'No more entries will occur from now. Run /reload_config to reset.'}
 
     def __exec_force_exit(self, trade: Trade, ordertype: Optional[str],
                           amount: Optional[float] = None) -> None:

freqtrade/rpc/telegram.py

@@ -114,18 +114,20 @@ class Telegram(RPCHandler):
         # TODO: DRY! - its not good to list all valid cmds here. But otherwise
         #       this needs refactoring of the whole telegram module (same
         #       problem in _help()).
-        valid_keys: List[str] = [r'/start$', r'/stop$', r'/status$', r'/status table$',
-                                 r'/trades$', r'/performance$', r'/buys', r'/entries',
-                                 r'/sells', r'/exits', r'/mix_tags',
-                                 r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+',
-                                 r'/stats$', r'/count$', r'/locks$', r'/balance$',
-                                 r'/stopbuy$', r'/reload_config$', r'/show_config$',
-                                 r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$',
-                                 r'/blacklist$', r'/bl_delete$',
-                                 r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$',
-                                 r'/forcebuy$', r'/forcelong$', r'/forceshort$',
-                                 r'/forcesell$', r'/forceexit$',
-                                 r'/edge$', r'/health$', r'/help$', r'/version$']
+        valid_keys: List[str] = [
+            r'/start$', r'/stop$', r'/status$', r'/status table$',
+            r'/trades$', r'/performance$', r'/buys', r'/entries',
+            r'/sells', r'/exits', r'/mix_tags',
+            r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+',
+            r'/stats$', r'/count$', r'/locks$', r'/balance$',
+            r'/stopbuy$', r'/stopentry$', r'/reload_config$', r'/show_config$',
+            r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$',
+            r'/blacklist$', r'/bl_delete$',
+            r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$',
+            r'/forcebuy$', r'/forcelong$', r'/forceshort$',
+            r'/forcesell$', r'/forceexit$',
+            r'/edge$', r'/health$', r'/help$', r'/version$'
+        ]
         # Create keys for generation
         valid_keys_print = [k.replace('$', '') for k in valid_keys]
 
@@ -182,7 +184,7 @@ class Telegram(RPCHandler):
             CommandHandler(['unlock', 'delete_locks'], self._delete_locks),
             CommandHandler(['reload_config', 'reload_conf'], self._reload_config),
             CommandHandler(['show_config', 'show_conf'], self._show_config),
-            CommandHandler('stopbuy', self._stopbuy),
+            CommandHandler(['stopbuy', 'stopentry'], self._stopentry),
             CommandHandler('whitelist', self._whitelist),
             CommandHandler('blacklist', self._blacklist),
             CommandHandler(['blacklist_delete', 'bl_delete'], self._blacklist_delete),
@@ -984,7 +986,7 @@ class Telegram(RPCHandler):
         self._send_msg(f"Status: `{msg['status']}`")
 
     @authorized_only
-    def _stopbuy(self, update: Update, context: CallbackContext) -> None:
+    def _stopentry(self, update: Update, context: CallbackContext) -> None:
         """
         Handler for /stop_buy.
         Sets max_open_trades to 0 and gracefully sells all open trades
@@ -992,7 +994,7 @@ class Telegram(RPCHandler):
         :param update: message update
         :return: None
         """
-        msg = self._rpc._rpc_stopbuy()
+        msg = self._rpc._rpc_stopentry()
         self._send_msg(f"Status: `{msg['status']}`")
 
     @authorized_only
@@ -1488,7 +1490,7 @@ class Telegram(RPCHandler):
             "------------\n"
             "*/start:* `Starts the trader`\n"
             "*/stop:* Stops the trader\n"
-            "*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
+            "*/stopentry:* `Stops entering, but handles open trades gracefully` \n"
             "*/forceexit <trade_id>|all:* `Instantly exits the given trade or all trades, "
             "regardless of profit`\n"
             "*/fx <trade_id>|all:* `Alias to /forceexit`\n"

freqtrade/strategy/parameters.py

@@ -7,6 +7,9 @@ from abc import ABC, abstractmethod
 from contextlib import suppress
 from typing import Any, Optional, Sequence, Union
 
+from freqtrade.enums.hyperoptstate import HyperoptState
+from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer
+
 
 with suppress(ImportError):
     from skopt.space import Integer, Real, Categorical
@@ -57,6 +60,13 @@ class BaseParameter(ABC):
         Get-space - will be used by Hyperopt to get the hyperopt Space
         """
 
+    def can_optimize(self):
+        return (
+            self.in_space
+            and self.optimize
+            and HyperoptStateContainer.state != HyperoptState.OPTIMIZE
+        )
+
 
 class NumericParameter(BaseParameter):
     """ Internal parameter used for Numeric purposes """
@@ -133,7 +143,7 @@ class IntParameter(NumericParameter):
         Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
         calculating 100ds of indicators.
         """
-        if self.in_space and self.optimize:
+        if self.can_optimize():
             # Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
             return range(self.low, self.high + 1)
         else:
@@ -212,7 +222,7 @@ class DecimalParameter(NumericParameter):
         Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
         calculating 100ds of indicators.
         """
-        if self.in_space and self.optimize:
+        if self.can_optimize():
             low = int(self.low * pow(10, self._decimals))
             high = int(self.high * pow(10, self._decimals)) + 1
             return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)]
@@ -261,7 +271,7 @@ class CategoricalParameter(BaseParameter):
         Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
         calculating 100ds of indicators.
         """
-        if self.in_space and self.optimize:
+        if self.can_optimize():
             return self.opt_range
         else:
             return [self.value]

requirements-dev.txt

@@ -17,7 +17,7 @@ pytest-mock==3.8.2
 pytest-random-order==1.0.4
 isort==5.10.1
 # For datetime mocking
-time-machine==2.7.1
+time-machine==2.8.1
 
 # Convert jupyter notebooks to markdown documents
 nbconvert==6.5.3
@@ -25,6 +25,6 @@ nbconvert==6.5.3
 # mypy types
 types-cachetools==5.2.1
 types-filelock==3.2.7
-types-requests==2.28.8
+types-requests==2.28.9
 types-tabulate==0.8.11
 types-python-dateutil==2.8.19

requirements.txt

@@ -2,7 +2,7 @@ numpy==1.23.2
 pandas==1.4.3
 pandas-ta==0.3.14b
 
-ccxt==1.92.20
+ccxt==1.92.52
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==37.0.4
 aiohttp==3.8.1
@@ -12,7 +12,7 @@ arrow==1.2.2
 cachetools==4.2.2
 requests==2.28.1
 urllib3==1.26.11
-jsonschema==4.9.1
+jsonschema==4.14.0
 TA-Lib==0.4.24
 technical==1.3.0
 tabulate==0.8.10
@@ -34,7 +34,7 @@ orjson==3.7.12
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.79.0
+fastapi==0.79.1
 uvicorn==0.18.2
 pyjwt==2.4.0
 aiofiles==0.8.0

scripts/rest_client.py

@@ -361,6 +361,13 @@ class FtRestClient():
         """
         return self._get("sysinfo")
 
+    def health(self):
+        """Provides a quick health check of the running bot.
+
+        :return: json object
+        """
+        return self._get("health")
+
 
 def add_arguments():
     parser = argparse.ArgumentParser()

tests/commands/test_commands.py

@@ -1430,6 +1430,27 @@ def test_start_list_data(testdatadir, capsys):
     assert "\n|      XRP/USDT |          1h |      futures |\n" in captured.out
     assert "\n|      XRP/USDT |      1h, 8h |         mark |\n" in captured.out
 
