Merge branch 'develop' into feat/externalsignals

.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`).

config_examples/config_freqai.example.json

@@ -53,7 +53,6 @@
     ],
     "freqai": {
         "enabled": true,
-        "startup_candles": 10000,
         "purge_old_models": true,
         "train_period_days": 15,
         "backtest_period_days": 7,
@@ -75,9 +74,10 @@
             "weight_factor": 0.9,
             "principal_component_analysis": false,
             "use_SVM_to_remove_outliers": true,
-            "stratify_training_data": 0,
+            "indicator_periods_candles": [
-            "indicator_max_period_candles": 20,
+                10,
-            "indicator_periods_candles": [10, 20]
+                20
+            ]
         },
         "data_split_parameters": {
             "test_size": 0.33,

config_examples/config_full.example.json

@@ -64,8 +64,8 @@
         "stoploss_on_exchange_limit_ratio": 0.99
     },
     "order_time_in_force": {
-        "entry": "gtc",
+        "entry": "GTC",
-        "exit": "gtc"
+        "exit": "GTC"
     },
     "pairlists": [
         {"method": "StaticPairList"},

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/configuration.md

@@ -58,9 +58,20 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
 
 !!! Tip "Use multiple configuration files to keep secrets secret"
     You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.  
+    The 2nd file should only specify what you intend to override.
+    If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
+
+    For one-off commands, you can also use the below syntax by specifying multiple "--config" parameters.
+
+    ``` bash
+    freqtrade trade --config user_data/config1.json --config user_data/config-private.json <...>
+    ```
+
+    The below is equivalent to the example above - but having 2 configuration files in the configuration, for easier reuse.
 
     ``` json title="user_data/config.json"
     "add_config_files": [
+        "config1.json",
         "config-private.json"
     ]
     ```
@@ -69,17 +80,6 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
     freqtrade trade --config user_data/config.json <...>
     ```
 
-    The 2nd file should only specify what you intend to override.
-    If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
-
-    For one-off commands, you can also use the below syntax by specifying multiple "--config" parameters.
-
-    ``` bash
-    freqtrade trade --config user_data/config.json --config user_data/config-private.json <...>
-    ```
-
-    This is equivalent to the example above - but `config-private.json` is specified as cli argument.
-
 ??? Note "config collision handling"
     If the same configuration setting takes place in both `config.json` and `config-import.json`, then the parent configuration wins.
     In the below case, `max_open_trades` would be 3 after the merging - as the reusable "import" configuration has this key overwritten.
@@ -111,6 +111,8 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
     }
     ```
     
+    If multiple files are in the `add_config_files` section, then they will be assumed to be at identical levels, having the last occurrence override the earlier config (unless a parent already defined such a key).
+
 ## Configuration parameters
 
 The table below will list all configuration parameters available.
@@ -525,21 +527,28 @@ It means if the order is not executed immediately AND fully then it is cancelled
 It is the same as FOK (above) except it can be partially fulfilled. The remaining part
 is automatically cancelled by the exchange.
 
-The `order_time_in_force` parameter contains a dict with buy and sell time in force policy values.
+**PO (Post only):**
+
+Post only order. The order is either placed as a maker order, or it is canceled.
+This means the order must be placed on orderbook for at at least time in an unfilled state.
+
+#### time_in_force config
+
+The `order_time_in_force` parameter contains a dict with entry and exit time in force policy values.
 This can be set in the configuration file or in the strategy.
 Values set in the configuration file overwrites values set in the strategy.
 
-The possible values are: `gtc` (default), `fok` or `ioc`.
+The possible values are: `GTC` (default), `FOK` or `IOC`.
 
 ``` python
 "order_time_in_force": {
-    "entry": "gtc",
+    "entry": "GTC",
-    "exit": "gtc"
+    "exit": "GTC"
 },
 ```
 
 !!! Warning
-    This is ongoing work. For now, it is supported only for binance and kucoin.
+    This is ongoing work. For now, it is supported only for binance, gate, ftx and kucoin.
     Please don't change the default value unless you know what you are doing and have researched the impact of using different values for your particular exchange.
 
 ### What values can be used for fiat_display_currency?

docs/data-download.md

@@ -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/developer.md

@@ -409,8 +409,9 @@ Determine if crucial bugfixes have been made between this commit and the current
 
 * Merge the release branch (stable) into this branch.
 * Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
-* Commit this part
+* Commit this part.
-* push that branch to the remote and create a PR against the stable branch
+* push that branch to the remote and create a PR against the stable branch.
+* Update develop version to next version following the pattern `2019.8-dev`.
 
 ### Create changelog from git commits
 

docs/exchanges.md

@@ -61,8 +61,8 @@ Binance supports [time_in_force](configuration.md#understand-order_time_in_force
 
 ### Binance Blacklist
 
-For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
+For Binance, it is suggested to add `"BNB/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `BNB` on the account or unless you're willing to disable using `BNB` for fees.
-Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
+Binance accounts may use `BNB` for fees, and if a trade happens to be on `BNB`, further trades may consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
 
 ### Binance Futures
 
@@ -205,8 +205,8 @@ Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force)
 
 ### Kucoin Blacklists
 
-For Kucoin, please add `"KCS/<STAKE>"` to your blacklist to avoid issues.
+For Kucoin, it is suggested to add `"KCS/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `KCS` on the account or unless you're willing to disable using `KCS` for fees. 
-Accounts having KCS accounts use this to pay for fees - if your first trade happens to be on `KCS`, further trades will consume this position and make the initial KCS trade unsellable as the expected amount is not there anymore.
+Kucoin accounts may use `KCS` for fees, and if a trade happens to be on `KCS`, further trades may consume this position and make the initial `KCS` trade unsellable as the expected amount is not there anymore.
 
 ## Huobi
 
@@ -278,7 +278,7 @@ For example, to test the order type `FOK` with Kraken, and modify candle limit t
 "exchange": {
     "name": "kraken",
     "_ft_has_params": {
-        "order_time_in_force": ["gtc", "fok"],
+        "order_time_in_force": ["GTC", "FOK"],
         "ohlcv_candle_limit": 200
         }
     //...

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.  
+    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.
-    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.
+    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/leverage.md

@@ -13,7 +13,7 @@
     Please only use advanced trading modes when you know how freqtrade (and your strategy) works.
     Also, never risk more than what you can afford to lose.
 
-Please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to v3 strategy that can short and trade futures.
+If you already have an existing strategy, please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to strategy of version 3 which can short and trade futures.
 
 ## Shorting
 
@@ -62,6 +62,13 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
 "margin_mode": "isolated"
 ```
 
+##### Pair namings
+
+Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
+A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
+
+Binance is currently still an exception to this naming scheme, where pairs are named `ETH/USDT` also for futures markets, but will be aligned as soon as CCXT is ready.
+
 ### Margin mode
 
 On top of `trading_mode` - you will also have to configure your `margin_mode`.

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
 mkdocs==1.3.1
-mkdocs-material==8.4.0
+mkdocs-material==8.4.2
 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/strategy-customization.md

@@ -166,7 +166,7 @@ Additional technical libraries can be installed as necessary, or custom indicato
 
 Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
 To account for this, the strategy can be assigned the `startup_candle_count` attribute.
-This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
+This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the `startup_candle_count` does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators.
 
 In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles.
 

docs/strategy_analysis_example.md

@@ -14,7 +14,7 @@ from freqtrade.configuration import Configuration
 
 # Initialize empty configuration object
 config = Configuration.from_files([])
-# Optionally, use existing configuration file
+# Optionally (recommended), use existing configuration file
 # config = Configuration.from_files(["config.json"])
 
 # Define some constants
@@ -22,7 +22,7 @@ config["timeframe"] = "5m"
 # Name of the strategy class
 config["strategy"] = "SampleStrategy"
 # Location of the data
-data_location = Path(config['user_data_dir'], 'data', 'binance')
+data_location = config['datadir']
 # Pair to analyze - Only use one pair here
 pair = "BTC/USDT"
 ```

docs/strategy_migration.md

@@ -332,8 +332,8 @@ After:
 
 ``` python hl_lines="2 3"
     order_time_in_force: Dict = {
-        "entry": "gtc",
+        "entry": "GTC",
-        "exit": "gtc",
+        "exit": "GTC",
     }
 ```
 

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__ = '2022.8.1+pubsub'  # Metadata 1.2 mandates PEP 440 version, but 'develop' is not
+__version__ = '2022.9.dev'
 
 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 import market_is_active, timeframe_to_minutes
-from freqtrade.exchange.exchange import market_is_active
 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)
+    if not config.get('show_timerange'):
-    for pair, timeframe, candle_type in sorted(
+        groupedpair = defaultdict(list)
-        paircombs,
+        for pair, timeframe, candle_type in sorted(
-        key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
+            paircombs,
-    ):
+            key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
-        groupedpair[(pair, candle_type)].append(timeframe)
+        ):
+            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)
+            (pair, timeframe, candle_type,
-            for (pair, candle_type), timeframes in groupedpair.items()
+                start.strftime(DATETIME_PRINT_FORMAT),
-        ],
+                end.strftime(DATETIME_PRINT_FORMAT))
-            headers=("Pair", "Timeframe", "Type"),
+            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/constants.py

@@ -23,7 +23,8 @@ REQUIRED_ORDERTIF = ['entry', 'exit']
 REQUIRED_ORDERTYPES = ['entry', 'exit', 'stoploss', 'stoploss_on_exchange']
 PRICING_SIDES = ['ask', 'bid', 'same', 'other']
 ORDERTYPE_POSSIBILITIES = ['limit', 'market']
-ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
+_ORDERTIF_POSSIBILITIES = ['GTC', 'FOK', 'IOC', 'PO']
+ORDERTIF_POSSIBILITIES = _ORDERTIF_POSSIBILITIES + [t.lower() for t in _ORDERTIF_POSSIBILITIES]
 HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
                          'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',

freqtrade/data/dataprovider.py

@@ -210,9 +210,9 @@ class DataProvider:
             timerange = TimeRange.parse_timerange(None if self._config.get(
                 'timerange') is None else str(self._config.get('timerange')))
             # Move informative start time respecting startup_candle_count
-            timerange.subtract_start(
+            startup_candles = self.get_required_startup(str(timeframe))
-                timeframe_to_seconds(str(timeframe)) * self._config.get('startup_candle_count', 0)
+            tf_seconds = timeframe_to_seconds(str(timeframe))
-            )
+            timerange.subtract_start(tf_seconds * startup_candles)
             self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
                 pair=pair,
                 timeframe=timeframe or self._config['timeframe'],
@@ -224,6 +224,21 @@ class DataProvider:
             )
         return self.__cached_pairs_backtesting[saved_pair].copy()
 
+    def get_required_startup(self, timeframe: str) -> int:
+        freqai_config = self._config.get('freqai', {})
+        if not freqai_config.get('enabled', False):
+            return self._config.get('startup_candle_count', 0)
+        else:
+            startup_candles = self._config.get('startup_candle_count', 0)
+            indicator_periods = freqai_config['feature_parameters']['indicator_periods_candles']
+            # make sure the startupcandles is at least the set maximum indicator periods
+            self._config['startup_candle_count'] = max(startup_candles, max(indicator_periods))
+            tf_seconds = timeframe_to_seconds(timeframe)
+            train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
+            total_candles = int(self._config['startup_candle_count'] + train_candles)
+            logger.info(f'Increasing startup_candle_count for freqai to {total_candles}')
+            return total_candles
+
     def get_pair_dataframe(
         self,
         pair: str,

freqtrade/data/history/hdf5datahandler.py

@@ -7,9 +7,8 @@ import numpy as np
 import pandas as pd
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS,
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
-                                 ListPairsWithTimeframes, TradeList)
+from freqtrade.enums import CandleType
-from freqtrade.enums import CandleType, TradingMode
 
 from .idatahandler import IDataHandler
 
@@ -21,29 +20,6 @@ class HDF5DataHandler(IDataHandler):
 
     _columns = DEFAULT_DATAFRAME_COLUMNS
 
-    @classmethod
-    def ohlcv_get_available_data(
-            cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes:
-        """
-        Returns a list of all pairs with ohlcv data available in this datadir
-        :param datadir: Directory to search for ohlcv files
-        :param trading_mode: trading-mode to be used
-        :return: List of Tuples of (pair, timeframe)
-        """
-        if trading_mode == TradingMode.FUTURES:
-            datadir = datadir.joinpath('futures')
-        _tmp = [
-            re.search(
-                cls._OHLCV_REGEX, p.name
-            ) for p in datadir.glob("*.h5")
-        ]
-        return [
-            (
-                cls.rebuild_pair_from_filename(match[1]),
-                cls.rebuild_timeframe_from_filename(match[2]),
-                CandleType.from_string(match[3])
-            ) for match in _tmp if match and len(match.groups()) > 1]
-
     @classmethod
     def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
         """

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']
+            tf_mark = exchange.get_option('mark_ohlcv_timeframe')
-            fr_candle_type = CandleType.from_string(exchange._ft_has['mark_ohlcv_price'])
+            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
 
@@ -39,15 +39,26 @@ class IDataHandler(ABC):
         raise NotImplementedError()
 
     @classmethod
-    @abstractmethod
     def ohlcv_get_available_data(
             cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes:
         """
         Returns a list of all pairs with ohlcv data available in this datadir
         :param datadir: Directory to search for ohlcv files
         :param trading_mode: trading-mode to be used
-        :return: List of Tuples of (pair, timeframe)
+        :return: List of Tuples of (pair, timeframe, CandleType)
         """
+        if trading_mode == TradingMode.FUTURES:
+            datadir = datadir.joinpath('futures')
+        _tmp = [
+            re.search(
+                cls._OHLCV_REGEX, p.name
+            ) for p in datadir.glob(f"*.{cls._get_file_extension()}")]
+        return [
+            (
+                cls.rebuild_pair_from_filename(match[1]),
+                cls.rebuild_timeframe_from_filename(match[2]),
+                CandleType.from_string(match[3])
+            ) for match in _tmp if match and len(match.groups()) > 1]
 
     @classmethod
     @abstractmethod
@@ -73,6 +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/data/history/jsondatahandler.py

@@ -8,9 +8,9 @@ from pandas import DataFrame, read_json, to_datetime
 
 from freqtrade import misc
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, ListPairsWithTimeframes, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
 from freqtrade.data.converter import trades_dict_to_list
-from freqtrade.enums import CandleType, TradingMode
+from freqtrade.enums import CandleType
 
 from .idatahandler import IDataHandler
 
@@ -23,28 +23,6 @@ class JsonDataHandler(IDataHandler):
     _use_zip = False
     _columns = DEFAULT_DATAFRAME_COLUMNS
 
-    @classmethod
-    def ohlcv_get_available_data(
-            cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes:
-        """
-        Returns a list of all pairs with ohlcv data available in this datadir
-        :param datadir: Directory to search for ohlcv files
-        :param trading_mode: trading-mode to be used
-        :return: List of Tuples of (pair, timeframe)
-        """
-        if trading_mode == 'futures':
-            datadir = datadir.joinpath('futures')
-        _tmp = [
-            re.search(
-                cls._OHLCV_REGEX, p.name
-            ) for p in datadir.glob(f"*.{cls._get_file_extension()}")]
-        return [
-            (
-                cls.rebuild_pair_from_filename(match[1]),
-                cls.rebuild_timeframe_from_filename(match[2]),
-                CandleType.from_string(match[3])
-            ) for match in _tmp if match and len(match.groups()) > 1]
-
     @classmethod
     def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
         """

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, RPCRequestType

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,
+from freqtrade.exchange.exchange import (amount_to_contract_precision, amount_to_contracts,
-                                         date_minus_candles, is_exchange_known_ccxt,
+                                         amount_to_precision, available_exchanges, ccxt_exchanges,
-                                         is_exchange_officially_supported, market_is_active,
+                                         contracts_to_amount, date_minus_candles,
-                                         price_to_precision, timeframe_to_minutes,
+                                         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

@@ -23,8 +23,7 @@ class Binance(Exchange):
     _ft_has: Dict = {
         "stoploss_on_exchange": True,
         "stoploss_order_types": {"limit": "stop_loss_limit"},
-        "order_time_in_force": ['gtc', 'fok', 'ioc'],
+        "order_time_in_force": ['GTC', 'FOK', 'IOC'],
-        "time_in_force_parameter": "timeInForce",
         "ohlcv_candle_limit": 1000,
         "trades_pagination": "id",
         "trades_pagination_arg": "fromId",
@@ -137,23 +136,27 @@ 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 amount: Absolute value of position size incl. leverage (in base currency)
-        :param wallet_balance: (WB)
+        :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 +168,11 @@ 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 +184,9 @@ 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
 
@@ -52,15 +54,15 @@ class Exchange:
     # Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
     _params: Dict = {}
 
-    # Additional headers - added to the ccxt object
+    # Additional parameters - added to the ccxt object
-    _headers: Dict = {}
+    _ccxt_params: Dict = {}
 
     # Dict to specify which options each exchange implements
     # This defines defaults, which can be selectively overridden by subclasses using _ft_has
     # or by specifying them in the configuration.
     _ft_has_default: Dict = {
         "stoploss_on_exchange": False,
-        "order_time_in_force": ["gtc"],
+        "order_time_in_force": ["GTC"],
         "time_in_force_parameter": "timeInForce",
         "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
@@ -240,9 +242,9 @@ class Exchange:
         }
         if ccxt_kwargs:
             logger.info('Applying additional ccxt config: %s', ccxt_kwargs)
-        if self._headers:
+        if self._ccxt_params:
-            # Inject static headers after the above output to not confuse users.
+            # Inject static options after the above output to not confuse users.
-            ccxt_kwargs = deep_merge_dicts({'headers': self._headers}, ccxt_kwargs)
+            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)
+        contract_size = self.get_contract_size(pair)
-        if contract_size and contract_size != 1:
+        return amount_to_contracts(amount, contract_size)
-            return amount / contract_size
-        else:
-            return amount
 
     def _contracts_to_amount(self, pair: str, num_contracts: float) -> float:
 
-        contract_size = self._get_contract_size(pair)
+        contract_size = self.get_contract_size(pair)
-        if contract_size and contract_size != 1:
+        return contracts_to_amount(num_contracts, contract_size)
-            return num_contracts * contract_size
-        else:
-            return num_contracts
 
     def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None:
         if exchange_config.get('sandbox'):
@@ -615,7 +611,7 @@ class Exchange:
         """
         Checks if order time in force configured in strategy/config are supported
         """
-        if any(v not in self._ft_has["order_time_in_force"]
+        if any(v.upper() not in self._ft_has["order_time_in_force"]
                for k, v in order_time_in_force.items()):
             raise OperationalException(
                 f'Time in force policies are not supported for {self.name} yet.')
@@ -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.
@@ -987,12 +989,12 @@ class Exchange:
         ordertype: str,
         leverage: float,
         reduceOnly: bool,
-        time_in_force: str = 'gtc',
+        time_in_force: str = 'GTC',
     ) -> Dict:
         params = self._params.copy()
-        if time_in_force != 'gtc' and ordertype != 'market':
+        if time_in_force != 'GTC' and ordertype != 'market':
             param = self._ft_has.get('time_in_force_parameter', '')
-            params.update({param: time_in_force})
+            params.update({param: time_in_force.upper()})
         if reduceOnly:
             params.update({'reduceOnly': True})
         return params
@@ -1007,7 +1009,7 @@ class Exchange:
         rate: float,
         leverage: float,
         reduceOnly: bool = False,
-        time_in_force: str = 'gtc',
+        time_in_force: str = 'GTC',
     ) -> Dict:
         if self._config['dry_run']:
             dry_order = self.create_dry_run_order(
@@ -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.
+                tiers_cached = self.load_cached_leverage_tiers(self._config['stake_currency'])
-                logger.info(
+                if tiers_cached:
-                    f"Initializing leverage_tiers for {len(symbols)} markets. "
+                    tiers = tiers_cached
-                    "This will take about a minute.")
 
-                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'],
+            'minNotional': tier['minNotional'],
-            'max': tier['maxNotional'],
+            'maxNotional': tier['maxNotional'],
-            'mmr': tier['maintenanceMarginRate'],
+            'maintenanceMarginRate': tier['maintenanceMarginRate'],
-            'lev': tier['maxLeverage'],
+            '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'
@@ -2394,35 +2432,6 @@ class Exchange:
         """
         return 0.0
 
-    def get_liquidation_price(
-            self,
-            pair: str,
-            open_rate: float,
-            amount: float,  # quote currency, includes leverage
-            leverage: float,
-            is_short: bool
-    ) -> Optional[float]:
-
-        if self.trading_mode in TradingMode.SPOT:
-            return None
-        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,
-                mm_ex_1=0.0,
-                upnl_ex_1=0.0,
-            )
-            return isolated_liq
-        else:
-            raise OperationalException(
-                "Freqtrade currently only supports futures for leverage trading.")
-
     def funding_fee_cutoff(self, open_date: datetime):
         """
         :param open_date: The open date for a trade
@@ -2583,34 +2592,36 @@ class Exchange:
         else:
             return 0.0
 
-    def get_or_calculate_liquidation_price(
+    def get_liquidation_price(
         self,
         pair: str,
         # 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
-        wallet_balance: float,  # Or margin balance
+        stake_amount: float,
+        wallet_balance: float,
         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
         elif (self.trading_mode != TradingMode.FUTURES):
             raise OperationalException(
-                f"{self.name} does not support {self.margin_mode.value} {self.trading_mode.value}")
+                f"{self.name} does not support {self.margin_mode} {self.trading_mode}")
 
+        isolated_liq = None
         if self._config['dry_run'] or not self.exchange_has("fetchPositions"):
 
             isolated_liq = self.dry_run_liquidation_price(
                 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
@@ -2620,8 +2631,6 @@ class Exchange:
             if len(positions) > 0:
                 pos = positions[0]
                 isolated_liq = pos['liquidationPrice']
-            else:
-                return None
 
         if isolated_liq:
             buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer
@@ -2639,22 +2648,24 @@ 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 +2679,7 @@ 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 +2687,7 @@ 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 +2723,8 @@ class Exchange:
             pair_tiers = self._leverage_tiers[pair]
 
             for tier in reversed(pair_tiers):
-                if nominal_value >= tier['min']:
+                if nominal_value >= tier['minNotional']:
-                    return (tier['mmr'], tier['maintAmt'])
+                    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 +2866,33 @@ 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 +2917,29 @@ 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/ftx.py

@@ -19,6 +19,7 @@ logger = logging.getLogger(__name__)
 class Ftx(Exchange):
 
     _ft_has: Dict = {
+        "order_time_in_force": ['GTC', 'IOC', 'PO'],
         "stoploss_on_exchange": True,
         "ohlcv_candle_limit": 1500,
         "ohlcv_require_since": True,

freqtrade/exchange/gateio.py

@@ -25,16 +25,13 @@ class Gateio(Exchange):
 
     _ft_has: Dict = {
         "ohlcv_candle_limit": 1000,
-        "ohlcv_volume_currency": "quote",
+        "order_time_in_force": ['GTC', 'IOC'],
-        "time_in_force_parameter": "timeInForce",
-        "order_time_in_force": ['gtc', 'ioc'],
         "stoploss_order_types": {"limit": "limit"},
         "stoploss_on_exchange": True,
     }
 
     _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'],
     }
@@ -59,7 +56,7 @@ class Gateio(Exchange):
             ordertype: str,
             leverage: float,
             reduceOnly: bool,
-            time_in_force: str = 'gtc',
+            time_in_force: str = 'GTC',
             ) -> Dict:
         params = super()._get_params(
             side=side,
@@ -71,7 +68,7 @@ class Gateio(Exchange):
         if ordertype == 'market' and self.trading_mode == TradingMode.FUTURES:
             params['type'] = 'market'
             param = self._ft_has.get('time_in_force_parameter', '')
-            params.update({param: 'ioc'})
+            params.update({param: 'IOC'})
         return params
 
     def get_trades_for_order(self, order_id: str, pair: str, since: datetime,

freqtrade/exchange/kraken.py

@@ -171,7 +171,7 @@ class Kraken(Exchange):
         ordertype: str,
         leverage: float,
         reduceOnly: bool,
-        time_in_force: str = 'gtc'
+        time_in_force: str = 'GTC'
     ) -> Dict:
         params = super()._get_params(
             side=side,

freqtrade/exchange/kucoin.py

@@ -23,8 +23,7 @@ class Kucoin(Exchange):
         "stoploss_order_types": {"limit": "limit", "market": "market"},
         "l2_limit_range": [20, 100],
         "l2_limit_range_required": False,
-        "order_time_in_force": ['gtc', 'fok', 'ioc'],
+        "order_time_in_force": ['GTC', 'FOK', 'IOC'],
-        "time_in_force_parameter": "timeInForce",
         "ohlcv_candle_limit": 1500,
     }
 

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:
         """
@@ -96,7 +98,7 @@ class Okx(Exchange):
         ordertype: str,
         leverage: float,
         reduceOnly: bool,
-        time_in_force: str = 'gtc',
+        time_in_force: str = 'GTC',
     ) -> Dict:
         params = super()._get_params(
             side=side,
@@ -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

@@ -76,6 +76,8 @@ class FreqaiDataDrawer:
             self.full_path / f"follower_dictionary-{self.follower_name}.json"
         )
         self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
+        self.historic_predictions_bkp_path = Path(
+            self.full_path / "historic_predictions.backup.pkl")
         self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
         self.follow_mode = follow_mode
         if follow_mode:
@@ -118,13 +120,21 @@ class FreqaiDataDrawer:
         """
         exists = self.historic_predictions_path.is_file()
         if exists:
-            with open(self.historic_predictions_path, "rb") as fp:
+            try:
-                self.historic_predictions = cloudpickle.load(fp)
+                with open(self.historic_predictions_path, "rb") as fp:
-            logger.info(
+                    self.historic_predictions = cloudpickle.load(fp)
-                f"Found existing historic predictions at {self.full_path}, but beware "
+                logger.info(
-                "that statistics may be inaccurate if the bot has been offline for "
+                    f"Found existing historic predictions at {self.full_path}, but beware "
-                "an extended period of time."
+                    "that statistics may be inaccurate if the bot has been offline for "
-            )
+                    "an extended period of time."
+                )
+            except EOFError:
+                logger.warning(
+                    'Historical prediction file was corrupted. Trying to load backup file.')
+                with open(self.historic_predictions_bkp_path, "rb") as fp:
+                    self.historic_predictions = cloudpickle.load(fp)
+                logger.warning('FreqAI successfully loaded the backup historical predictions file.')
+
         elif not self.follow_mode:
             logger.info("Could not find existing historic_predictions, starting from scratch")
         else:
@@ -142,6 +152,9 @@ class FreqaiDataDrawer:
         with open(self.historic_predictions_path, "wb") as fp:
             cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
 
+        # create a backup
+        shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
+
     def save_drawer_to_disk(self):
         """
         Save data drawer full of all pair model metadata in present model folder.
@@ -421,7 +434,7 @@ 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 +473,8 @@ 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:
+        if dk.live and coin in self.model_dictionary:
-            model = self.model_dictionary[dk.model_filename]
+            model = self.model_dictionary[coin]
         elif not dk.keras:
             model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
         else:
@@ -566,7 +579,6 @@ 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

@@ -16,8 +16,6 @@ from sklearn.model_selection import train_test_split
 from sklearn.neighbors import NearestNeighbors
 
 from freqtrade.configuration import TimeRange
-from freqtrade.data.dataprovider import DataProvider
-from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.strategy.interface import IStrategy
@@ -71,6 +69,8 @@ class FreqaiDataKitchen:
         self.label_list: List = []
         self.training_features_list: List = []
         self.model_filename: str = ""
+        self.backtesting_results_path = Path()
+        self.backtest_predictions_folder: str = "backtesting_predictions"
         self.live = live
         self.pair = pair
 
@@ -168,9 +168,17 @@ class FreqaiDataKitchen:
             train_labels = labels
             train_weights = weights
 
-        return self.build_data_dictionary(
+        # Simplest way to reverse the order of training and test data:
-            train_features, test_features, train_labels, test_labels, train_weights, test_weights
+        if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False):
-        )
+            return self.build_data_dictionary(
+                test_features, train_features, test_labels,
+                train_labels, test_weights, train_weights
+                )
+        else:
+            return self.build_data_dictionary(
+                train_features, test_features, train_labels,
+                test_labels, train_weights, test_weights
+            )
 
     def filter_features(
         self,
@@ -281,6 +289,7 @@ class FreqaiDataKitchen:
         :returns:
         :data_dictionary: updated dictionary with standardized values.
         """
+
         # standardize the data by training stats
         train_max = data_dictionary["train_features"].max()
         train_min = data_dictionary["train_features"].min()
@@ -314,10 +323,24 @@ class FreqaiDataKitchen:
                     - 1
                 )
 
-            self.data[f"{item}_max"] = train_labels_max  # .to_dict()
+            self.data[f"{item}_max"] = train_labels_max
-            self.data[f"{item}_min"] = train_labels_min  # .to_dict()
+            self.data[f"{item}_min"] = train_labels_min
         return data_dictionary
 