+    args = [
+        "list-data",
+        "--data-format-ohlcv",
+        "json",
+        "--pairs", "XRP/ETH",
+        "--datadir",
+        str(testdatadir),
+        "--show-timerange",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_list_data(pargs)
+    captured = capsys.readouterr()
+    assert "Found 2 pair / timeframe combinations." in captured.out
+    assert ("\n|    Pair |   Timeframe |   Type |                From |                  To |\n"
+            in captured.out)
+    assert "UNITTEST/BTC" not in captured.out
+    assert (
+            "\n| XRP/ETH |          1m |   spot | 2019-10-11 00:00:00 | 2019-10-13 11:19:00 |\n"
+            in captured.out)
+
 
 @pytest.mark.usefixtures("init_persistence")
 def test_show_trades(mocker, fee, capsys, caplog):

tests/conftest.py

@@ -3085,416 +3085,416 @@ def leverage_tiers():
     return {
         "1000SHIB/USDT": [
             {
-                'min': 0,
-                'max': 50000,
-                'mmr': 0.01,
-                'lev': 50,
+                'minNotional': 0,
+                'maxNotional': 50000,
+                'maintenanceMarginRate': 0.01,
+                'maxLeverage': 50,
                 'maintAmt': 0.0
             },
             {
-                'min': 50000,
-                'max': 150000,
-                'mmr': 0.025,
-                'lev': 20,
+                'minNotional': 50000,
+                'maxNotional': 150000,
+                'maintenanceMarginRate': 0.025,
+                'maxLeverage': 20,
                 'maintAmt': 750.0
             },
             {
-                'min': 150000,
-                'max': 250000,
-                'mmr': 0.05,
-                'lev': 10,
+                'minNotional': 150000,
+                'maxNotional': 250000,
+                'maintenanceMarginRate': 0.05,
+                'maxLeverage': 10,
                 'maintAmt': 4500.0
             },
             {
-                'min': 250000,
-                'max': 500000,
-                'mmr': 0.1,
-                'lev': 5,
+                'minNotional': 250000,
+                'maxNotional': 500000,
+                'maintenanceMarginRate': 0.1,
+                'maxLeverage': 5,
                 'maintAmt': 17000.0
             },
             {
-                'min': 500000,
-                'max': 1000000,
-                'mmr': 0.125,
-                'lev': 4,
+                'minNotional': 500000,
+                'maxNotional': 1000000,
+                'maintenanceMarginRate': 0.125,
+                'maxLeverage': 4,
                 'maintAmt': 29500.0
             },
             {
-                'min': 1000000,
-                'max': 2000000,
-                'mmr': 0.25,
-                'lev': 2,
+                'minNotional': 1000000,
+                'maxNotional': 2000000,
+                'maintenanceMarginRate': 0.25,
+                'maxLeverage': 2,
                 'maintAmt': 154500.0
             },
             {
-                'min': 2000000,
-                'max': 30000000,
-                'mmr': 0.5,
-                'lev': 1,
+                'minNotional': 2000000,
+                'maxNotional': 30000000,
+                'maintenanceMarginRate': 0.5,
+                'maxLeverage': 1,
                 'maintAmt': 654500.0
             },
         ],
         "1INCH/USDT": [
             {
-                'min': 0,
-                'max': 5000,
-                'mmr': 0.012,
-                'lev': 50,
+                'minNotional': 0,
+                'maxNotional': 5000,
+                'maintenanceMarginRate': 0.012,
+                'maxLeverage': 50,
                 'maintAmt': 0.0
             },
             {
-                'min': 5000,
-                'max': 25000,
-                'mmr': 0.025,
-                'lev': 20,
+                'minNotional': 5000,
+                'maxNotional': 25000,
+                'maintenanceMarginRate': 0.025,
+                'maxLeverage': 20,
                 'maintAmt': 65.0
             },
             {
-                'min': 25000,
-                'max': 100000,
-                'mmr': 0.05,
-                'lev': 10,
+                'minNotional': 25000,
+                'maxNotional': 100000,
+                'maintenanceMarginRate': 0.05,
+                'maxLeverage': 10,
                 'maintAmt': 690.0
             },
             {
-                'min': 100000,
-                'max': 250000,
-                'mmr': 0.1,
-                'lev': 5,
+                'minNotional': 100000,
+                'maxNotional': 250000,
+                'maintenanceMarginRate': 0.1,
+                'maxLeverage': 5,
                 'maintAmt': 5690.0
             },
             {
-                'min': 250000,
-                'max': 1000000,
-                'mmr': 0.125,
-                'lev': 2,
+                'minNotional': 250000,
+                'maxNotional': 1000000,
+                'maintenanceMarginRate': 0.125,
+                'maxLeverage': 2,
                 'maintAmt': 11940.0
             },
             {
-                'min': 1000000,
-                'max': 100000000,
-                'mmr': 0.5,
-                'lev': 1,
+                'minNotional': 1000000,
+                'maxNotional': 100000000,
+                'maintenanceMarginRate': 0.5,
+                'maxLeverage': 1,
                 'maintAmt': 386940.0
             },
         ],
         "AAVE/USDT": [
             {
-                'min': 0,
-                'max': 5000,
-                'mmr': 0.01,
-                'lev': 50,
+                'minNotional': 0,
+                'maxNotional': 5000,
+                'maintenanceMarginRate': 0.01,
+                'maxLeverage': 50,
                 'maintAmt': 0.0
             },
             {
-                'min': 5000,
-                'max': 25000,
-                'mmr': 0.02,
-                'lev': 25,
+                'minNotional': 5000,
+                'maxNotional': 25000,
+                'maintenanceMarginRate': 0.02,
+                'maxLeverage': 25,
                 'maintAmt': 75.0
             },
             {
-                'min': 25000,
-                'max': 100000,
-                'mmr': 0.05,
-                'lev': 10,
+                'minNotional': 25000,
+                'maxNotional': 100000,
+                'maintenanceMarginRate': 0.05,
+                'maxLeverage': 10,
                 'maintAmt': 700.0
             },
             {
-                'min': 100000,
-                'max': 250000,
-                'mmr': 0.1,
-                'lev': 5,
+                'minNotional': 100000,
+                'maxNotional': 250000,
+                'maintenanceMarginRate': 0.1,
+                'maxLeverage': 5,
                 'maintAmt': 5700.0
             },
             {
-                'min': 250000,
-                'max': 1000000,
-                'mmr': 0.125,
-                'lev': 2,
+                'minNotional': 250000,
+                'maxNotional': 1000000,
+                'maintenanceMarginRate': 0.125,
+                'maxLeverage': 2,
                 'maintAmt': 11950.0
             },
             {
-                'min': 10000000,
-                'max': 50000000,
-                'mmr': 0.5,
-                'lev': 1,
+                'minNotional': 10000000,
+                'maxNotional': 50000000,
+                'maintenanceMarginRate': 0.5,
+                'maxLeverage': 1,
                 'maintAmt': 386950.0
             },
         ],
         "ADA/BUSD": [
             {
-                "min": 0,
-                "max": 100000,
-                "mmr": 0.025,
-                "lev": 20,
+                "minNotional": 0,
+                "maxNotional": 100000,
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
                 "maintAmt": 0.0
             },
             {
-                "min": 100000,
-                "max": 500000,
-                "mmr": 0.05,
-                "lev": 10,
+                "minNotional": 100000,
+                "maxNotional": 500000,
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
                 "maintAmt": 2500.0
             },
             {
-                "min": 500000,
-                "max": 1000000,
-                "mmr": 0.1,
-                "lev": 5,
+                "minNotional": 500000,
+                "maxNotional": 1000000,
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
                 "maintAmt": 27500.0
             },
             {
-                "min": 1000000,
-                "max": 2000000,
-                "mmr": 0.15,
-                "lev": 3,
+                "minNotional": 1000000,
+                "maxNotional": 2000000,
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
                 "maintAmt": 77500.0
             },
             {
-                "min": 2000000,
-                "max": 5000000,
-                "mmr": 0.25,
-                "lev": 2,
+                "minNotional": 2000000,
+                "maxNotional": 5000000,
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
                 "maintAmt": 277500.0
             },
             {
-                "min": 5000000,
-                "max": 30000000,
-                "mmr": 0.5,
-                "lev": 1,
+                "minNotional": 5000000,
+                "maxNotional": 30000000,
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
                 "maintAmt": 1527500.0
             },
         ],
         'BNB/BUSD': [
             {
-                "min": 0,       # stake(before leverage) = 0
-                "max": 100000,  # max stake(before leverage) = 5000
-                "mmr": 0.025,
-                "lev": 20,
+                "minNotional": 0,       # stake(before leverage) = 0
+                "maxNotional": 100000,  # max stake(before leverage) = 5000
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
                 "maintAmt": 0.0
             },
             {
-                "min": 100000,  # stake = 10000.0
-                "max": 500000,  # max_stake = 50000.0
-                "mmr": 0.05,
-                "lev": 10,
+                "minNotional": 100000,  # stake = 10000.0
+                "maxNotional": 500000,  # max_stake = 50000.0
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
                 "maintAmt": 2500.0
             },
             {
-                "min": 500000,   # stake = 100000.0
-                "max": 1000000,  # max_stake = 200000.0
-                "mmr": 0.1,
-                "lev": 5,
+                "minNotional": 500000,   # stake = 100000.0
+                "maxNotional": 1000000,  # max_stake = 200000.0
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
                 "maintAmt": 27500.0
             },
             {
-                "min": 1000000,  # stake = 333333.3333333333
-                "max": 2000000,  # max_stake = 666666.6666666666
-                "mmr": 0.15,
-                "lev": 3,
+                "minNotional": 1000000,  # stake = 333333.3333333333
+                "maxNotional": 2000000,  # max_stake = 666666.6666666666
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
                 "maintAmt": 77500.0
             },
             {
-                "min": 2000000,  # stake = 1000000.0
-                "max": 5000000,  # max_stake = 2500000.0
-                "mmr": 0.25,