+    def normalize_single_dataframe(self, df: DataFrame) -> DataFrame:
+
+        train_max = df.max()
+        train_min = df.min()
+        df = (
+            2 * (df - train_min) / (train_max - train_min) - 1
+        )
+
+        for item in train_max.keys():
+            self.data[item + "_max"] = train_max[item]
+            self.data[item + "_min"] = train_min[item]
+
+        return df
+
     def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame:
         """
         Normalize a set of data using the mean and standard deviation from
@@ -431,7 +454,8 @@ class FreqaiDataKitchen:
         start = datetime.datetime.fromtimestamp(timerange.startts, tz=datetime.timezone.utc)
         stop = datetime.datetime.fromtimestamp(timerange.stopts, tz=datetime.timezone.utc)
         df = df.loc[df["date"] >= start, :]
-        df = df.loc[df["date"] <= stop, :]
+        if not self.live:
+            df = df.loc[df["date"] < stop, :]
 
         return df
 
@@ -444,23 +468,23 @@ class FreqaiDataKitchen:
 
         from sklearn.decomposition import PCA  # avoid importing if we dont need it
 
-        n_components = self.data_dictionary["train_features"].shape[1]
+        pca = PCA(0.999)
-        pca = PCA(n_components=n_components)
         pca = pca.fit(self.data_dictionary["train_features"])
-        n_keep_components = np.argmin(pca.explained_variance_ratio_.cumsum() < 0.999)
+        n_keep_components = pca.n_components_
-        pca2 = PCA(n_components=n_keep_components)
         self.data["n_kept_components"] = n_keep_components
-        pca2 = pca2.fit(self.data_dictionary["train_features"])
+        n_components = self.data_dictionary["train_features"].shape[1]
         logger.info("reduced feature dimension by %s", n_components - n_keep_components)
-        logger.info("explained variance %f", np.sum(pca2.explained_variance_ratio_))
+        logger.info("explained variance %f", np.sum(pca.explained_variance_ratio_))
-        train_components = pca2.transform(self.data_dictionary["train_features"])
-        test_components = pca2.transform(self.data_dictionary["test_features"])
 
+        train_components = pca.transform(self.data_dictionary["train_features"])
         self.data_dictionary["train_features"] = pd.DataFrame(
             data=train_components,
             columns=["PC" + str(i) for i in range(0, n_keep_components)],
             index=self.data_dictionary["train_features"].index,
         )
+        # normalsing transformed training features
+        self.data_dictionary["train_features"] = self.normalize_single_dataframe(
+            self.data_dictionary["train_features"])
 
         # keeping a copy of the non-transformed features so we can check for errors during
         # model load from disk
@@ -468,14 +492,18 @@ 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 = pca.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)],
                 index=self.data_dictionary["test_features"].index,
             )
+            # normalise transformed test feature to transformed training features
+            self.data_dictionary["test_features"] = self.normalize_data_from_metadata(
+                self.data_dictionary["test_features"])
 
         self.data["n_kept_components"] = n_keep_components
-        self.pca = pca2
+        self.pca = pca
 
         logger.info(f"PCA reduced total features from  {n_components} to {n_keep_components}")
 
@@ -496,6 +524,9 @@ class FreqaiDataKitchen:
             columns=["PC" + str(i) for i in range(0, self.data["n_kept_components"])],
             index=filtered_dataframe.index,
         )
+        # normalise transformed predictions to transformed training features
+        self.data_dictionary["prediction_features"] = self.normalize_data_from_metadata(
+            self.data_dictionary["prediction_features"])
 
     def compute_distances(self) -> float:
         """
@@ -506,10 +537,25 @@ 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:
+            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 +593,17 @@ 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."
+                )
+                self.svm_model = None
+                return
+
             self.data_dictionary["train_features"] = self.data_dictionary["train_features"][
                 (y_pred == 1)
             ]
@@ -560,7 +615,7 @@ 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 +624,7 @@ 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 +635,7 @@ 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."
             )
 
@@ -598,7 +653,11 @@ class FreqaiDataKitchen:
         is an outlier.
         """
 
+        from math import cos, sin
+
         if predict:
+            if not self.data['DBSCAN_eps']:
+                return
             train_ft_df = self.data_dictionary['train_features']
             pred_ft_df = self.data_dictionary['prediction_features']
             num_preds = len(pred_ft_df)
@@ -616,28 +675,61 @@ class FreqaiDataKitchen:
 
         else:
 
-            MinPts = len(self.data_dictionary['train_features'].columns) * 2
+            def normalise_distances(distances):
-            # measure pairwise distances to train_features.shape[1]*2 nearest neighbours
+                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)
+            distances = np.sort(distances, axis=0).mean(axis=1)
-            index_ten_pct = int(len(distances[:, 1]) * 0.1)
+
-            distances = distances[index_ten_pct:, 1]
+            normalised_distances = normalise_distances(distances)
-            epsilon = distances[-1]
+            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."
+                )
+                self.data['DBSCAN_eps'] = 0
+                return
+
             self.data_dictionary['train_features'] = self.data_dictionary['train_features'][
                 (clustering.labels_ != -1)
             ]
@@ -695,8 +787,8 @@ class FreqaiDataKitchen:
 
         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"
+                "being too far from training data."
             )
 
         self.do_predict += do_predict
@@ -711,9 +803,10 @@ class FreqaiDataKitchen:
         weights = np.exp(-np.arange(num_weights) / (wfactor * num_weights))[::-1]
         return weights
 
-    def append_predictions(self, predictions: DataFrame, do_predict: npt.ArrayLike) -> None:
+    def get_predictions_to_append(self, predictions: DataFrame,
+                                  do_predict: npt.ArrayLike) -> DataFrame:
         """
-        Append backtest prediction from current backtest period to all previous periods
+        Get backtest prediction from current backtest period
         """
 
         append_df = DataFrame()
@@ -728,13 +821,18 @@ class FreqaiDataKitchen:
         if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
             append_df["DI_values"] = self.DI_values
 
+        return append_df
+
+    def append_predictions(self, append_df: DataFrame) -> None:
+        """
+        Append backtest prediction from current backtest period to all previous periods
+        """
+
         if self.full_df.empty:
             self.full_df = append_df
         else:
             self.full_df = pd.concat([self.full_df, append_df], axis=0)
 
-        return
-
     def fill_predictions(self, dataframe):
         """
         Back fill values to before the backtesting range so that the dataframe matches size
@@ -834,9 +932,7 @@ class FreqaiDataKitchen:
         # We notice that users like to use exotic indicators where
         # they do not know the required timeperiod. Here we include a factor
         # of safety by multiplying the user considered "max" by 2.
-        max_period = self.freqai_config["feature_parameters"].get(
+        max_period = self.config.get('startup_candle_count', 20) * 2
-            "indicator_max_period_candles", 20
-        ) * 2
         additional_seconds = max_period * max_tf_seconds
 
         if trained_timestamp != 0:
@@ -870,13 +966,6 @@ 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):
@@ -889,31 +978,6 @@ class FreqaiDataKitchen:
 
         self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
 
-    def download_all_data_for_training(self, timerange: TimeRange, dp: DataProvider) -> None:
-        """
-        Called only once upon start of bot to download the necessary data for
-        populating indicators and training the model.
-        :param timerange: TimeRange = The full data timerange for populating the indicators
-                                      and training the model.
-        :param dp: DataProvider instance attached to the strategy
-        """
-        new_pairs_days = int((timerange.stopts - timerange.startts) / SECONDS_IN_DAY)
-        if not dp._exchange:
-            # Not realistic - this is only called in live mode.
-            raise OperationalException("Dataprovider did not have an exchange attached.")
-        refresh_backtest_ohlcv_data(
-            dp._exchange,
-            pairs=self.all_pairs,
-            timeframes=self.freqai_config["feature_parameters"].get("include_timeframes"),
-            datadir=self.config["datadir"],
-            timerange=timerange,
-            new_pairs_days=new_pairs_days,
-            erase=False,
-            data_format=self.config.get("dataformat_ohlcv", "json"),
-            trading_mode=self.config.get("trading_mode", "spot"),
-            prepend=self.config.get("prepend_data", False),
-        )
-
     def set_all_pairs(self) -> None:
 
         self.all_pairs = copy.deepcopy(
@@ -1027,3 +1091,50 @@ class FreqaiDataKitchen:
         if self.unique_classes:
             for label in self.unique_classes:
                 self.unique_class_list += list(self.unique_classes[label])
+
+    def save_backtesting_prediction(
+        self, append_df: DataFrame
+    ) -> None:
+
+        """
+        Save prediction dataframe from backtesting to h5 file format
+        :param append_df: dataframe for backtesting period
+        """
+        full_predictions_folder = Path(self.full_path / self.backtest_predictions_folder)
+        if not full_predictions_folder.is_dir():
+            full_predictions_folder.mkdir(parents=True, exist_ok=True)
+
+        append_df.to_hdf(self.backtesting_results_path, key='append_df', mode='w')
+
+    def get_backtesting_prediction(
+        self
+    ) -> DataFrame:
+
+        """
+        Get prediction dataframe from h5 file format
+        """
+        append_df = pd.read_hdf(self.backtesting_results_path)
+        return append_df
+
+    def check_if_backtest_prediction_exists(
+        self
+    ) -> bool:
+        """
+        Check if a backtesting prediction already exists
+        :param dk: FreqaiDataKitchen
+        :return:
+        :boolean: whether the prediction file exists or not.
+        """
+        path_to_predictionfile = Path(self.full_path /
+                                      self.backtest_predictions_folder /
+                                      f"{self.model_filename}_prediction.h5")
+        self.backtesting_results_path = path_to_predictionfile
+
+        file_exists = path_to_predictionfile.is_file()
+        if file_exists:
+            logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
+        else:
+            logger.info(
+                f"Could not find backtesting prediction file at {path_to_predictionfile}"
+            )
+        return file_exists

freqtrade/freqai/freqai_interface.py

@@ -7,7 +7,7 @@ import time
 from abc import ABC, abstractmethod
 from pathlib import Path
 from threading import Lock
-from typing import Any, Dict, Tuple
+from typing import Any, Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -27,13 +27,6 @@ pd.options.mode.chained_assignment = None
 logger = logging.getLogger(__name__)
 
 
-def threaded(fn):
-    def wrapper(*args, **kwargs):
-        threading.Thread(target=fn, args=args, kwargs=kwargs).start()
-
-    return wrapper
-
-
 class IFreqaiModel(ABC):
     """
     Class containing all tools for training and prediction in the strategy.
@@ -71,6 +64,9 @@ class IFreqaiModel(ABC):
         self.first = True
         self.set_full_path()
         self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
+        self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", False)
+        if self.save_backtest_models:
+            logger.info('Backtesting module configured to save all models.')
         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
@@ -80,14 +76,20 @@ class IFreqaiModel(ABC):
             logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
         self.CONV_WIDTH = self.freqai_info.get("conv_width", 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'])
 
+        self._threads: List[threading.Thread] = []
+        self._stop_event = threading.Event()
+
     def assert_config(self, config: Dict[str, Any]) -> None:
 
         if not config.get("freqai", {}):
@@ -121,27 +123,54 @@ class IFreqaiModel(ABC):
         elif not self.follow_mode:
             self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
             logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
-            with self.analysis_lock:
+            dataframe = self.dk.use_strategy_to_populate_indicators(
-                dataframe = self.dk.use_strategy_to_populate_indicators(
+                strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
-                    strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
+            )
-                )
             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
 
-    @threaded
+    def clean_up(self):
-    def start_scanning(self, strategy: IStrategy) -> None:
+        """
+        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
+
+    def shutdown(self):
+        """
+        Cleans up threads on Shutdown, set stop event. Join threads to wait
+        for current training iteration.
+        """
+        logger.info("Stopping FreqAI")
+        self._stop_event.set()
+
+        logger.info("Waiting on Training iteration")
+        for _thread in self._threads:
+            _thread.join()
+
+    def start_scanning(self, *args, **kwargs) -> None:
+        """
+        Start `self._start_scanning` in a separate thread
+        """
+        _thread = threading.Thread(target=self._start_scanning, args=args, kwargs=kwargs)
+        self._threads.append(_thread)
+        _thread.start()
+
+    def _start_scanning(self, strategy: IStrategy) -> None:
         """
         Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
         to improve model youth. This function is agnostic to data preparation/collection/storage,
         it simply trains on what ever data is available in the self.dd.
         :param strategy: IStrategy = The user defined strategy class
         """
-        while 1:
+        while not self._stop_event.is_set():
             time.sleep(1)
             for pair in self.config.get("exchange", {}).get("pair_whitelist"):
 
@@ -159,9 +188,11 @@ 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()
 
@@ -210,28 +241,39 @@ class IFreqaiModel(ABC):
                 "trains"
             )
 
+            trained_timestamp_int = int(trained_timestamp.stopts)
             dk.data_path = Path(
                 dk.full_path
                 /
-                f"sub-train-{metadata['pair'].split('/')[0]}_{int(trained_timestamp.stopts)}"
+                f"sub-train-{metadata['pair'].split('/')[0]}_{trained_timestamp_int}"
                 )
-            if not self.model_exists(
+
-                metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
+            dk.set_new_model_names(metadata["pair"], trained_timestamp)
-            ):
+
-                dk.find_features(dataframe_train)
+            if dk.check_if_backtest_prediction_exists():
-                self.model = self.train(dataframe_train, metadata["pair"], dk)
+                append_df = dk.get_backtesting_prediction()
-                self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
+                dk.append_predictions(append_df)
-                    trained_timestamp.stopts)
-                dk.set_new_model_names(metadata["pair"], trained_timestamp)
-                self.dd.save_data(self.model, metadata["pair"], dk)
             else:
-                self.model = self.dd.load_data(metadata["pair"], dk)
+                if not self.model_exists(
+                    metadata["pair"], dk, trained_timestamp=trained_timestamp_int
+                ):
+                    dk.find_features(dataframe_train)
+                    self.model = self.train(dataframe_train, metadata["pair"], dk)
+                    self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
+                        trained_timestamp.stopts)
 
-            self.check_if_feature_list_matches_strategy(dataframe_train, dk)
+                    if self.save_backtest_models:
+                        logger.info('Saving backtest model to disk.')
+                        self.dd.save_data(self.model, metadata["pair"], dk)
+                else:
+                    self.model = self.dd.load_data(metadata["pair"], dk)
 
-            pred_df, do_preds = self.predict(dataframe_backtest, dk)
+                self.check_if_feature_list_matches_strategy(dataframe_train, dk)
 
-            dk.append_predictions(pred_df, do_preds)
+                pred_df, do_preds = self.predict(dataframe_backtest, dk)
+                append_df = dk.get_predictions_to_append(pred_df, do_preds)
+                dk.append_predictions(append_df)
+                dk.save_backtesting_prediction(append_df)
 
         dk.fill_predictions(dataframe)
 
@@ -276,14 +318,8 @@ class IFreqaiModel(ABC):
             )
             dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
 
-            # download candle history if it is not already in memory
+            # load candle history into memory if it is not yet.
             if not self.dd.historic_data:
-                logger.info(
-                    "Downloading all training data for all pairs in whitelist and "
-                    "corr_pairlist, this may take a while if you do not have the "
-                    "data saved"
-                )
-                dk.download_all_data_for_training(data_load_timerange, strategy.dp)
                 self.dd.load_all_pair_histories(data_load_timerange, dk)
 
             if not self.scanning:
@@ -448,11 +484,6 @@ class IFreqaiModel(ABC):
         :return:
         :boolean: whether the model file exists or not.
         """
-        coin, _ = pair.split("/")
-
-        if not self.live:
-            dk.model_filename = model_filename = f"cb_{coin.lower()}_{trained_timestamp}"
-
         path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
         file_exists = path_to_modelfile.is_file()
         if file_exists and not scanning:
@@ -480,8 +511,7 @@ class IFreqaiModel(ABC):
         data_load_timerange: TimeRange,
     ):
         """
-        Retrieve data and train model in single threaded mode (only used if model directory is empty
+        Retrieve data and train model.
-        upon startup for dry/live )
         :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
@@ -606,12 +636,30 @@ class IFreqaiModel(ABC):
                 logger.info(
                     f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
                 if self.inference_time > 0.25 * self.base_tf_seconds:
-                    logger.warning('Inference took over 25/% of the candle time. Reduce pairlist to'
+                    logger.warning("Inference took over 25% of the candle time. Reduce pairlist to"
-                                   ' avoid blinding open trades and degrading performance.')
+                                   " avoid blinding open trades and degrading performance.")
                 self.pair_it = 0
                 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/freqai/utils.py

@@ -0,0 +1,134 @@
+import logging
+from datetime import datetime, timezone
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.exchange.exchange import market_is_active
+from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
+
+
+logger = logging.getLogger(__name__)
+
+
+def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
+    """
+    Called only once upon start of bot to download the necessary data for
+    populating indicators and training the model.
+    :param timerange: TimeRange = The full data timerange for populating the indicators
+                                    and training the model.
+    :param dp: DataProvider instance attached to the strategy
+    """
+
+    if dp._exchange is None:
+        raise OperationalException('No exchange object found.')
+    markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
+               or config.get('include_inactive')]
+
+    all_pairs = dynamic_expand_pairlist(config, markets)
+
+    timerange = get_required_data_timerange(config)
+
+    new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
+
+    refresh_backtest_ohlcv_data(
+        dp._exchange,
+        pairs=all_pairs,
+        timeframes=config["freqai"]["feature_parameters"].get("include_timeframes"),
+        datadir=config["datadir"],
+        timerange=timerange,
+        new_pairs_days=new_pairs_days,
+        erase=False,
+        data_format=config.get("dataformat_ohlcv", "json"),
+        trading_mode=config.get("trading_mode", "spot"),
+        prepend=config.get("prepend_data", False),
+    )
+
+
+def get_required_data_timerange(
+    config: dict
+) -> TimeRange:
+    """
+    Used to compute the required data download time range
+    for auto data-download in FreqAI
+    """
+    time = datetime.now(tz=timezone.utc).timestamp()
+
+    timeframes = config["freqai"]["feature_parameters"].get("include_timeframes")
+
+    max_tf_seconds = 0
+    for tf in timeframes:
+        secs = timeframe_to_seconds(tf)
+        if secs > max_tf_seconds:
+            max_tf_seconds = secs
+
+    startup_candles = config.get('startup_candle_count', 0)
+    indicator_periods = config["freqai"]["feature_parameters"]["indicator_periods_candles"]
+
+    # factor the max_period as a factor of safety.
+    max_period = int(max(startup_candles, max(indicator_periods)) * 1.5)
+    config['startup_candle_count'] = max_period
+    logger.info(f'FreqAI auto-downloader using {max_period} startup candles.')
+
+    additional_seconds = max_period * max_tf_seconds
+
+    startts = int(
+        time
+        - config["freqai"].get("train_period_days", 0) * 86400
+        - additional_seconds
+    )
+    stopts = int(time)
+    data_load_timerange = TimeRange('date', 'date', startts, stopts)
+
+    return data_load_timerange
+
+
+# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
+# def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
+#     """
+#     Called only once upon start of bot to download the necessary data for
+#     populating indicators and training a FreqAI model.
+#     :param timerange: TimeRange = The full data timerange for populating the indicators
+#                                     and training the model.
+#     :param dp: DataProvider instance attached to the strategy
+#     """
+
+#     if dp._exchange is not None:
+#         markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
+#                    or config.get('include_inactive')]
+#     else:
+#         # This should not occur:
+#         raise OperationalException('No exchange object found.')
+
+#     all_pairs = dynamic_expand_pairlist(config, markets)
+
+#     if not dp._exchange:
+#         # Not realistic - this is only called in live mode.
+#         raise OperationalException("Dataprovider did not have an exchange attached.")
+
+#     time = datetime.now(tz=timezone.utc).timestamp()
+
+#     for tf in config["freqai"]["feature_parameters"].get("include_timeframes"):
+#         timerange = TimeRange()
+#         timerange.startts = int(time)
+#         timerange.stopts = int(time)
+#         startup_candles = dp.get_required_startup(str(tf))
+#         tf_seconds = timeframe_to_seconds(str(tf))
+#         timerange.subtract_start(tf_seconds * startup_candles)
+#         new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
+#         # FIXME: now that we are looping on `refresh_backtest_ohlcv_data`, the function
+#         # redownloads the funding rate for each pair.
+#         refresh_backtest_ohlcv_data(
+#             dp._exchange,
+#             pairs=all_pairs,
+#             timeframes=[tf],
+#             datadir=config["datadir"],
+#             timerange=timerange,
+#             new_pairs_days=new_pairs_days,
+#             erase=False,
+#             data_format=config.get("dataformat_ohlcv", "json"),
+#             trading_mode=config.get("trading_mode", "spot"),
+#             prepend=config.get("prepend_data", False),
+#         )

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 import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
-from freqtrade.exchange.exchange import timeframe_to_next_date
 from freqtrade.misc import safe_value_fallback, safe_value_fallback2
 from freqtrade.mixins import LoggingMixin
 from freqtrade.persistence import Order, PairLocks, Trade, init_db
@@ -154,6 +153,8 @@ class FreqtradeBot(LoggingMixin):
 
         self.check_for_open_trades()
 
+        self.strategy.ft_bot_cleanup()
+
         self.rpc.cleanup()
         if self.emc:
             self.emc.shutdown()
@@ -247,7 +248,7 @@ 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 ''}",
             }
@@ -284,7 +285,7 @@ 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):
@@ -303,13 +304,14 @@ 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):
@@ -768,6 +770,7 @@ 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
@@ -1564,9 +1567,10 @@ class FreqtradeBot(LoggingMixin):
         trade.close_rate_requested = limit
         trade.exit_reason = exit_reason
 
-        # Lock pair for one candle to prevent immediate re-trading
+        if not sub_trade_amt:
-        self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
+            # Lock pair for one candle to prevent immediate re-trading
-                                reason='Auto lock')
+            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
@@ -1743,11 +1747,12 @@ class FreqtradeBot(LoggingMixin):
                 # TODO: Margin will need to use interest_rate as well.
                 # interest_rate = self.exchange.get_interest_rate()
                 trade.set_liquidation_price(self.exchange.get_liquidation_price(
-                    leverage=trade.leverage,
                     pair=trade.pair,
-                    amount=trade.amount,
                     open_rate=trade.open_rate,
-                    is_short=trade.is_short
+                    is_short=trade.is_short,
+                    amount=trade.amount,
+                    stake_amount=trade.stake_amount,
+                    wallet_balance=trade.stake_amount,
                 ))
 
             # Updating wallets when order is closed
@@ -1788,7 +1793,7 @@ class FreqtradeBot(LoggingMixin):
             self.rpc.send_msg(msg)
 
     def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
-                              amount: float, fee_abs: float) -> float:
+                              amount: float, fee_abs: float, order_obj: Order) -> Optional[float]:
         """
         Applies the fee to amount (either from Order or from Trades).
         Can eat into dust if more than the required asset is available.
@@ -1796,40 +1801,42 @@ class FreqtradeBot(LoggingMixin):
         never in base currency.
         """
         self.wallets.update()
-        if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount:
+        amount_ = amount
+        if order_obj.ft_order_side == trade.exit_side or order_obj.ft_order_side == 'stoploss':
+            # check against remaining amount!
+            amount_ = trade.amount - amount
+
+        if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount_:
             # Eat into dust if we own more than base currency
             logger.info(f"Fee amount for {trade} was in base currency - "
                         f"Eating Fee {fee_abs} into dust.")
         elif fee_abs != 0:
-            real_amount = self.exchange.amount_to_precision(trade.pair, amount - fee_abs)
+            logger.info(f"Applying fee on amount for {trade}, fee={fee_abs}.")
-            logger.info(f"Applying fee on amount for {trade} "
+            return fee_abs
-                        f"(from {amount} to {real_amount}).")
+        return None
-            return real_amount
-        return amount
 
     def handle_order_fee(self, trade: Trade, order_obj: Order, order: Dict[str, Any]) -> None:
         # Try update amount (binance-fix)
         try:
-            new_amount = self.get_real_amount(trade, order, order_obj)
+            fee_abs = self.get_real_amount(trade, order, order_obj)
-            if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
+            if fee_abs is not None:
-                           abs_tol=constants.MATH_CLOSE_PREC):
+                order_obj.ft_fee_base = fee_abs
-                order_obj.ft_fee_base = trade.amount - new_amount
         except DependencyException as exception:
             logger.warning("Could not update trade amount: %s", exception)
 
-    def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> float:
+    def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> Optional[float]:
         """
         Detect and update trade fee.
         Calls trade.update_fee() upon correct detection.
         Returns modified amount if the fee was taken from the destination currency.
         Necessary for exchanges which charge fees in base currency (e.g. binance)
-        :return: identical (or new) amount for the trade
+        :return: Absolute fee to apply for this order or None
         """
         # Init variables
         order_amount = safe_value_fallback(order, 'filled', 'amount')
         # Only run for closed orders
         if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
-            return order_amount
+            return None
 
         trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
         # use fee from order-dict if possible
@@ -1846,13 +1853,14 @@ class FreqtradeBot(LoggingMixin):
                 if trade_base_currency == fee_currency:
                     # Apply fee to amount
                     return self.apply_fee_conditional(trade, trade_base_currency,
-                                                      amount=order_amount, fee_abs=fee_cost)
+                                                      amount=order_amount, fee_abs=fee_cost,
-                return order_amount
+                                                      order_obj=order_obj)
+                return None
         return self.fee_detection_from_trades(
             trade, order, order_obj, order_amount, order.get('trades', []))
 
     def fee_detection_from_trades(self, trade: Trade, order: Dict, order_obj: Order,
-                                  order_amount: float, trades: List) -> float:
+                                  order_amount: float, trades: List) -> Optional[float]:
         """
         fee-detection fallback to Trades.
         Either uses provided trades list or the result of fetch_my_trades to get correct fee.
@@ -1863,7 +1871,7 @@ class FreqtradeBot(LoggingMixin):
 
         if len(trades) == 0:
             logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)
-            return order_amount
+            return None
         fee_currency = None
         amount = 0
         fee_abs = 0.0
@@ -1905,10 +1913,9 @@ class FreqtradeBot(LoggingMixin):
             raise DependencyException("Half bought? Amounts don't match")
 
         if fee_abs != 0:
-            return self.apply_fee_conditional(trade, trade_base_currency,
+            return self.apply_fee_conditional(
-                                              amount=amount, fee_abs=fee_abs)
+                trade, trade_base_currency, amount=amount, fee_abs=fee_abs, order_obj=order_obj)
-        else:
+        return None
-            return amount
 
     def get_valid_price(self, custom_price: float, proposed_price: float) -> float:
         """

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
@@ -211,21 +212,12 @@ class Backtesting:
         """
         self.progress.init_step(BacktestState.DATALOAD, 1)
 
-        if self.config.get('freqai', {}).get('enabled', False):
-            startup_candles = int(self.config.get('freqai', {}).get('startup_candles', 0))
-            if not startup_candles:
-                raise OperationalException('FreqAI backtesting module requires user set '
-                                           'startup_candles in config.')
-            self.required_startup += int(self.config.get('freqai', {}).get('startup_candles', 0))
-            logger.info(f'Increasing startup_candle_count for freqai to {self.required_startup}')
-            self.config['startup_candle_count'] = self.required_startup
-
         data = history.load_data(
             datadir=self.config['datadir'],
             pairs=self.pairlists.whitelist,
             timeframe=self.timeframe,
             timerange=self.timerange,
-            startup_candles=self.required_startup,
+            startup_candles=self.dataprovider.get_required_startup(self.timeframe),
             fail_without_data=True,
             data_format=self.config.get('dataformat_ohlcv', 'json'),
             candle_type=self.config.get('candle_type_def', CandleType.SPOT)
@@ -266,7 +258,7 @@ 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 +270,12 @@ 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 +525,16 @@ class Backtesting:
 
         # Check if we should increase our position
         if stake_amount is not None and stake_amount > 0.0:
-
+            check_adjust_entry = True
-            pos_trade = self._enter_trade(
+            if self.strategy.max_entry_position_adjustment > -1:
-                trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade)
+                entry_count = trade.nr_of_successful_entries
-            if pos_trade is not None:
+                check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment)
-                self.wallets.update()
+            if check_adjust_entry:
-                return pos_trade
+                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 +545,8 @@ 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 +566,7 @@ class Backtesting:
 
         # Check if we need to adjust our current positions
         if self.strategy.position_adjustment_enable:
-            check_adjust_entry = True
+            trade = self._get_adjust_trade_entry_for_candle(trade, row)
-            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)
 
         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 +581,15 @@ class Backtesting:
                 return t
         return None
 