-                "lev": 2,
+                "minNotional": 2000000,  # stake = 1000000.0
+                "maxNotional": 5000000,  # max_stake = 2500000.0
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
                 "maintAmt": 277500.0
             },
             {
-                "min": 5000000,   # stake = 5000000.0
-                "max": 30000000,  # max_stake = 30000000.0
-                "mmr": 0.5,
-                "lev": 1,
+                "minNotional": 5000000,   # stake = 5000000.0
+                "maxNotional": 30000000,  # max_stake = 30000000.0
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
                 "maintAmt": 1527500.0
             }
         ],
         'BNB/USDT': [
             {
-                "min": 0,      # stake = 0.0
-                "max": 10000,  # max_stake = 133.33333333333334
-                "mmr": 0.0065,
-                "lev": 75,
+                "minNotional": 0,      # stake = 0.0
+                "maxNotional": 10000,  # max_stake = 133.33333333333334
+                "maintenanceMarginRate": 0.0065,
+                "maxLeverage": 75,
                 "maintAmt": 0.0
             },
             {
-                "min": 10000,  # stake = 200.0
-                "max": 50000,  # max_stake = 1000.0
-                "mmr": 0.01,
-                "lev": 50,
+                "minNotional": 10000,  # stake = 200.0
+                "maxNotional": 50000,  # max_stake = 1000.0
+                "maintenanceMarginRate": 0.01,
+                "maxLeverage": 50,
                 "maintAmt": 35.0
             },
             {
-                "min": 50000,   # stake = 2000.0
-                "max": 250000,  # max_stake = 10000.0
-                "mmr": 0.02,
-                "lev": 25,
+                "minNotional": 50000,   # stake = 2000.0
+                "maxNotional": 250000,  # max_stake = 10000.0
+                "maintenanceMarginRate": 0.02,
+                "maxLeverage": 25,
                 "maintAmt": 535.0
             },
             {
-                "min": 250000,   # stake = 25000.0
-                "max": 1000000,  # max_stake = 100000.0
-                "mmr": 0.05,
-                "lev": 10,
+                "minNotional": 250000,   # stake = 25000.0
+                "maxNotional": 1000000,  # max_stake = 100000.0
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
                 "maintAmt": 8035.0
             },
             {
-                "min": 1000000,  # stake = 200000.0
-                "max": 2000000,  # max_stake = 400000.0
-                "mmr": 0.1,
-                "lev": 5,
+                "minNotional": 1000000,  # stake = 200000.0
+                "maxNotional": 2000000,  # max_stake = 400000.0
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
                 "maintAmt": 58035.0
             },
             {
-                "min": 2000000,  # stake = 500000.0
-                "max": 5000000,  # max_stake = 1250000.0
-                "mmr": 0.125,
-                "lev": 4,
+                "minNotional": 2000000,  # stake = 500000.0
+                "maxNotional": 5000000,  # max_stake = 1250000.0
+                "maintenanceMarginRate": 0.125,
+                "maxLeverage": 4,
                 "maintAmt": 108035.0
             },
             {
-                "min": 5000000,   # stake = 1666666.6666666667
-                "max": 10000000,  # max_stake = 3333333.3333333335
-                "mmr": 0.15,
-                "lev": 3,
+                "minNotional": 5000000,   # stake = 1666666.6666666667
+                "maxNotional": 10000000,  # max_stake = 3333333.3333333335
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
                 "maintAmt": 233035.0
             },
             {
-                "min": 10000000,  # stake = 5000000.0
-                "max": 20000000,  # max_stake = 10000000.0
-                "mmr": 0.25,
-                "lev": 2,
+                "minNotional": 10000000,  # stake = 5000000.0
+                "maxNotional": 20000000,  # max_stake = 10000000.0
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
                 "maintAmt": 1233035.0
             },
             {
-                "min": 20000000,  # stake = 20000000.0
-                "max": 50000000,  # max_stake = 50000000.0
-                "mmr": 0.5,
-                "lev": 1,
+                "minNotional": 20000000,  # stake = 20000000.0
+                "maxNotional": 50000000,  # max_stake = 50000000.0
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
                 "maintAmt": 6233035.0
             },
         ],
         'BTC/USDT': [
             {
-                "min": 0,      # stake = 0.0
-                "max": 50000,  # max_stake = 400.0
-                "mmr": 0.004,
-                "lev": 125,
+                "minNotional": 0,      # stake = 0.0
+                "maxNotional": 50000,  # max_stake = 400.0
+                "maintenanceMarginRate": 0.004,
+                "maxLeverage": 125,
                 "maintAmt": 0.0
             },
             {
-                "min": 50000,   # stake = 500.0
-                "max": 250000,  # max_stake = 2500.0
-                "mmr": 0.005,
-                "lev": 100,
+                "minNotional": 50000,   # stake = 500.0
+                "maxNotional": 250000,  # max_stake = 2500.0
+                "maintenanceMarginRate": 0.005,
+                "maxLeverage": 100,
                 "maintAmt": 50.0
             },
             {
-                "min": 250000,   # stake = 5000.0
-                "max": 1000000,  # max_stake = 20000.0
-                "mmr": 0.01,
-                "lev": 50,
+                "minNotional": 250000,   # stake = 5000.0
+                "maxNotional": 1000000,  # max_stake = 20000.0
+                "maintenanceMarginRate": 0.01,
+                "maxLeverage": 50,
                 "maintAmt": 1300.0
             },
             {
-                "min": 1000000,  # stake = 50000.0
-                "max": 7500000,  # max_stake = 375000.0
-                "mmr": 0.025,
-                "lev": 20,
+                "minNotional": 1000000,  # stake = 50000.0
+                "maxNotional": 7500000,  # max_stake = 375000.0
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
                 "maintAmt": 16300.0
             },
             {
-                "min": 7500000,   # stake = 750000.0
-                "max": 40000000,  # max_stake = 4000000.0
-                "mmr": 0.05,
-                "lev": 10,
+                "minNotional": 7500000,   # stake = 750000.0
+                "maxNotional": 40000000,  # max_stake = 4000000.0
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
                 "maintAmt": 203800.0
             },
             {
-                "min": 40000000,   # stake = 8000000.0
-                "max": 100000000,  # max_stake = 20000000.0
-                "mmr": 0.1,
-                "lev": 5,
+                "minNotional": 40000000,   # stake = 8000000.0
+                "maxNotional": 100000000,  # max_stake = 20000000.0
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
                 "maintAmt": 2203800.0
             },
             {
-                "min": 100000000,  # stake = 25000000.0
-                "max": 200000000,  # max_stake = 50000000.0
-                "mmr": 0.125,
-                "lev": 4,
+                "minNotional": 100000000,  # stake = 25000000.0
+                "maxNotional": 200000000,  # max_stake = 50000000.0
+                "maintenanceMarginRate": 0.125,
+                "maxLeverage": 4,
                 "maintAmt": 4703800.0
             },
             {
-                "min": 200000000,  # stake = 66666666.666666664
-                "max": 400000000,  # max_stake = 133333333.33333333
-                "mmr": 0.15,
-                "lev": 3,
+                "minNotional": 200000000,  # stake = 66666666.666666664
+                "maxNotional": 400000000,  # max_stake = 133333333.33333333
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
                 "maintAmt": 9703800.0
             },
             {
-                "min": 400000000,  # stake = 200000000.0
-                "max": 600000000,  # max_stake = 300000000.0
-                "mmr": 0.25,
-                "lev": 2,
+                "minNotional": 400000000,  # stake = 200000000.0
+                "maxNotional": 600000000,  # max_stake = 300000000.0
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
                 "maintAmt": 4.97038E7
             },
             {
-                "min": 600000000,   # stake = 600000000.0
-                "max": 1000000000,  # max_stake = 1000000000.0
-                "mmr": 0.5,
-                "lev": 1,
+                "minNotional": 600000000,   # stake = 600000000.0
+                "maxNotional": 1000000000,  # max_stake = 1000000000.0
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
                 "maintAmt": 1.997038E8
             },
         ],
         "ZEC/USDT": [
             {
-                'min': 0,
-                'max': 50000,
-                'mmr': 0.01,
-                'lev': 50,
+                'minNotional': 0,
+                'maxNotional': 50000,
+                'maintenanceMarginRate': 0.01,
+                'maxLeverage': 50,
                 'maintAmt': 0.0
             },
             {
-                'min': 50000,
-                'max': 150000,
-                'mmr': 0.025,
-                'lev': 20,
+                'minNotional': 50000,
+                'maxNotional': 150000,
+                'maintenanceMarginRate': 0.025,
+                'maxLeverage': 20,
                 'maintAmt': 750.0
             },
             {
-                'min': 150000,
-                'max': 250000,
-                'mmr': 0.05,
-                'lev': 10,
+                'minNotional': 150000,
+                'maxNotional': 250000,
+                'maintenanceMarginRate': 0.05,
+                'maxLeverage': 10,
                 'maintAmt': 4500.0
             },
             {
-                'min': 250000,
-                'max': 500000,
-                'mmr': 0.1,
-                'lev': 5,
+                'minNotional': 250000,
+                'maxNotional': 500000,
+                'maintenanceMarginRate': 0.1,
+                'maxLeverage': 5,
                 'maintAmt': 17000.0
             },
             {
-                'min': 500000,
-                'max': 1000000,
-                'mmr': 0.125,
-                'lev': 4,
+                'minNotional': 500000,
+                'maxNotional': 1000000,
+                'maintenanceMarginRate': 0.125,
+                'maxLeverage': 4,
                 'maintAmt': 29500.0
             },
             {
-                'min': 1000000,
-                'max': 2000000,
-                'mmr': 0.25,
-                'lev': 2,
+                'minNotional': 1000000,
+                'maxNotional': 2000000,
+                'maintenanceMarginRate': 0.25,
+                'maxLeverage': 2,
                 'maintAmt': 154500.0
             },
             {
-                'min': 2000000,
-                'max': 30000000,
-                'mmr': 0.5,
-                'lev': 1,
+                'minNotional': 2000000,
+                'maxNotional': 30000000,
+                'maintenanceMarginRate': 0.5,
+                'maxLeverage': 1,
                 'maintAmt': 654500.0
             },
         ]