-    def _get_exit_for_signal(self, trade: LocalTrade, row: Tuple,
+    def _get_exit_for_signal(
-                             exit_: ExitCheckTuple) -> Optional[LocalTrade]:
+            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 +598,8 @@ 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 +627,23 @@ 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(
+            if (exit_.exit_type not in (ExitType.LIQUIDATION, ExitType.PARTIAL_EXIT)
-                self.strategy.confirm_trade_exit, default_retval=True)(
+                    and not strategy_safe_wrapper(
-                    pair=trade.pair,
+                    self.strategy.confirm_trade_exit, default_retval=True)(
-                    trade=trade,  # type: ignore[arg-type]
+                        pair=trade.pair,
-                    order_type=order_type,
+                        trade=trade,  # type: ignore[arg-type]
-                    amount=trade.amount,
+                        order_type=order_type,
-                    rate=close_rate,
+                        amount=amount_,
-                    time_in_force=time_in_force,
+                        rate=close_rate,
-                    sell_reason=exit_reason,  # deprecated
+                        time_in_force=time_in_force,
-                    exit_reason=exit_reason,
+                        sell_reason=exit_reason,  # deprecated
-                    current_time=exit_candle_time)):
+                        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 +651,10 @@ 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 +668,12 @@ class Backtesting:
             side=trade.exit_side,
             order_type=order_type,
             status="open",
-            price=close_rate,
+            price=rate,
-            average=close_rate,
+            average=rate,
             amount=amount,
             filled=0,
             remaining=amount,
-            cost=amount * close_rate,
+            cost=amount * rate,
         )
         trade.orders.append(order)
         return trade
@@ -821,7 +819,17 @@ 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 +858,10 @@ class Backtesting:
                     trading_mode=self.trading_mode,
                     leverage=leverage,
                     # interest_rate=interest_rate,
-                    amount_precision=self.exchange.get_precision_amount(pair),
+                    amount_precision=precision_amount,
-                    price_precision=self.exchange.get_precision_price(pair),
+                    price_precision=precision_price,
                     precision_mode=self.precision_mode,
+                    contract_size=contract_size,
                     orders=[],
                 )
 
@@ -862,7 +871,8 @@ class Backtesting:
                 pair=pair,
                 open_rate=propose_rate,
                 amount=amount,
-                leverage=leverage,
+                stake_amount=trade.stake_amount,
+                wallet_balance=trade.stake_amount,
                 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:
+    def advise_and_trim(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
-        data, timerange = self.backtesting.load_bt_data()
-        self.backtesting.load_bt_data_detail()
-        logger.info("Dataload complete. Calculating indicators")
-
         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)} '
+    def prepare_hyperopt_data(self) -> None:
-                    f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
+        HyperoptStateContainer.set_state(HyperoptState.DATALOAD)
-                    f'({(self.max_date - self.min_date).days} days)..')
+        data, self.timerange = self.backtesting.load_bt_data()
-        # Store non-trimmed data - will be trimmed after signal generation.
+        self.backtesting.load_bt_data_detail()
-        dump(preprocessed, self.data_pickle_file)
+        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}
             """))
 
@@ -307,7 +308,9 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
     # Migrates both trades and orders table!
     # if ('orders' not in previous_tables
     # or not has_column(cols_orders, 'stop_price')):
-    if not has_column(cols_trades, 'precision_mode'):
+    migrating = False
+    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}")
         migrate_trades_and_orders_table(
@@ -315,6 +318,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
             order_table_bak_name, cols_orders)
 
     if not has_column(cols_pairlocks, 'side'):
+        migrating = True
         logger.info(f"Running database migration for pairlocks - "
                     f"backup: {pairlock_table_bak_name}")
 
@@ -329,3 +333,6 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
 
     set_sqlite_to_wal(engine)
     fix_old_dry_orders(engine)
+
+    if migrating:
+        logger.info("Database migration finished.")

freqtrade/persistence/models.py

@@ -53,7 +53,7 @@ def init_db(db_url: str) -> None:
     # https://docs.sqlalchemy.org/en/13/orm/contextual.html#thread-local-scope
     # Scoped sessions proxy requests to the appropriate thread-local session.
     # We should use the scoped_session object - not a seperately initialized version
-    Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=True))
+    Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=False))
     Trade.query = Trade._session.query_property()
     Order.query = Trade._session.query_property()
     PairLock.query = Trade._session.query_property()

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:
@@ -646,7 +648,6 @@ class LocalTrade():
         """
         self.close_rate = rate
         self.close_date = self.close_date or datetime.utcnow()
-        self.close_profit_abs = self.calc_profit(rate) + self.realized_profit
         self.is_open = False
         self.exit_order_status = 'closed'
         self.open_order_id = None
@@ -841,7 +842,7 @@ 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 +869,6 @@ 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 +877,8 @@ class LocalTrade():
             self.realized_profit = close_profit_abs
             self.close_profit_abs = profit
 
-        current_amount_tr = amount_to_precision(float(current_amount),
+        current_amount_tr = amount_to_contract_precision(
-                                                self.amount_precision, self.precision_mode)
+            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 +893,7 @@ 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 +1044,16 @@ 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 +1142,7 @@ 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/PrecisionFilter.py

@@ -51,16 +51,21 @@ class PrecisionFilter(IPairList):
         :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
+        if ticker.get('last', None) is None:
+            self.log_once(f"Removed {pair} from whitelist, because "
+                          "ticker['last'] is empty (Usually no trade in the last 24h).",
+                          logger.info)
+            return False
         stop_price = ticker['last'] * self._stoploss
 
         # Adjust stop-prices to precision
         sp = self._exchange.price_to_precision(pair, stop_price)
 
         stop_gap_price = self._exchange.price_to_precision(pair, stop_price * 0.99)
-        logger.debug(f"{ticker['symbol']} - {sp} : {stop_gap_price}")
+        logger.debug(f"{pair} - {sp} : {stop_gap_price}")
 
         if sp <= stop_gap_price:
-            self.log_once(f"Removed {ticker['symbol']} from whitelist, because "
+            self.log_once(f"Removed {pair} from whitelist, because "
                           f"stop price {sp} would be <= stop limit {stop_gap_price}", logger.info)
             return False
 

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, "
@@ -186,6 +186,7 @@ class VolumePairList(IPairList):
                     needed_pairs, since_ms=since_ms, cache=False
                 )
             for i, p in enumerate(filtered_tickers):
+                contract_size = self._exchange.markets[p['symbol']].get('contractSize', 1.0) or 1.0
                 pair_candles = candles[
                     (p['symbol'], self._lookback_timeframe, self._def_candletype)
                 ] if (
@@ -193,12 +194,13 @@ 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
 
                         pair_candles['quoteVolume'] = (
                             pair_candles['volume'] * pair_candles['typical_price']
+                            * contract_size
                         )
                     else:
                         # Exchange ohlcv data is in quote volume already.

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

@@ -6,6 +6,7 @@ This module manage Telegram communication
 import json
 import logging
 import re
+from copy import deepcopy
 from dataclasses import dataclass
 from datetime import date, datetime, timedelta
 from functools import partial
@@ -114,18 +115,20 @@ 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$', r'/status table$',
+        valid_keys: List[str] = [
-                                 r'/trades$', r'/performance$', r'/buys', r'/entries',
+            r'/start$', r'/stop$', r'/status$', r'/status table$',
-                                 r'/sells', r'/exits', r'/mix_tags',
+            r'/trades$', r'/performance$', r'/buys', r'/entries',
-                                 r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+',
+            r'/sells', r'/exits', r'/mix_tags',
-                                 r'/stats$', r'/count$', r'/locks$', r'/balance$',
+            r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+',
-                                 r'/stopbuy$', r'/reload_config$', r'/show_config$',
+            r'/stats$', r'/count$', r'/locks$', r'/balance$',
-                                 r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$',
+            r'/stopbuy$', r'/stopentry$', r'/reload_config$', r'/show_config$',
-                                 r'/blacklist$', r'/bl_delete$',
+            r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$',
-                                 r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$',
+            r'/blacklist$', r'/bl_delete$',
-                                 r'/forcebuy$', r'/forcelong$', r'/forceshort$',
+            r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$',
-                                 r'/forcesell$', r'/forceexit$',
+            r'/forcebuy$', r'/forcelong$', r'/forceshort$',
-                                 r'/edge$', r'/health$', r'/help$', r'/version$']
+            r'/forcesell$', 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 +185,7 @@ 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),
@@ -372,7 +375,7 @@ class Telegram(RPCHandler):
             message += f"\n*Duration:* `{msg['duration']} ({msg['duration_min']:.1f} min)`"
         return message
 
-    def compose_message(self, msg: Dict[str, Any], msg_type: RPCMessageType) -> str:
+    def compose_message(self, msg: Dict[str, Any], msg_type: RPCMessageType) -> Optional[str]:
         if msg_type in [RPCMessageType.ENTRY, RPCMessageType.ENTRY_FILL]:
             message = self._format_entry_msg(msg)
 
@@ -409,7 +412,8 @@ class Telegram(RPCHandler):
         elif msg_type == RPCMessageType.STRATEGY_MSG:
             message = f"{msg['msg']}"
         else:
-            raise NotImplementedError(f"Unknown message type: {msg_type}")
+            logger.debug("Unknown message type: %s", msg_type)
+            return None
         return message
 
     def send_msg(self, msg: Dict[str, Any]) -> None:
@@ -436,13 +440,9 @@ class Telegram(RPCHandler):
             # Notification disabled
             return
 
-        # Would this be better than adding un-needed if statements to compose_message?
+        message = self.compose_message(deepcopy(msg), msg_type)
-        try:
+        if message:
-            message = self.compose_message(msg, msg_type)
             self._send_msg(message, disable_notification=(noti == 'silent'))
-        except NotImplementedError:
-            # just skip it
-            return
 
     def _get_sell_emoji(self, msg):
         """
@@ -988,7 +988,7 @@ 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
@@ -996,7 +996,7 @@ 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
@@ -1492,7 +1492,7 @@ 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/interface.py

@@ -79,8 +79,8 @@ class IStrategy(ABC, HyperStrategyMixin):
 
     # Optional time in force
     order_time_in_force: Dict = {
-        'entry': 'gtc',
+        'entry': 'GTC',
-        'exit': 'gtc',
+        'exit': 'GTC',
     }
 
     # run "populate_indicators" only for new candle
@@ -149,10 +149,19 @@ class IStrategy(ABC, HyperStrategyMixin):
     def load_freqAI_model(self) -> None:
         if self.config.get('freqai', {}).get('enabled', False):
             # Import here to avoid importing this if freqAI is disabled
+            from freqtrade.freqai.utils import download_all_data_for_training
             from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
-
             self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
             self.freqai_info = self.config["freqai"]
+
+            # download the desired data in dry/live
+            if self.config.get('runmode') in (RunMode.DRY_RUN, RunMode.LIVE):
+                logger.info(
+                    "Downloading all training data for all pairs in whitelist and "
+                    "corr_pairlist, this may take a while if the data is not "
+                    "already on disk."
+                )
+                download_all_data_for_training(self.dp, self.config)
         else:
             # Gracious failures if freqAI is disabled but "start" is called.
             class DummyClass():
@@ -160,6 +169,10 @@ class IStrategy(ABC, HyperStrategyMixin):
                     raise OperationalException(
                         'freqAI is not enabled. '
                         'Please enable it in your config to use this strategy.')
+
+                def shutdown(self, *args, **kwargs):
+                    pass
+
             self.freqai = DummyClass()  # type: ignore
 
     def ft_bot_start(self, **kwargs) -> None:
@@ -173,6 +186,12 @@ class IStrategy(ABC, HyperStrategyMixin):
 
         self.ft_load_hyper_params(self.config.get('runmode') == RunMode.HYPEROPT)
 
+    def ft_bot_cleanup(self) -> None:
+        """
+        Clean up FreqAI and child threads
+        """
+        self.freqai.shutdown()
+
     @abstractmethod
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """

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]

freqtrade/templates/FreqaiExampleStrategy.py

@@ -43,7 +43,8 @@ class FreqaiExampleStrategy(IStrategy):
     process_only_new_candles = True
     stoploss = -0.05
     use_exit_signal = True
-    startup_candle_count: int = 300
+    # this is the maximum period fed to talib (timeframe independent)
+    startup_candle_count: int = 40
     can_short = False
 