tests/exchange/test_binance.py

@@ -376,96 +376,96 @@ def test_fill_leverage_tiers_binance(default_conf, mocker):
     assert exchange._leverage_tiers == {
         'ADA/BUSD': [
             {
-                "min": 0,
-                "max": 100000,
-                "mmr": 0.025,
-                "lev": 20,
+                "minNotional": 0,
+                "maxNotional": 100000,
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
                 "maintAmt": 0.0
             },
             {
-                "min": 100000,
-                "max": 500000,
-                "mmr": 0.05,
-                "lev": 10,
+                "minNotional": 100000,
+                "maxNotional": 500000,
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
                 "maintAmt": 2500.0
             },
             {
-                "min": 500000,
-                "max": 1000000,
-                "mmr": 0.1,
-                "lev": 5,
+                "minNotional": 500000,
+                "maxNotional": 1000000,
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
                 "maintAmt": 27500.0
             },
             {
-                "min": 1000000,
-                "max": 2000000,
-                "mmr": 0.15,
-                "lev": 3,
+                "minNotional": 1000000,
+                "maxNotional": 2000000,
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
                 "maintAmt": 77500.0
             },
             {
-                "min": 2000000,
-                "max": 5000000,
-                "mmr": 0.25,
-                "lev": 2,
+                "minNotional": 2000000,
+                "maxNotional": 5000000,
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
                 "maintAmt": 277500.0
             },
             {
-                "min": 5000000,
-                "max": 30000000,
-                "mmr": 0.5,
-                "lev": 1,
+                "minNotional": 5000000,
+                "maxNotional": 30000000,
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
                 "maintAmt": 1527500.0
             }
         ],
         "ZEC/USDT": [
             {
-                'min': 0,
-                'max': 50000,
-                'mmr': 0.01,
-                'lev': 50,
+                'minNotional': 0,
+                'maxNotional': 50000,
+                'maintenanceMarginRate': 0.01,
+                'maxLeverage': 50,
                 'maintAmt': 0.0
             },
             {
-                'min': 50000,
-                'max': 150000,
-                'mmr': 0.025,
-                'lev': 20,
+                'minNotional': 50000,
+                'maxNotional': 150000,
+                'maintenanceMarginRate': 0.025,
+                'maxLeverage': 20,
                 'maintAmt': 750.0
             },
             {
-                'min': 150000,
-                'max': 250000,
-                'mmr': 0.05,
-                'lev': 10,
+                'minNotional': 150000,
+                'maxNotional': 250000,
+                'maintenanceMarginRate': 0.05,
+                'maxLeverage': 10,
                 'maintAmt': 4500.0
             },
             {
-                'min': 250000,
-                'max': 500000,
-                'mmr': 0.1,
-                'lev': 5,
+                'minNotional': 250000,
+                'maxNotional': 500000,
+                'maintenanceMarginRate': 0.1,
+                'maxLeverage': 5,
                 'maintAmt': 17000.0
             },
             {
-                'min': 500000,
-                'max': 1000000,
-                'mmr': 0.125,
-                'lev': 4,
+                'minNotional': 500000,
+                'maxNotional': 1000000,
+                'maintenanceMarginRate': 0.125,
+                'maxLeverage': 4,
                 'maintAmt': 29500.0
             },
             {
-                'min': 1000000,
-                'max': 2000000,
-                'mmr': 0.25,
-                'lev': 2,
+                'minNotional': 1000000,
+                'maxNotional': 2000000,
+                'maintenanceMarginRate': 0.25,
+                'maxLeverage': 2,
                 'maintAmt': 154500.0
             },
             {
-                'min': 2000000,
-                'max': 30000000,
-                'mmr': 0.5,
-                'lev': 1,
+                'minNotional': 2000000,
+                'maxNotional': 30000000,
+                'maintenanceMarginRate': 0.5,
+                'maxLeverage': 1,
                 'maintAmt': 654500.0
             },
         ]

tests/exchange/test_ccxt_compat.py

@@ -137,6 +137,10 @@ def exchange_futures(request, exchange_conf, class_mocker):
             'freqtrade.exchange.binance.Binance.fill_leverage_tiers')
         class_mocker.patch('freqtrade.exchange.exchange.Exchange.fetch_trading_fees')
         class_mocker.patch('freqtrade.exchange.okx.Okx.additional_exchange_init')
+        class_mocker.patch('freqtrade.exchange.exchange.Exchange.load_cached_leverage_tiers',
+                           return_value=None)
+        class_mocker.patch('freqtrade.exchange.exchange.Exchange.cache_leverage_tiers')
+
         exchange = ExchangeResolver.load_exchange(
             request.param, exchange_conf, validate=True, load_leverage_tiers=True)
 
@@ -405,14 +409,14 @@ class TestCCXTExchange():
                 assert (isinstance(futures_leverage, float) or isinstance(futures_leverage, int))
                 assert futures_leverage >= 1.0
 
-    def test_ccxt__get_contract_size(self, exchange_futures):
+    def test_ccxt_get_contract_size(self, exchange_futures):
         futures, futures_name = exchange_futures
         if futures:
             futures_pair = EXCHANGES[futures_name].get(
                 'futures_pair',
                 EXCHANGES[futures_name]['pair']
             )
-            contract_size = futures._get_contract_size(futures_pair)
+            contract_size = futures.get_contract_size(futures_pair)
             assert (isinstance(contract_size, float) or isinstance(contract_size, int))
             assert contract_size >= 0.0
 
@@ -464,6 +468,7 @@ class TestCCXTExchange():
                 False,
                 100,
                 100,
+                100,
             )
             assert (isinstance(liquidation_price, float))
             assert liquidation_price >= 0.0
@@ -474,6 +479,7 @@ class TestCCXTExchange():
                 False,
                 100,
                 100,
+                100,
             )
             assert (isinstance(liquidation_price, float))
             assert liquidation_price >= 0.0

tests/exchange/test_exchange.py

@@ -181,11 +181,11 @@ def test_init_ccxt_kwargs(default_conf, mocker, caplog):
     assert log_has("Applying additional ccxt config: {'TestKWARG': 11, 'TestKWARG44': 11}", caplog)
     assert log_has(asynclogmsg, caplog)
     # Test additional headers case
-    Exchange._headers = {'hello': 'world'}
+    Exchange._ccxt_params = {'hello': 'world'}
     ex = Exchange(conf)
 
     assert log_has("Applying additional ccxt config: {'TestKWARG': 11, 'TestKWARG44': 11}", caplog)
-    assert ex._api.headers == {'hello': 'world'}
+    assert ex._api.hello == 'world'
     assert ex._ccxt_config == {}
     Exchange._headers = {}
 
@@ -2352,10 +2352,11 @@ def test_fetch_l2_order_book(default_conf, mocker, order_book_l2, exchange_name)
         order_book = exchange.fetch_l2_order_book(pair='ETH/BTC', limit=val)
         assert api_mock.fetch_l2_order_book.call_args_list[0][0][0] == 'ETH/BTC'
         # Not all exchanges support all limits for orderbook
-        if not exchange._ft_has['l2_limit_range'] or val in exchange._ft_has['l2_limit_range']:
+        if (not exchange.get_option('l2_limit_range')
+                or val in exchange.get_option('l2_limit_range')):
             assert api_mock.fetch_l2_order_book.call_args_list[0][0][1] == val
         else:
-            next_limit = exchange.get_next_limit_in_list(val, exchange._ft_has['l2_limit_range'])
+            next_limit = exchange.get_next_limit_in_list(val, exchange.get_option('l2_limit_range'))
             assert api_mock.fetch_l2_order_book.call_args_list[0][0][1] == next_limit
 
 
@@ -3311,16 +3312,16 @@ def test_merge_ft_has_dict(default_conf, mocker):
 
     ex = Kraken(default_conf)
     assert ex._ft_has != Exchange._ft_has_default
-    assert ex._ft_has['trades_pagination'] == 'id'
-    assert ex._ft_has['trades_pagination_arg'] == 'since'
+    assert ex.get_option('trades_pagination') == 'id'
+    assert ex.get_option('trades_pagination_arg') == 'since'
 
     # Binance defines different values
     ex = Binance(default_conf)
     assert ex._ft_has != Exchange._ft_has_default
-    assert ex._ft_has['stoploss_on_exchange']
-    assert ex._ft_has['order_time_in_force'] == ['gtc', 'fok', 'ioc']
-    assert ex._ft_has['trades_pagination'] == 'id'
-    assert ex._ft_has['trades_pagination_arg'] == 'fromId'
+    assert ex.get_option('stoploss_on_exchange')
+    assert ex.get_option('order_time_in_force') == ['gtc', 'fok', 'ioc']
+    assert ex.get_option('trades_pagination') == 'id'
+    assert ex.get_option('trades_pagination_arg') == 'fromId'
 
     conf = copy.deepcopy(default_conf)
     conf['exchange']['_ft_has_params'] = {"DeadBeef": 20,
@@ -4131,7 +4132,8 @@ def test_get_or_calculate_liquidation_price(mocker, default_conf):
         pair='NEAR/USDT:USDT',
         open_rate=18.884,
         is_short=False,
-        position=0.8,
+        amount=0.8,
+        stake_amount=18.884 * 0.8,
         wallet_balance=0.8,
     )
     assert liq_price == 17.47
@@ -4142,7 +4144,8 @@ def test_get_or_calculate_liquidation_price(mocker, default_conf):
         pair='NEAR/USDT:USDT',
         open_rate=18.884,
         is_short=False,
-        position=0.8,
+        amount=0.8,
+        stake_amount=18.884 * 0.8,
         wallet_balance=0.8,
     )
     assert liq_price == 17.540699999999998
@@ -4287,7 +4290,7 @@ def test__fetch_and_calculate_funding_fees_datetime_called(
     ('XLTCUSDT', 0.01, 'futures'),
     ('ETH/USDT:USDT', 10, 'futures')
 ])
-def test__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode):
+def est__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode):
     api_mock = MagicMock()
     default_conf['trading_mode'] = trading_mode
     default_conf['margin_mode'] = 'isolated'
@@ -4306,7 +4309,7 @@ def test__get_contract_size(mocker, default_conf, pair, expected_size, trading_m
             'contractSize': '10',
         }
     })
-    size = exchange._get_contract_size(pair)
+    size = exchange.get_contract_size(pair)
     assert expected_size == size
 