     linear_roi_offset = DecimalParameter(

freqtrade/templates/FreqaiHybridExampleStrategy.py

@@ -0,0 +1,258 @@
+import logging
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical import qtpylib
+
+from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair
+
+
+logger = logging.getLogger(__name__)
+
+
+class FreqaiExampleHybridStrategy(IStrategy):
+    """
+    Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
+    FreqAI to bolster a typical Freqtrade strategy.
+
+    Launching this strategy would be:
+
+    freqtrade trade --strategy FreqaiExampleHyridStrategy --strategy-path freqtrade/templates
+    --freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
+
+    or the user simply adds this to their config:
+
+    "freqai": {
+        "enabled": true,
+        "purge_old_models": true,
+        "train_period_days": 15,
+        "identifier": "uniqe-id",
+        "feature_parameters": {
+            "include_timeframes": [
+                "3m",
+                "15m",
+                "1h"
+            ],
+            "include_corr_pairlist": [
+                "BTC/USDT",
+                "ETH/USDT"
+            ],
+            "label_period_candles": 20,
+            "include_shifted_candles": 2,
+            "DI_threshold": 0.9,
+            "weight_factor": 0.9,
+            "principal_component_analysis": false,
+            "use_SVM_to_remove_outliers": true,
+            "indicator_periods_candles": [10, 20]
+        },
+        "data_split_parameters": {
+            "test_size": 0,
+            "random_state": 1
+        },
+        "model_training_parameters": {
+            "n_estimators": 800
+        }
+    },
+
+    Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
+    """
+
+    minimal_roi = {
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04
+    }
+
+    plot_config = {
+        'main_plot': {
+            'tema': {},
+        },
+        'subplots': {
+            "MACD": {
+                'macd': {'color': 'blue'},
+                'macdsignal': {'color': 'orange'},
+            },
+            "RSI": {
+                'rsi': {'color': 'red'},
+            },
+            "Up_or_down": {
+                '&s-up_or_down': {'color': 'green'},
+            }
+        }
+    }
+
+    process_only_new_candles = True
+    stoploss = -0.05
+    use_exit_signal = True
+    startup_candle_count: int = 300
+    can_short = True
+
+    # Hyperoptable parameters
+    buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
+    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
+    short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
+    exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
+
+    # FreqAI required function, leave as is or add additional informatives to existing structure.
+    def informative_pairs(self):
+        whitelist_pairs = self.dp.current_whitelist()
+        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
+        informative_pairs = []
+        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
+            for pair in whitelist_pairs:
+                informative_pairs.append((pair, tf))
+            for pair in corr_pairs:
+                if pair in whitelist_pairs:
+                    continue  # avoid duplication
+                informative_pairs.append((pair, tf))
+        return informative_pairs
+
+    # FreqAI required function, user can add or remove indicators, but general structure
+    # must stay the same.
+    def populate_any_indicators(
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
+    ):
+        """
+        User feeds these indicators to FreqAI to train a classifier to decide
+        if the market will go up or down.
+
+        :param pair: pair to be used as informative
+        :param df: strategy dataframe which will receive merges from informatives
+        :param tf: timeframe of the dataframe which will modify the feature names
+        :param informative: the dataframe associated with the informative pair
+        """
+
+        coin = pair.split('/')[0]
+
+        if informative is None:
+            informative = self.dp.get_pair_dataframe(pair, tf)
+
+        # first loop is automatically duplicating indicators for time periods
+        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+
+            t = int(t)
+            informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
+            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
+            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
+            informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
+            informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
+            informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
+            informative[f"%-{coin}relative_volume-period_{t}"] = (
+                informative["volume"] / informative["volume"].rolling(t).mean()
+            )
+
+        # FreqAI needs the following lines in order to detect features and automatically
+        # expand upon them.
+        indicators = [col for col in informative if col.startswith("%")]
+        # This loop duplicates and shifts all indicators to add a sense of recency to data
+        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
+            if n == 0:
+                continue
+            informative_shift = informative[indicators].shift(n)
+            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
+            informative = pd.concat((informative, informative_shift), axis=1)
+
+        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
+        skip_columns = [
+            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        df = df.drop(columns=skip_columns)
+
+        # User can set the "target" here (in present case it is the
+        # "up" or "down")
+        if set_generalized_indicators:
+            # User "looks into the future" here to figure out if the future
+            # will be "up" or "down". This same column name is available to
+            # the user
+            df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
+                                           df["close"], 'up', 'down')
+
+        return df
+
+    # flake8: noqa: C901
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # User creates their own custom strat here. Present example is a supertrend
+        # based strategy.
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        # TA indicators to combine with the Freqai targets
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Bollinger Bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe["bb_percent"] = (
+            (dataframe["close"] - dataframe["bb_lowerband"]) /
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
+        )
+        dataframe["bb_width"] = (
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
+        )
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) &
+                (df['tema'] <= df['bb_middleband']) &  # Guard: tema below BB middle
+                (df['tema'] > df['tema'].shift(1)) &  # Guard: tema is raising
+                (df['volume'] > 0) &  # Make sure Volume is not 0
+                (df['do_predict'] == 1) &  # Make sure Freqai is confident in the prediction
+                # Only enter trade if Freqai thinks the trend is in this direction
+                (df['&s-up_or_down'] == 'up')
+            ),
+            'enter_long'] = 1
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) &
+                (df['tema'] > df['bb_middleband']) &  # Guard: tema above BB middle
+                (df['tema'] < df['tema'].shift(1)) &  # Guard: tema is falling
+                (df['volume'] > 0) &  # Make sure Volume is not 0
+                (df['do_predict'] == 1) &  # Make sure Freqai is confident in the prediction
+                # Only enter trade if Freqai thinks the trend is in this direction
+                (df['&s-up_or_down'] == 'down')
+            ),
+            'enter_short'] = 1
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(df['rsi'], self.sell_rsi.value)) &
+                (df['tema'] > df['bb_middleband']) &  # Guard: tema above BB middle
+                (df['tema'] < df['tema'].shift(1)) &  # Guard: tema is falling
+                (df['volume'] > 0)  # Make sure Volume is not 0
+            ),
+
+            'exit_long'] = 1
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value)) &
+                # Guard: tema below BB middle
+                (df['tema'] <= df['bb_middleband']) &
+                (df['tema'] > df['tema'].shift(1)) &  # Guard: tema is raising
+                (df['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'exit_short'] = 1
+
+        return df

freqtrade/templates/base_strategy.py.j2

@@ -88,8 +88,8 @@ class {{ strategy }}(IStrategy):
 
     # Optional order time in force.
     order_time_in_force = {
-        'entry': 'gtc',
+        'entry': 'GTC',
-        'exit': 'gtc'
+        'exit': 'GTC'
     }
     {{ plot_config | indent(4) }}
 

freqtrade/templates/sample_strategy.py

@@ -88,8 +88,8 @@ class SampleStrategy(IStrategy):
 
     # Optional order time in force.
     order_time_in_force = {
-        'entry': 'gtc',
+        'entry': 'GTC',
-        'exit': 'gtc'
+        'exit': 'GTC'
     }
 
     plot_config = {

freqtrade/templates/strategy_analysis_example.ipynb

@@ -30,7 +30,7 @@
     "\n",
     "# Initialize empty configuration object\n",
     "config = Configuration.from_files([])\n",
-    "# Optionally, use existing configuration file\n",
+    "# Optionally (recommended), use existing configuration file\n",
     "# config = Configuration.from_files([\"config.json\"])\n",
     "\n",
     "# Define some constants\n",
@@ -38,7 +38,7 @@
     "# Name of the strategy class\n",
     "config[\"strategy\"] = \"SampleStrategy\"\n",
     "# Location of the data\n",
-    "data_location = Path(config['user_data_dir'], 'data', 'binance')\n",
+    "data_location = config['datadir']\n",
     "# Pair to analyze - Only use one pair here\n",
     "pair = \"BTC/USDT\""
    ]
@@ -365,7 +365,7 @@
  "metadata": {
   "file_extension": ".py",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.7 64-bit ('trade_397')",
    "language": "python",
    "name": "python3"
   },
@@ -379,7 +379,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.7"
   },
   "mimetype": "text/x-python",
   "name": "python",
@@ -427,7 +427,12 @@
    ],
    "window_display": false
   },
-  "version": 3
+  "version": 3,
+  "vscode": {
+   "interpreter": {
+    "hash": "675f32a300d6d26767470181ad0b11dd4676bcce7ed1dd2ffe2fbc370c95fc7c"
+   }
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 4

requirements-dev.txt

@@ -10,21 +10,21 @@ flake8==5.0.4
 flake8-tidy-imports==4.8.0
 mypy==0.971
 pre-commit==2.20.0
-pytest==7.1.2
+pytest==7.1.3
 pytest-asyncio==0.19.0
 pytest-cov==3.0.0
 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
+nbconvert==7.0.0
 
 # 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-hyperopt.txt

@@ -2,7 +2,7 @@
 -r requirements.txt
 
 # Required for hyperopt
-scipy==1.9.0
+scipy==1.9.1
 scikit-learn==1.1.2
 scikit-optimize==0.9.0
 filelock==3.8.0

requirements.txt

@@ -1,22 +1,22 @@
 numpy==1.23.2
-pandas==1.4.3
+pandas==1.4.4
 pandas-ta==0.3.14b
 
-ccxt==1.92.20
+ccxt==1.93.3
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==37.0.4
 aiohttp==3.8.1
 SQLAlchemy==1.4.40
-python-telegram-bot==13.13
+python-telegram-bot==13.14
-arrow==1.2.2
+arrow==1.2.3
 cachetools==4.2.2
 requests==2.28.1
-urllib3==1.26.11
+urllib3==1.26.12
-jsonschema==4.9.1
+jsonschema==4.14.0
 TA-Lib==0.4.24
 technical==1.3.0
 tabulate==0.8.10
-pycoingecko==2.2.0
+pycoingecko==3.0.0
 jinja2==3.1.2
 tables==3.7.0
 blosc==1.10.6
@@ -28,23 +28,23 @@ py_find_1st==1.1.5
 # Load ticker files 30% faster
 python-rapidjson==1.8
 # Properly format api responses
-orjson==3.7.12
+orjson==3.8.0
 
 # Notify systemd
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.79.0
+fastapi==0.82.0
-uvicorn==0.18.2
+uvicorn==0.18.3
 pyjwt==2.4.0
 aiofiles==0.8.0
-psutil==5.9.1
+psutil==5.9.2
 
 # Support for colorized terminal output
 colorama==0.4.5
 # Building config files interactively
 questionary==1.10.0
-prompt-toolkit==3.0.30
+prompt-toolkit==3.0.31
 # Extensions to datetime library
 python-dateutil==2.8.2
 

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

tests/data/test_btanalysis.py

@@ -1,4 +1,3 @@
-from math import isclose
 from pathlib import Path
 from unittest.mock import MagicMock
 
@@ -269,7 +268,7 @@ def test_create_cum_profit(testdatadir):
                                     "cum_profits", timeframe="5m")
     assert "cum_profits" in cum_profits.columns
     assert cum_profits.iloc[0]['cum_profits'] == 0
-    assert isclose(cum_profits.iloc[-1]['cum_profits'], 8.723007518796964e-06)
+    assert pytest.approx(cum_profits.iloc[-1]['cum_profits']) == 8.723007518796964e-06
 
 
 def test_create_cum_profit1(testdatadir):
@@ -287,7 +286,7 @@ def test_create_cum_profit1(testdatadir):
                                     "cum_profits", timeframe="5m")
     assert "cum_profits" in cum_profits.columns
     assert cum_profits.iloc[0]['cum_profits'] == 0
-    assert isclose(cum_profits.iloc[-1]['cum_profits'], 8.723007518796964e-06)
+    assert pytest.approx(cum_profits.iloc[-1]['cum_profits']) == 8.723007518796964e-06
 
     with pytest.raises(ValueError, match='Trade dataframe empty.'):
         create_cum_profit(df.set_index('date'), bt_data[bt_data["pair"] == 'NOTAPAIR'],

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,
+                "minNotional": 0,
-                "max": 100000,
+                "maxNotional": 100000,
-                "mmr": 0.025,
+                "maintenanceMarginRate": 0.025,
-                "lev": 20,
+                "maxLeverage": 20,
                 "maintAmt": 0.0
             },
             {
-                "min": 100000,
+                "minNotional": 100000,
-                "max": 500000,
+                "maxNotional": 500000,
-                "mmr": 0.05,
+                "maintenanceMarginRate": 0.05,
-                "lev": 10,
+                "maxLeverage": 10,
                 "maintAmt": 2500.0
             },
             {
-                "min": 500000,
+                "minNotional": 500000,
-                "max": 1000000,
+                "maxNotional": 1000000,
-                "mmr": 0.1,
+                "maintenanceMarginRate": 0.1,
-                "lev": 5,
+                "maxLeverage": 5,
                 "maintAmt": 27500.0
             },
             {
-                "min": 1000000,
+                "minNotional": 1000000,
-                "max": 2000000,
+                "maxNotional": 2000000,
-                "mmr": 0.15,
+                "maintenanceMarginRate": 0.15,
-                "lev": 3,
+                "maxLeverage": 3,
                 "maintAmt": 77500.0
             },
             {
-                "min": 2000000,
+                "minNotional": 2000000,
-                "max": 5000000,
+                "maxNotional": 5000000,
-                "mmr": 0.25,
+                "maintenanceMarginRate": 0.25,
-                "lev": 2,
+                "maxLeverage": 2,
                 "maintAmt": 277500.0
             },
             {
-                "min": 5000000,
+                "minNotional": 5000000,
-                "max": 30000000,
+                "maxNotional": 30000000,
-                "mmr": 0.5,
+                "maintenanceMarginRate": 0.5,
-                "lev": 1,
+                "maxLeverage": 1,
                 "maintAmt": 1527500.0
             }
         ],
         "ZEC/USDT": [
             {
-                'min': 0,
+                'minNotional': 0,
-                'max': 50000,
+                'maxNotional': 50000,
-                'mmr': 0.01,
+                'maintenanceMarginRate': 0.01,
-                'lev': 50,
+                'maxLeverage': 50,
                 'maintAmt': 0.0
             },
             {
-                'min': 50000,
+                'minNotional': 50000,
-                'max': 150000,
+                'maxNotional': 150000,
-                'mmr': 0.025,
+                'maintenanceMarginRate': 0.025,
-                'lev': 20,
+                'maxLeverage': 20,
                 'maintAmt': 750.0
             },
             {
-                'min': 150000,
+                'minNotional': 150000,
-                'max': 250000,
+                'maxNotional': 250000,
-                'mmr': 0.05,
+                'maintenanceMarginRate': 0.05,
-                'lev': 10,
+                'maxLeverage': 10,
                 'maintAmt': 4500.0
             },
             {
-                'min': 250000,
+                'minNotional': 250000,
-                'max': 500000,
+                'maxNotional': 500000,
-                'mmr': 0.1,
+                'maintenanceMarginRate': 0.1,
-                'lev': 5,
+                'maxLeverage': 5,
                 'maintAmt': 17000.0
             },
             {
-                'min': 500000,
+                'minNotional': 500000,
-                'max': 1000000,
+                'maxNotional': 1000000,
-                'mmr': 0.125,
+                'maintenanceMarginRate': 0.125,
-                'lev': 4,
+                'maxLeverage': 4,
                 'maintAmt': 29500.0
             },
             {
-                'min': 1000000,
+                'minNotional': 1000000,
-                'max': 2000000,
+                'maxNotional': 2000000,
-                'mmr': 0.25,
+                'maintenanceMarginRate': 0.25,
-                'lev': 2,
+                'maxLeverage': 2,
                 'maintAmt': 154500.0
             },
             {
-                'min': 2000000,
+                'minNotional': 2000000,
-                'max': 30000000,
+                'maxNotional': 30000000,
-                'mmr': 0.5,
+                'maintenanceMarginRate': 0.5,
-                'lev': 1,
+                '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

@@ -2,7 +2,6 @@ import copy
 import logging
 from copy import deepcopy
 from datetime import datetime, timedelta, timezone
-from math import isclose
 from random import randint
 from unittest.mock import MagicMock, Mock, PropertyMock, patch
 
@@ -181,11 +180,11 @@ 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 = {}
 
@@ -275,7 +274,7 @@ def test_validate_order_time_in_force(default_conf, mocker, caplog):
         ex.validate_order_time_in_force(tif2)
 
     # Patch to see if this will pass if the values are in the ft dict
-    ex._ft_has.update({"order_time_in_force": ["gtc", "fok", "ioc"]})
+    ex._ft_has.update({"order_time_in_force": ["GTC", "FOK", "IOC"]})
     ex.validate_order_time_in_force(tif2)
 
 
@@ -407,10 +406,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
     # min
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 1, stoploss)
     expected_result = 2 * (1 + 0.05) / (1 - abs(stoploss))
-    assert isclose(result, expected_result)
+    assert pytest.approx(result) == expected_result
     # With Leverage
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 1, stoploss, 3.0)
-    assert isclose(result, expected_result / 3)
+    assert pytest.approx(result) == expected_result / 3
     # max
     result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
     assert result == 10000
@@ -426,10 +425,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
     )
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss)
     expected_result = 2 * 2 * (1 + 0.05) / (1 - abs(stoploss))
-    assert isclose(result, expected_result)
+    assert pytest.approx(result) == expected_result
     # With Leverage
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 5.0)
-    assert isclose(result, expected_result / 5)
+    assert pytest.approx(result) == expected_result / 5
     # max
     result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
     assert result == 20000
@@ -445,10 +444,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
     )
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss)
     expected_result = max(2, 2 * 2) * (1 + 0.05) / (1 - abs(stoploss))
-    assert isclose(result, expected_result)
+    assert pytest.approx(result) == expected_result
     # With Leverage
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 10)
-    assert isclose(result, expected_result / 10)
+    assert pytest.approx(result) == expected_result / 10
 