 
@@ -4542,7 +4545,8 @@ def test_liquidation_price_is_none(
         pair='DOGE/USDT',
         open_rate=open_rate,
         is_short=is_short,
-        position=71200.81144,
+        amount=71200.81144,
+        stake_amount=open_rate * 71200.81144,
         wallet_balance=-56354.57,
         mm_ex_1=0.10,
         upnl_ex_1=0.0
@@ -4551,7 +4555,7 @@ def test_liquidation_price_is_none(
 
 @pytest.mark.parametrize(
     'exchange_name, is_short, trading_mode, margin_mode, wallet_balance, '
-    'mm_ex_1, upnl_ex_1, maintenance_amt, position, open_rate, '
+    'mm_ex_1, upnl_ex_1, maintenance_amt, amount, open_rate, '
     'mm_ratio, expected',
     [
         ("binance", False, 'futures', 'isolated', 1535443.01, 0.0,
@@ -4565,7 +4569,7 @@ def test_liquidation_price_is_none(
     ])
 def test_liquidation_price(
     mocker, default_conf, exchange_name, open_rate, is_short, trading_mode,
-    margin_mode, wallet_balance, mm_ex_1, upnl_ex_1, maintenance_amt, position, mm_ratio, expected
+    margin_mode, wallet_balance, mm_ex_1, upnl_ex_1, maintenance_amt, amount, mm_ratio, expected
 ):
     default_conf['trading_mode'] = trading_mode
     default_conf['margin_mode'] = margin_mode
@@ -4579,7 +4583,8 @@ def test_liquidation_price(
         wallet_balance=wallet_balance,
         mm_ex_1=mm_ex_1,
         upnl_ex_1=upnl_ex_1,
-        position=position,
+        amount=amount,
+        stake_amount=open_rate * amount,
     ), 2), expected)
 
 
@@ -4791,6 +4796,20 @@ def test_load_leverage_tiers(mocker, default_conf, leverage_tiers, exchange_name
     )
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize('exchange_name', EXCHANGES)
+async def test_get_market_leverage_tiers(mocker, default_conf, exchange_name):
+    default_conf['exchange']['name'] = exchange_name
+    await async_ccxt_exception(
+        mocker,
+        default_conf,
+        MagicMock(),
+        "get_market_leverage_tiers",
+        "fetch_market_leverage_tiers",
+        symbol='BTC/USDT:USDT'
+    )
+
+
 def test_parse_leverage_tier(mocker, default_conf):
     exchange = get_patched_exchange(mocker, default_conf)
 
@@ -4811,10 +4830,10 @@ def test_parse_leverage_tier(mocker, default_conf):
     }
 
     assert exchange.parse_leverage_tier(tier) == {
-        "min": 0,
-        "max": 100000,
-        "mmr": 0.025,
-        "lev": 20,
+        "minNotional": 0,
+        "maxNotional": 100000,
+        "maintenanceMarginRate": 0.025,
+        "maxLeverage": 20,
         "maintAmt": 0.0,
     }
 
@@ -4840,10 +4859,10 @@ def test_parse_leverage_tier(mocker, default_conf):
     }
 
     assert exchange.parse_leverage_tier(tier2) == {
-        'min': 0,
-        'max': 2000,
-        'mmr': 0.01,
-        'lev': 75,
+        'minNotional': 0,
+        'maxNotional': 2000,
+        'maintenanceMarginRate': 0.01,
+        'maxLeverage': 75,
         "maintAmt": None,
     }
 
@@ -5096,6 +5115,7 @@ def test_get_liquidation_price(
         pair='ETH/USDT:USDT',
         open_rate=open_rate,
         amount=amount,
+        stake_amount=amount * open_rate / leverage,
         leverage=leverage,
         is_short=is_short,
     )
@@ -5131,7 +5151,7 @@ def test_stoploss_contract_size(mocker, default_conf, contract_size, order_amoun
     mocker.patch('freqtrade.exchange.Exchange.price_to_precision', lambda s, x, y: y)
 
     exchange = get_patched_exchange(mocker, default_conf, api_mock)
-    exchange._get_contract_size = MagicMock(return_value=contract_size)
+    exchange.get_contract_size = MagicMock(return_value=contract_size)
 
     api_mock.create_order.reset_mock()
     order = exchange.stoploss(

tests/exchange/test_okx.py

@@ -1,4 +1,5 @@
 from datetime import datetime, timedelta, timezone
+from pathlib import Path
 from unittest.mock import MagicMock, PropertyMock
 
 import pytest
@@ -6,7 +7,7 @@ import pytest
 from freqtrade.enums import MarginMode, TradingMode
 from freqtrade.enums.candletype import CandleType
 from freqtrade.exchange.exchange import timeframe_to_minutes
-from tests.conftest import get_mock_coro, get_patched_exchange
+from tests.conftest import get_mock_coro, get_patched_exchange, log_has
 from tests.exchange.test_exchange import ccxt_exceptionhandlers
 
 
@@ -267,7 +268,10 @@ def test_additional_exchange_init_okx(default_conf, mocker):
                            "additional_exchange_init", "fetch_accounts")
 
 
-def test_load_leverage_tiers_okx(default_conf, mocker, markets):
+def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog, time_machine):
+
+    default_conf['datadir'] = Path(tmpdir)
+    # fd_mock = mocker.patch('freqtrade.exchange.exchange.file_dump_json')
     api_mock = MagicMock()
     type(api_mock).has = PropertyMock(return_value={
         'fetchLeverageTiers': False,
@@ -410,48 +414,66 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets):
     assert exchange._leverage_tiers == {
         'ADA/USDT:USDT': [
             {
-                'min': 0,
-                'max': 500,
-                'mmr': 0.02,
-                'lev': 75,
+                'minNotional': 0,
+                'maxNotional': 500,
+                'maintenanceMarginRate': 0.02,
+                'maxLeverage': 75,
                 'maintAmt': None
             },
             {
-                'min': 501,
-                'max': 1000,
-                'mmr': 0.025,
-                'lev': 50,
+                'minNotional': 501,
+                'maxNotional': 1000,
+                'maintenanceMarginRate': 0.025,
+                'maxLeverage': 50,
                 'maintAmt': None
             },
             {
-                'min': 1001,
-                'max': 2000,
-                'mmr': 0.03,
-                'lev': 20,
+                'minNotional': 1001,
+                'maxNotional': 2000,
+                'maintenanceMarginRate': 0.03,
+                'maxLeverage': 20,
                 'maintAmt': None
             },
         ],
         'ETH/USDT:USDT': [
             {
-                'min': 0,
-                'max': 2000,
-                'mmr': 0.01,
-                'lev': 75,
+                'minNotional': 0,
+                'maxNotional': 2000,
+                'maintenanceMarginRate': 0.01,
+                'maxLeverage': 75,
                 'maintAmt': None
             },
             {
-                'min': 2001,
-                'max': 4000,
-                'mmr': 0.015,
-                'lev': 50,
+                'minNotional': 2001,
+                'maxNotional': 4000,
+                'maintenanceMarginRate': 0.015,
+                'maxLeverage': 50,
                 'maintAmt': None
             },
             {
-                'min': 4001,
-                'max': 8000,
-                'mmr': 0.02,
-                'lev': 20,
+                'minNotional': 4001,
+                'maxNotional': 8000,
+                'maintenanceMarginRate': 0.02,
+                'maxLeverage': 20,
                 'maintAmt': None
             },
         ],
     }
+    filename = (default_conf['datadir'] /
+                f"futures/leverage_tiers_{default_conf['stake_currency']}.json")
+    assert filename.is_file()
+
+    logmsg = 'Cached leverage tiers are outdated. Will update.'
+    assert not log_has(logmsg, caplog)
+
+    api_mock.fetch_market_leverage_tiers.reset_mock()
+
+    exchange.load_leverage_tiers()
+    assert not log_has(logmsg, caplog)
+
+    api_mock.fetch_market_leverage_tiers.call_count == 0
+    # 2 day passes ...
+    time_machine.move_to(datetime.now() + timedelta(days=2))
+    exchange.load_leverage_tiers()
+
+    assert log_has(logmsg, caplog)

tests/freqai/conftest.py

@@ -1,5 +1,6 @@
 from copy import deepcopy
 from pathlib import Path
+from unittest.mock import MagicMock
 
 import pytest
 
@@ -81,6 +82,51 @@ def get_patched_freqaimodel(mocker, freqaiconf):
     return freqaimodel
 