     # min amount and cost are set (amount is minial)
     markets["ETH/BTC"]["limits"] = {
@@ -461,20 +460,20 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
     )
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss)
     expected_result = max(8, 2 * 2) * (1 + 0.05) / (1 - abs(stoploss))
-    assert isclose(result, expected_result)
+    assert pytest.approx(result) == expected_result
     # With Leverage
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 7.0)
-    assert isclose(result, expected_result / 7.0)
+    assert pytest.approx(result) == expected_result / 7.0
     # Max
     result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
     assert result == 1000
 
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -0.4)
     expected_result = max(8, 2 * 2) * 1.5
-    assert isclose(result, expected_result)
+    assert pytest.approx(result) == expected_result
     # With Leverage
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -0.4, 8.0)
-    assert isclose(result, expected_result / 8.0)
+    assert pytest.approx(result) == expected_result / 8.0
     # Max
     result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
     assert result == 1000
@@ -482,10 +481,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
     # Really big stoploss
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1)
     expected_result = max(8, 2 * 2) * 1.5
-    assert isclose(result, expected_result)
+    assert pytest.approx(result) == expected_result
     # With Leverage
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1, 12.0)
-    assert isclose(result, expected_result / 12)
+    assert pytest.approx(result) == expected_result / 12
     # Max
     result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
     assert result == 1000
@@ -501,7 +500,7 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
 
     # Contract size 0.01
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1)
-    assert isclose(result, expected_result * 0.01)
+    assert pytest.approx(result) == expected_result * 0.01
     # Max
     result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
     assert result == 10
@@ -513,7 +512,7 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
     )
     # With Leverage, Contract size 10
     result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1, 12.0)
-    assert isclose(result, (expected_result / 12) * 10.0)
+    assert pytest.approx(result) == (expected_result / 12) * 10.0
     # Max
     result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
     assert result == 10000
@@ -1503,7 +1502,7 @@ def test_buy_considers_time_in_force(default_conf, mocker, exchange_name):
     assert api_mock.create_order.call_args[0][3] == 1
     assert api_mock.create_order.call_args[0][4] == 200
     assert "timeInForce" in api_mock.create_order.call_args[0][5]
-    assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force
+    assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force.upper()
 
     order_type = 'market'
     time_in_force = 'ioc'
@@ -1642,10 +1641,10 @@ def test_sell_considers_time_in_force(default_conf, mocker, exchange_name):
     assert api_mock.create_order.call_args[0][3] == 1
     assert api_mock.create_order.call_args[0][4] == 200
     assert "timeInForce" in api_mock.create_order.call_args[0][5]
-    assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force
+    assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force.upper()
 
     order_type = 'market'
-    time_in_force = 'ioc'
+    time_in_force = 'IOC'
     order = exchange.create_order(pair='ETH/BTC', ordertype=order_type, side="sell",
                                   amount=1, rate=200, leverage=1.0,
                                   time_in_force=time_in_force)
@@ -2352,10 +2351,11 @@ 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
 
 
@@ -3238,7 +3238,7 @@ def test_get_trades_for_order(default_conf, mocker, exchange_name, trading_mode,
     orders = exchange.get_trades_for_order(order_id, 'ETH/USDT:USDT', since)
     assert len(orders) == 1
     assert orders[0]['price'] == 165
-    assert isclose(orders[0]['amount'], amount)
+    assert pytest.approx(orders[0]['amount']) == amount
     assert api_mock.fetch_my_trades.call_count == 1
     # since argument should be
     assert isinstance(api_mock.fetch_my_trades.call_args[0][1], int)
@@ -3311,16 +3311,16 @@ 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.get_option('trades_pagination') == 'id'
-    assert ex._ft_has['trades_pagination_arg'] == 'since'
+    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.get_option('stoploss_on_exchange')
-    assert ex._ft_has['order_time_in_force'] == ['gtc', 'fok', 'ioc']
+    assert ex.get_option('order_time_in_force') == ['GTC', 'FOK', 'IOC']
-    assert ex._ft_has['trades_pagination'] == 'id'
+    assert ex.get_option('trades_pagination') == 'id'
-    assert ex._ft_has['trades_pagination_arg'] == 'fromId'
+    assert ex.get_option('trades_pagination_arg') == 'fromId'
 
     conf = copy.deepcopy(default_conf)
     conf['exchange']['_ft_has_params'] = {"DeadBeef": 20,
@@ -3775,8 +3775,8 @@ def test__get_funding_fees_from_exchange(default_conf, mocker, exchange_name):
         since=unix_time
     )
 
-    assert (isclose(expected_fees, fees_from_datetime))
+    assert pytest.approx(expected_fees) == fees_from_datetime
-    assert (isclose(expected_fees, fees_from_unix_time))
+    assert pytest.approx(expected_fees) == fees_from_unix_time
 
     ccxt_exceptionhandlers(
         mocker,
@@ -4088,66 +4088,6 @@ def test_combine_funding_and_mark(
         assert len(df) == 0
 
 
-def test_get_or_calculate_liquidation_price(mocker, default_conf):
-
-    api_mock = MagicMock()
-    positions = [
-        {
-            'info': {},
-            'symbol': 'NEAR/USDT:USDT',
-            'timestamp': 1642164737148,
-            'datetime': '2022-01-14T12:52:17.148Z',
-            'initialMargin': 1.51072,
-            'initialMarginPercentage': 0.1,
-            'maintenanceMargin': 0.38916147,
-            'maintenanceMarginPercentage': 0.025,
-            'entryPrice': 18.884,
-            'notional': 15.1072,
-            'leverage': 9.97,
-            'unrealizedPnl': 0.0048,
-            'contracts': 8,
-            'contractSize': 0.1,
-            'marginRatio': None,
-            'liquidationPrice': 17.47,
-            'markPrice': 18.89,
-            'margin_mode': 1.52549075,
-            'marginType': 'isolated',
-            'side': 'buy',
-            'percentage': 0.003177292946409658
-        }
-    ]
-    api_mock.fetch_positions = MagicMock(return_value=positions)
-    mocker.patch.multiple(
-        'freqtrade.exchange.Exchange',
-        exchange_has=MagicMock(return_value=True),
-    )
-    default_conf['dry_run'] = False
-    default_conf['trading_mode'] = 'futures'
-    default_conf['margin_mode'] = 'isolated'
-    default_conf['liquidation_buffer'] = 0.0
-
-    exchange = get_patched_exchange(mocker, default_conf, api_mock)
-    liq_price = exchange.get_or_calculate_liquidation_price(
-        pair='NEAR/USDT:USDT',
-        open_rate=18.884,
-        is_short=False,
-        position=0.8,
-        wallet_balance=0.8,
-    )
-    assert liq_price == 17.47
-
-    default_conf['liquidation_buffer'] = 0.05
-    exchange = get_patched_exchange(mocker, default_conf, api_mock)
-    liq_price = exchange.get_or_calculate_liquidation_price(
-        pair='NEAR/USDT:USDT',
-        open_rate=18.884,
-        is_short=False,
-        position=0.8,
-        wallet_balance=0.8,
-    )
-    assert liq_price == 17.540699999999998
-
-
 @pytest.mark.parametrize('exchange,rate_start,rate_end,d1,d2,amount,expected_fees', [
     ('binance', 0, 2, "2021-09-01 01:00:00", "2021-09-01 04:00:00",  30.0, 0.0),
     ('binance', 0, 2, "2021-09-01 00:00:00", "2021-09-01 08:00:00",  30.0, -0.00091409999),
@@ -4287,7 +4227,7 @@ 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 +4246,7 @@ 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
 
 
@@ -4538,11 +4478,12 @@ def test_liquidation_price_is_none(
     default_conf['trading_mode'] = trading_mode
     default_conf['margin_mode'] = margin_mode
     exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
-    assert exchange.get_or_calculate_liquidation_price(
+    assert exchange.get_liquidation_price(
         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 +4492,7 @@ 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,
@@ -4565,22 +4506,23 @@ 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
     default_conf['liquidation_buffer'] = 0.0
     exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
     exchange.get_maintenance_ratio_and_amt = MagicMock(return_value=(mm_ratio, maintenance_amt))
-    assert isclose(round(exchange.get_or_calculate_liquidation_price(
+    assert pytest.approx(round(exchange.get_liquidation_price(
         pair='DOGE/USDT',
         open_rate=open_rate,
         is_short=is_short,
         wallet_balance=wallet_balance,
         mm_ex_1=mm_ex_1,
         upnl_ex_1=upnl_ex_1,
-        position=position,
+        amount=amount,
-    ), 2), expected)
+        stake_amount=open_rate * amount,
+    ), 2)) == expected
 
 
 def test_get_max_pair_stake_amount(
@@ -4791,6 +4733,20 @@ 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 +4767,10 @@ def test_parse_leverage_tier(mocker, default_conf):
     }
 
     assert exchange.parse_leverage_tier(tier) == {
-        "min": 0,
+        "minNotional": 0,
-        "max": 100000,
+        "maxNotional": 100000,
-        "mmr": 0.025,
+        "maintenanceMarginRate": 0.025,
-        "lev": 20,
+        "maxLeverage": 20,
         "maintAmt": 0.0,
     }
 
@@ -4840,10 +4796,10 @@ def test_parse_leverage_tier(mocker, default_conf):
     }
 
     assert exchange.parse_leverage_tier(tier2) == {
-        'min': 0,
+        'minNotional': 0,
-        'max': 2000,
+        'maxNotional': 2000,
-        'mmr': 0.01,
+        'maintenanceMarginRate': 0.01,
-        'lev': 75,
+        'maxLeverage': 75,
         "maintAmt": None,
     }
 
@@ -4911,8 +4867,8 @@ def test_get_max_leverage_futures(default_conf, mocker, leverage_tiers):
     assert exchange.get_max_leverage("BNB/BUSD", 1.0) == 20.0
     assert exchange.get_max_leverage("BNB/USDT", 100.0) == 75.0
     assert exchange.get_max_leverage("BTC/USDT", 170.30) == 125.0
-    assert isclose(exchange.get_max_leverage("BNB/BUSD", 99999.9), 5.000005)
+    assert pytest.approx(exchange.get_max_leverage("BNB/BUSD", 99999.9)) == 5.000005
-    assert isclose(exchange.get_max_leverage("BNB/USDT", 1500), 33.333333333333333)
+    assert pytest.approx(exchange.get_max_leverage("BNB/USDT", 1500)) == 33.333333333333333
     assert exchange.get_max_leverage("BTC/USDT", 300000000) == 2.0
     assert exchange.get_max_leverage("BTC/USDT", 600000000) == 1.0  # Last tier
 
@@ -4935,7 +4891,7 @@ def test__get_params(mocker, default_conf, exchange_name):
     params1 = {'test': True}
     params2 = {
         'test': True,
-        'timeInForce': 'ioc',
+        'timeInForce': 'IOC',
         'reduceOnly': True,
     }
 
@@ -4950,7 +4906,7 @@ def test__get_params(mocker, default_conf, exchange_name):
         side="buy",
         ordertype='market',
         reduceOnly=False,
-        time_in_force='gtc',
+        time_in_force='GTC',
         leverage=1.0,
     ) == params1
 
@@ -4958,7 +4914,7 @@ def test__get_params(mocker, default_conf, exchange_name):
         side="buy",
         ordertype='market',
         reduceOnly=False,
-        time_in_force='ioc',
+        time_in_force='IOC',
         leverage=1.0,
     ) == params1
 
@@ -4966,7 +4922,7 @@ def test__get_params(mocker, default_conf, exchange_name):
         side="buy",
         ordertype='limit',
         reduceOnly=False,
-        time_in_force='gtc',
+        time_in_force='GTC',
         leverage=1.0,
     ) == params1
 
@@ -4979,11 +4935,97 @@ def test__get_params(mocker, default_conf, exchange_name):
         side="buy",
         ordertype='limit',
         reduceOnly=True,
-        time_in_force='ioc',
+        time_in_force='IOC',
         leverage=3.0,
     ) == params2
 