 
+def make_data_dictionary(mocker, freqai_conf):
+    freqai_conf.update({"timerange": "20180110-20180130"})
+
+    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)
+    freqai.dk.pair = "ADA/BTC"
+    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")
+
+    corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(
+            data_load_timerange, freqai.dk.pair, freqai.dk
+        )
+
+    unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
+                strategy, corr_dataframes, base_dataframes, freqai.dk.pair
+            )
+
+    unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
+
+    freqai.dk.find_features(unfiltered_dataframe)
+
+    features_filtered, labels_filtered = freqai.dk.filter_features(
+            unfiltered_dataframe,
+            freqai.dk.training_features_list,
+            freqai.dk.label_list,
+            training_filter=True,
+        )
+
+    data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
+
+    data_dictionary = freqai.dk.normalize_data(data_dictionary)
+
+    return freqai
+
+
 def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
     strategy = get_patched_freqai_strategy(mocker, freqaiconf)
     exchange = get_patched_exchange(mocker, freqaiconf)

tests/freqai/test_freqai_datakitchen.py

@@ -5,7 +5,8 @@ from pathlib import Path
 import pytest
 
 from freqtrade.exceptions import OperationalException
-from tests.freqai.conftest import get_patched_data_kitchen
+from tests.conftest import log_has_re
+from tests.freqai.conftest import get_patched_data_kitchen, make_data_dictionary
 
 
 @pytest.mark.parametrize(
@@ -66,3 +67,30 @@ def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected):
     dk = get_patched_data_kitchen(mocker, freqai_conf)
     assert dk.check_if_model_expired(timestamp) == expected
     shutil.rmtree(Path(dk.full_path))
+
+
+def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog):
+    freqai = make_data_dictionary(mocker, freqai_conf)
+    # freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1})
+    freqai.dk.use_DBSCAN_to_remove_outliers(predict=False)
+    assert log_has_re(
+        "DBSCAN found eps of 2.42.",
+        caplog,
+    )
+
+
+def test_compute_distances(mocker, freqai_conf):
+    freqai = make_data_dictionary(mocker, freqai_conf)
+    freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 1})
+    avg_mean_dist = freqai.dk.compute_distances()
+    assert round(avg_mean_dist, 2) == 2.56
+
+
+def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog):
+    freqai = make_data_dictionary(mocker, freqai_conf)
+    freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
+    freqai.dk.use_SVM_to_remove_outliers(predict=False)
+    assert log_has_re(
+        "SVM detected 8.46%",
+        caplog,
+    )

tests/optimize/test_backtesting.py

@@ -550,6 +550,7 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None:
     mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
     mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
     mocker.patch("freqtrade.exchange.Exchange.get_max_leverage", return_value=100)
+    mocker.patch("freqtrade.optimize.backtesting.price_to_precision", lambda p, *args: p)
     patch_exchange(mocker)
     default_conf_usdt['stake_amount'] = 300
     default_conf_usdt['max_open_trades'] = 2
@@ -559,13 +560,13 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None:
     default_conf_usdt['exchange']['pair_whitelist'] = ['.*']
     backtesting = Backtesting(default_conf_usdt)
     backtesting._set_strategy(backtesting.strategylist[0])
-    pair = 'UNITTEST/USDT:USDT'
+    pair = 'ETH/USDT:USDT'
     row = [
         pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
-        0.001,  # Open
-        0.0012,  # High
-        0.00099,  # Low
-        0.0011,  # Close
+        0.1,  # Open
+        0.12,  # High
+        0.099,  # Low
+        0.11,  # Close
         1,  # enter_long
         0,  # exit_long
         1,  # enter_short
@@ -580,8 +581,8 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None:
                  return_value=(0.01, 0.01))
 
     # leverage = 5
-    # ep1(trade.open_rate) = 0.001
-    # position(trade.amount) = 1500000
+    # ep1(trade.open_rate) = 0.1
+    # position(trade.amount) = 15000
     # stake_amount = 300 -> wb = 300 / 5 = 60
     # mmr = 0.01
     # cum_b = 0.01
@@ -591,26 +592,26 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None:
     # Binance, Long
     # liquidation_price
     #   = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
-    #   = ((300 + 0.01) - (1 * 1500000 * 0.001)) / ((1500000 * 0.01) - (1 * 1500000))
+    #   = ((300 + 0.01) - (1 * 15000 * 0.1)) / ((15000 * 0.01) - (1 * 15000))
     #   = 0.0008080740740740741
     # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1)
-    #   = 0.0008080740740740741 + ((0.001 - 0.0008080740740740741) * 0.05 * 1)
-    #   = 0.0008176703703703704
+    #   = 0.08080740740740741 + ((0.1 - 0.08080740740740741) * 0.05 * 1)
+    #   = 0.08176703703703704
 
     trade = backtesting._enter_trade(pair, row=row, direction='long')
-    assert pytest.approx(trade.liquidation_price) == 0.00081767037
+    assert pytest.approx(trade.liquidation_price) == 0.081767037
 
     # Binance, Short
     # liquidation_price
     #   = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
-    #   = ((300 + 0.01) - ((-1) * 1500000 * 0.001)) / ((1500000 * 0.01) - ((-1) * 1500000))
+    #   = ((300 + 0.01) - ((-1) * 15000 * 0.1)) / ((15000 * 0.01) - ((-1) * 15000))
     #   = 0.0011881254125412541
     # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1)
-    #   = 0.0011881254125412541 + (abs(0.001 - 0.0011881254125412541) * 0.05 * -1)
-    #   = 0.0011787191419141915
+    #   = 0.11881254125412541 + (abs(0.1 - 0.11881254125412541) * 0.05 * -1)
+    #   = 0.11787191419141915
 
     trade = backtesting._enter_trade(pair, row=row, direction='short')
-    assert pytest.approx(trade.liquidation_price) == 0.0011787191
+    assert pytest.approx(trade.liquidation_price) == 0.11787191
 
     # Stake-amount too high!
     mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=600.0)

tests/optimize/test_backtesting_adjust_position.py

@@ -18,6 +18,8 @@ from tests.conftest import patch_exchange
 def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None:
     default_conf['use_exit_signal'] = False
     mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    mocker.patch('freqtrade.optimize.backtesting.amount_to_contract_precision',
+                 lambda x, *args, **kwargs: round(x, 8))
     mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
     mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
     patch_exchange(mocker)

tests/rpc/test_rpc.py

@@ -663,7 +663,7 @@ def test_rpc_stop(mocker, default_conf) -> None:
     assert freqtradebot.state == State.STOPPED
 
 
-def test_rpc_stopbuy(mocker, default_conf) -> None:
+def test_rpc_stopentry(mocker, default_conf) -> None:
     mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
     mocker.patch.multiple(
         'freqtrade.exchange.Exchange',
@@ -676,8 +676,8 @@ def test_rpc_stopbuy(mocker, default_conf) -> None:
     freqtradebot.state = State.RUNNING
 
     assert freqtradebot.config['max_open_trades'] != 0
-    result = rpc._rpc_stopbuy()
-    assert {'status': 'No more buy will occur from now. Run /reload_config to reset.'} == result
+    result = rpc._rpc_stopentry()
+    assert {'status': 'No more entries will occur from now. Run /reload_config to reset.'} == result
     assert freqtradebot.config['max_open_trades'] == 0
 
 

tests/rpc/test_rpc_apiserver.py

@@ -422,13 +422,20 @@ def test_api_reloadconf(botclient):
     assert ftbot.state == State.RELOAD_CONFIG
 
 
-def test_api_stopbuy(botclient):
+def test_api_stopentry(botclient):
     ftbot, client = botclient
     assert ftbot.config['max_open_trades'] != 0
 
     rc = client_post(client, f"{BASE_URI}/stopbuy")
     assert_response(rc)
-    assert rc.json() == {'status': 'No more buy will occur from now. Run /reload_config to reset.'}
+    assert rc.json() == {
+        'status': 'No more entries will occur from now. Run /reload_config to reset.'}
+    assert ftbot.config['max_open_trades'] == 0
+
+    rc = client_post(client, f"{BASE_URI}/stopentry")
+    assert_response(rc)
+    assert rc.json() == {
+        'status': 'No more entries will occur from now. Run /reload_config to reset.'}
     assert ftbot.config['max_open_trades'] == 0
 
 

tests/rpc/test_rpc_telegram.py

@@ -103,7 +103,8 @@ def test_telegram_init(default_conf, mocker, caplog) -> None:
                    "['stats'], ['daily'], ['weekly'], ['monthly'], "
                    "['count'], ['locks'], ['unlock', 'delete_locks'], "
                    "['reload_config', 'reload_conf'], ['show_config', 'show_conf'], "
-                   "['stopbuy'], ['whitelist'], ['blacklist'], ['blacklist_delete', 'bl_delete'], "
+                   "['stopbuy', 'stopentry'], ['whitelist'], ['blacklist'], "
+                   "['blacklist_delete', 'bl_delete'], "
                    "['logs'], ['edge'], ['health'], ['help'], ['version']"
                    "]")
 
@@ -896,10 +897,10 @@ def test_stopbuy_handle(default_conf, update, mocker) -> None:
     telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
 
     assert freqtradebot.config['max_open_trades'] != 0
-    telegram._stopbuy(update=update, context=MagicMock())
+    telegram._stopentry(update=update, context=MagicMock())
     assert freqtradebot.config['max_open_trades'] == 0
     assert msg_mock.call_count == 1
-    assert 'No more buy will occur from now. Run /reload_config to reset.' \
+    assert 'No more entries will occur from now. Run /reload_config to reset.' \
         in msg_mock.call_args_list[0][0][0]
 
 

tests/strategy/test_interface.py

@@ -12,7 +12,9 @@ from freqtrade.configuration import TimeRange
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.data.history import load_data
 from freqtrade.enums import ExitCheckTuple, ExitType, SignalDirection
+from freqtrade.enums.hyperoptstate import HyperoptState
 from freqtrade.exceptions import OperationalException, StrategyError
+from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer
 from freqtrade.optimize.space import SKDecimal
 from freqtrade.persistence import PairLocks, Trade
 from freqtrade.resolvers import StrategyResolver
@@ -859,7 +861,9 @@ def test_strategy_safe_wrapper_trade_copy(fee):
 
 
 def test_hyperopt_parameters():
+    HyperoptStateContainer.set_state(HyperoptState.INDICATORS)
     from skopt.space import Categorical, Integer, Real
+
     with pytest.raises(OperationalException, match=r"Name is determined.*"):
         IntParameter(low=0, high=5, default=1, name='hello')
 
@@ -937,6 +941,12 @@ def test_hyperopt_parameters():
 
     assert list(boolpar.range) == [True, False]
 
+    HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE)
+    assert len(list(intpar.range)) == 1
+    assert len(list(fltpar.range)) == 1
+    assert len(list(catpar.range)) == 1
+    assert len(list(boolpar.range)) == 1
+
 
 def test_auto_hyperopt_interface(default_conf):
     default_conf.update({'strategy': 'HyperoptableStrategyV2'})

tests/strategy/test_strategy_loading.py

@@ -48,6 +48,10 @@ def test_search_all_strategies_with_failed():
     assert len([x for x in strategies if x['class'] is not None]) == 9
     assert len([x for x in strategies if x['class'] is None]) == 1
 
+    directory = Path(__file__).parent / "strats_nonexistingdir"
+    strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
+    assert len(strategies) == 0
+
 
 def test_load_strategy(default_conf, result):
     default_conf.update({'strategy': 'SampleStrategy',

tests/test_freqtradebot.py

@@ -473,8 +473,6 @@ def test_create_trade_no_signal(default_conf_usdt, fee, mocker) -> None:
     freqtrade = FreqtradeBot(default_conf_usdt)
     patch_get_signal(freqtrade, enter_long=False, exit_long=False)
 
-    Trade.query = MagicMock()
-    Trade.query.filter = MagicMock()
     assert not freqtrade.create_trade('ETH/USDT')
 
 

tests/test_persistence.py

@@ -1689,6 +1689,7 @@ def test_get_open(fee, is_short, use_db):
 
     create_mock_trades(fee, is_short, use_db)
     assert len(Trade.get_open_trades()) == 4
+    assert Trade.get_open_trade_count() == 4
 
     Trade.use_db = True
 
@@ -1701,6 +1702,7 @@ def test_get_open_lev(fee, use_db):
 
     create_mock_trades_with_leverage(fee, use_db)
     assert len(Trade.get_open_trades()) == 5
+    assert Trade.get_open_trade_count() == 5
 
     Trade.use_db = True
 
@@ -2892,8 +2894,8 @@ def test_order_to_ccxt(limit_buy_order_open):
             (('buy', 100, 9), (200.0, 8.5, 1700.0, 0.0, None, None)),
             (('sell', 100, 10), (100.0, 8.5, 850.0, 150.0, 150.0, 0.17647059)),
             (('buy', 150, 11), (250.0, 10, 2500.0, 150.0, 150.0, 0.17647059)),
-            (('sell', 100, 12), (150.0, 10.0, 1500.0, 350.0, 350.0, 0.2)),
-            (('sell', 150, 14), (150.0, 10.0, 1500.0, 950.0, 950.0, 0.40)),
+            (('sell', 100, 12), (150.0, 10.0, 1500.0, 350.0, 200.0, 0.2)),
+            (('sell', 150, 14), (150.0, 10.0, 1500.0, 950.0, 600.0, 0.40)),
         ],
         'end_profit': 950.0,
         'end_profit_ratio': 0.283582,
@@ -2958,9 +2960,8 @@ def test_recalc_trade_from_orders_dca(data) -> None:
         assert trade.amount == result[0]
         assert trade.open_rate == result[1]
         assert trade.stake_amount == result[2]
-        # TODO: enable the below.
         assert pytest.approx(trade.realized_profit) == result[3]
-        # assert pytest.approx(trade.close_profit_abs) == result[4]
+        assert pytest.approx(trade.close_profit_abs) == result[4]
         assert pytest.approx(trade.close_profit) == result[5]
 
     trade.close(price)