 
+def test_get_liquidation_price1(mocker, default_conf):
+
+    api_mock = MagicMock()
+    positions = [
+        {
+            'info': {},
+            'symbol': 'NEAR/USDT:USDT',
+            'timestamp': 1642164737148,
+            'datetime': '2022-01-14T12:52:17.148Z',
+            'initialMargin': 1.51072,
+            'initialMarginPercentage': 0.1,
+            'maintenanceMargin': 0.38916147,
+            'maintenanceMarginPercentage': 0.025,
+            'entryPrice': 18.884,
+            'notional': 15.1072,
+            'leverage': 9.97,
+            'unrealizedPnl': 0.0048,
+            'contracts': 8,
+            'contractSize': 0.1,
+            'marginRatio': None,
+            'liquidationPrice': 17.47,
+            'markPrice': 18.89,
+            'margin_mode': 1.52549075,
+            'marginType': 'isolated',
+            'side': 'buy',
+            'percentage': 0.003177292946409658
+        }
+    ]
+    api_mock.fetch_positions = MagicMock(return_value=positions)
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        exchange_has=MagicMock(return_value=True),
+    )
+    default_conf['dry_run'] = False
+    default_conf['trading_mode'] = 'futures'
+    default_conf['margin_mode'] = 'isolated'
+    default_conf['liquidation_buffer'] = 0.0
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    liq_price = exchange.get_liquidation_price(
+        pair='NEAR/USDT:USDT',
+        open_rate=18.884,
+        is_short=False,
+        amount=0.8,
+        stake_amount=18.884 * 0.8,
+        wallet_balance=18.884 * 0.8,
+    )
+    assert liq_price == 17.47
+
+    default_conf['liquidation_buffer'] = 0.05
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    liq_price = exchange.get_liquidation_price(
+        pair='NEAR/USDT:USDT',
+        open_rate=18.884,
+        is_short=False,
+        amount=0.8,
+        stake_amount=18.884 * 0.8,
+        wallet_balance=18.884 * 0.8,
+    )
+    assert liq_price == 17.540699999999998
+
+    api_mock.fetch_positions = MagicMock(return_value=[])
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    liq_price = exchange.get_liquidation_price(
+        pair='NEAR/USDT:USDT',
+        open_rate=18.884,
+        is_short=False,
+        amount=0.8,
+        stake_amount=18.884 * 0.8,
+        wallet_balance=18.884 * 0.8,
+    )
+    assert liq_price is None
+    default_conf['trading_mode'] = 'margin'
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    with pytest.raises(OperationalException, match=r'.*does not support .* margin'):
+        exchange.get_liquidation_price(
+            pair='NEAR/USDT:USDT',
+            open_rate=18.884,
+            is_short=False,
+            amount=0.8,
+            stake_amount=18.884 * 0.8,
+            wallet_balance=18.884 * 0.8,
+        )
+
+
 @pytest.mark.parametrize('liquidation_buffer', [0.0, 0.05])
 @pytest.mark.parametrize(
     "is_short,trading_mode,exchange_name,margin_mode,leverage,open_rate,amount,expected_liq", [
@@ -4997,22 +5039,22 @@ def test__get_params(mocker, default_conf, exchange_name):
         (True, 'futures', 'binance', 'isolated', 5.0, 10.0, 1.0, 11.89108910891089),
         (True, 'futures', 'binance', 'isolated', 3.0, 10.0, 1.0, 13.211221122079207),
         (True, 'futures', 'binance', 'isolated', 5.0, 8.0, 1.0, 9.514851485148514),
-        (True, 'futures', 'binance', 'isolated', 5.0, 10.0, 0.6, 12.557755775577558),
+        (True, 'futures', 'binance', 'isolated', 5.0, 10.0, 0.6, 11.897689768976898),
         # Binance, long
         (False, 'futures', 'binance', 'isolated', 5, 10, 1.0, 8.070707070707071),
         (False, 'futures', 'binance', 'isolated', 5, 8, 1.0, 6.454545454545454),
-        (False, 'futures', 'binance', 'isolated', 3, 10, 1.0, 6.717171717171718),
+        (False, 'futures', 'binance', 'isolated', 3, 10, 1.0, 6.723905723905723),
-        (False, 'futures', 'binance', 'isolated', 5, 10, 0.6, 7.39057239057239),
+        (False, 'futures', 'binance', 'isolated', 5, 10, 0.6, 8.063973063973064),
         # Gateio/okx, short
         (True, 'futures', 'gateio', 'isolated', 5, 10, 1.0, 11.87413417771621),
         (True, 'futures', 'gateio', 'isolated', 5, 10, 2.0, 11.87413417771621),
-        (True, 'futures', 'gateio', 'isolated', 3, 10, 1.0, 13.476180850346978),
+        (True, 'futures', 'gateio', 'isolated', 3, 10, 1.0, 13.193482419684678),
         (True, 'futures', 'gateio', 'isolated', 5, 8, 1.0, 9.499307342172967),
+        (True, 'futures', 'okx', 'isolated', 3, 10, 1.0, 13.193482419684678),
         # Gateio/okx, long
         (False, 'futures', 'gateio', 'isolated', 5.0, 10.0, 1.0, 8.085708510208207),
         (False, 'futures', 'gateio', 'isolated', 3.0, 10.0, 1.0, 6.738090425173506),
-        # (True, 'futures', 'okx', 'isolated', 11.87413417771621),
+        (False, 'futures', 'okx', 'isolated', 3.0, 10.0, 1.0, 6.738090425173506),
-        # (False, 'futures', 'okx', 'isolated', 8.085708510208207),
     ]
 )
 def test_get_liquidation_price(
@@ -5085,7 +5127,7 @@ def test_get_liquidation_price(
     default_conf_usdt['exchange']['name'] = exchange_name
     default_conf_usdt['margin_mode'] = margin_mode
     mocker.patch('freqtrade.exchange.Gateio.validate_ordertypes')
-    exchange = get_patched_exchange(mocker, default_conf_usdt)
+    exchange = get_patched_exchange(mocker, default_conf_usdt, id=exchange_name)
 
     exchange.get_maintenance_ratio_and_amt = MagicMock(return_value=(0.01, 0.01))
     exchange.name = exchange_name
@@ -5096,7 +5138,9 @@ def test_get_liquidation_price(
         pair='ETH/USDT:USDT',
         open_rate=open_rate,
         amount=amount,
-        leverage=leverage,
+        stake_amount=amount * open_rate / leverage,
+        wallet_balance=amount * open_rate / leverage,
+        # leverage=leverage,
         is_short=is_short,
     )
     if expected_liq is None:
@@ -5104,7 +5148,7 @@ def test_get_liquidation_price(
     else:
         buffer_amount = liquidation_buffer * abs(open_rate - expected_liq)
         expected_liq = expected_liq - buffer_amount if is_short else expected_liq + buffer_amount
-        isclose(expected_liq, liq)
+        assert pytest.approx(expected_liq) == liq
 
 
 @pytest.mark.parametrize('contract_size,order_amount', [
@@ -5131,7 +5175,7 @@ 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_kraken.py

@@ -50,7 +50,7 @@ def test_buy_kraken_trading_agreement(default_conf, mocker):
     assert api_mock.create_order.call_args[0][2] == 'buy'
     assert api_mock.create_order.call_args[0][3] == 1
     assert api_mock.create_order.call_args[0][4] == 200
-    assert api_mock.create_order.call_args[0][5] == {'timeInForce': 'ioc',
+    assert api_mock.create_order.call_args[0][5] == {'timeInForce': 'IOC',
                                                      'trading_agreement': 'agree'}
 
 

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,
+                'minNotional': 0,
-                'max': 500,
+                'maxNotional': 500,
-                'mmr': 0.02,
+                'maintenanceMarginRate': 0.02,
-                'lev': 75,
+                'maxLeverage': 75,
                 'maintAmt': None
             },
             {
-                'min': 501,
+                'minNotional': 501,
-                'max': 1000,
+                'maxNotional': 1000,
-                'mmr': 0.025,
+                'maintenanceMarginRate': 0.025,
-                'lev': 50,
+                'maxLeverage': 50,
                 'maintAmt': None
             },
             {
-                'min': 1001,
+                'minNotional': 1001,
-                'max': 2000,
+                'maxNotional': 2000,
-                'mmr': 0.03,
+                'maintenanceMarginRate': 0.03,
-                'lev': 20,
+                'maxLeverage': 20,
                 'maintAmt': None
             },
         ],
         'ETH/USDT:USDT': [
             {
-                'min': 0,
+                'minNotional': 0,
-                'max': 2000,
+                'maxNotional': 2000,
-                'mmr': 0.01,
+                'maintenanceMarginRate': 0.01,
-                'lev': 75,
+                'maxLeverage': 75,
                 'maintAmt': None
             },
             {
-                'min': 2001,
+                'minNotional': 2001,
-                'max': 4000,
+                'maxNotional': 4000,
-                'mmr': 0.015,
+                'maintenanceMarginRate': 0.015,
-                'lev': 50,
+                'maxLeverage': 50,
                 'maintAmt': None
             },
             {
-                'min': 4001,
+                'minNotional': 4001,
-                'max': 8000,
+                'maxNotional': 8000,
-                'mmr': 0.02,
+                'maintenanceMarginRate': 0.02,
-                'lev': 20,
+                '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
 
@@ -44,7 +45,6 @@ def freqai_conf(default_conf, tmpdir):
                     "principal_component_analysis": False,
                     "use_SVM_to_remove_outliers": True,
                     "stratify_training_data": 0,
-                    "indicator_max_period_candles": 10,
                     "indicator_periods_candles": [10],
                 },
                 "data_split_parameters": {"test_size": 0.33, "random_state": 1},
@@ -81,6 +81,51 @@ 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_backtesting.py

@@ -48,10 +48,4 @@ def test_freqai_backtest_load_data(freqai_conf, mocker, caplog):
 
     assert log_has_re('Increasing startup_candle_count for freqai to.*', caplog)
 
-    del freqai_conf['freqai']['startup_candles']
-    backtesting = Backtesting(freqai_conf)
-    with pytest.raises(OperationalException,
-                       match=r'FreqAI backtesting module.*startup_candles in config.'):
-        backtesting.load_bt_data()
-
     Backtesting.cleanup()

tests/freqai/test_freqai_datakitchen.py

@@ -1,11 +1,12 @@
-import datetime
 import shutil
+from datetime import datetime, timedelta, timezone
 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(
@@ -55,14 +56,38 @@ def test_split_timerange(
     shutil.rmtree(Path(dk.full_path))
 
 
-@pytest.mark.parametrize(
+def test_check_if_model_expired(mocker, freqai_conf):
-    "timestamp, expected",
+
-    [
-        (datetime.datetime.now(tz=datetime.timezone.utc).timestamp() - 7200, True),
-        (datetime.datetime.now(tz=datetime.timezone.utc).timestamp(), False),
-    ],
-)
-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
+    now = datetime.now(tz=timezone.utc).timestamp()
+    assert dk.check_if_model_expired(now) is False
+    now = (datetime.now(tz=timezone.utc) - timedelta(hours=2)).timestamp()
+    assert dk.check_if_model_expired(now) is True
     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 2.36.",
+        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) == 2.54
+
+
+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 8.09%",
+        caplog,
+    )

tests/freqai/test_freqai_interface.py

@@ -174,6 +174,7 @@ def test_train_model_in_series_LightGBMClassifier(mocker, freqai_conf):
 
 def test_start_backtesting(mocker, freqai_conf):
     freqai_conf.update({"timerange": "20180120-20180130"})
+    freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
     strategy.dp = DataProvider(freqai_conf, exchange)
@@ -192,7 +193,7 @@ def test_start_backtesting(mocker, freqai_conf):
     freqai.start_backtesting(df, metadata, freqai.dk)
     model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
 
-    assert len(model_folders) == 5
+    assert len(model_folders) == 6
 
     shutil.rmtree(Path(freqai.dk.full_path))
 
@@ -200,6 +201,7 @@ def test_start_backtesting(mocker, freqai_conf):
 def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
     freqai_conf.update({"timerange": "20180120-20180124"})
     freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
+    freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
     strategy.dp = DataProvider(freqai_conf, exchange)
@@ -217,13 +219,14 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
     metadata = {"pair": "LTC/BTC"}
     freqai.start_backtesting(df, metadata, freqai.dk)
     model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
-    assert len(model_folders) == 8
+    assert len(model_folders) == 9
 
     shutil.rmtree(Path(freqai.dk.full_path))
 
 
 def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
     freqai_conf.update({"timerange": "20180120-20180130"})
+    freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
     strategy.dp = DataProvider(freqai_conf, exchange)
@@ -242,7 +245,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
     freqai.start_backtesting(df, metadata, freqai.dk)
     model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
 
-    assert len(model_folders) == 5
+    assert len(model_folders) == 6
 
     # without deleting the exiting folder structure, re-run
 
@@ -263,10 +266,14 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
     freqai.start_backtesting(df, metadata, freqai.dk)
 
     assert log_has_re(
-        "Found model at ",
+        "Found backtesting prediction file ",
         caplog,
     )
 
+    path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
+    prediction_files = [x for x in path.iterdir() if x.is_file()]
+    assert len(prediction_files) == 5
+
     shutil.rmtree(Path(freqai.dk.full_path))
 
 

tests/leverage/test_interest.py

@@ -1,5 +1,3 @@
-from math import isclose
-
 import pytest
 
 from freqtrade.leverage import interest
@@ -30,9 +28,9 @@ twentyfive_hours = FtPrecise(25.0)
 def test_interest(exchange, interest_rate, hours, expected):
     borrowed = FtPrecise(60.0)
 
-    assert isclose(interest(
+    assert pytest.approx(float(interest(
         exchange_name=exchange,
         borrowed=borrowed,
         rate=FtPrecise(interest_rate),
         hours=hours
-    ), expected)
+    ))) == expected

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
+        0.1,  # Open
-        0.0012,  # High
+        0.12,  # High
-        0.00099,  # Low
+        0.099,  # Low
-        0.0011,  # Close
+        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
+    # ep1(trade.open_rate) = 0.1
-    # position(trade.amount) = 1500000
+    # 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)
+    #   = 0.08080740740740741 + ((0.1 - 0.08080740740740741) * 0.05 * 1)
-    #   = 0.0008176703703703704
+    #   = 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)
+    #   = 0.11881254125412541 + (abs(0.1 - 0.11881254125412541) * 0.05 * -1)
-    #   = 0.0011787191419141915
+    #   = 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/plugins/test_pairlist.py

@@ -366,6 +366,9 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
     ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
       {"method": "PrecisionFilter"}],
      "BTC", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'XRP/BTC']),
+    ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+      {"method": "PrecisionFilter"}],
+     "USDT", ['ETH/USDT', 'NANO/USDT']),
     # PriceFilter and VolumePairList
     ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
       {"method": "PriceFilter", "low_price_ratio": 0.03}],

tests/plugins/test_protections.py

@@ -37,6 +37,7 @@ def generate_mock_trade(pair: str, fee: float, is_open: bool,
     trade.orders.append(Order(
         ft_order_side=trade.entry_side,
         order_id=f'{pair}-{trade.entry_side}-{trade.open_date}',
+        ft_is_open=False,
         ft_pair=pair,
         amount=trade.amount,
         filled=trade.amount,
@@ -51,6 +52,7 @@ def generate_mock_trade(pair: str, fee: float, is_open: bool,
         trade.orders.append(Order(
             ft_order_side=trade.exit_side,
             order_id=f'{pair}-{trade.exit_side}-{trade.close_date}',
+            ft_is_open=False,
             ft_pair=pair,
             amount=trade.amount,
             filled=trade.amount,
@@ -67,6 +69,8 @@ def generate_mock_trade(pair: str, fee: float, is_open: bool,
         trade.close(open_rate * (2 - profit_rate if is_short else profit_rate))
         trade.exit_reason = exit_reason
 
+    Trade.query.session.add(trade)
+    Trade.commit()
     return trade
 
 
@@ -125,33 +129,33 @@ def test_stoploss_guard(mocker, default_conf, fee, caplog, is_short):
     assert not log_has_re(message, caplog)
     caplog.clear()
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=200, min_ago_close=30, is_short=is_short,
-    ))
+    )
 
     assert not freqtrade.protections.global_stop()
     assert not log_has_re(message, caplog)
     caplog.clear()
     # This trade does not count, as it's closed too long ago
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'BCH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=250, min_ago_close=100, is_short=is_short,
-    ))
+    )
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'ETH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=240, min_ago_close=30, is_short=is_short,
-    ))
+    )
     # 3 Trades closed - but the 2nd has been closed too long ago.
     assert not freqtrade.protections.global_stop()
     assert not log_has_re(message, caplog)
     caplog.clear()
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'LTC/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=180, min_ago_close=30, is_short=is_short,
-    ))
+    )
 
     assert freqtrade.protections.global_stop()
     assert log_has_re(message, caplog)
@@ -186,25 +190,25 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair
     assert not log_has_re(message, caplog)
     caplog.clear()
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=200, min_ago_close=30, profit_rate=0.9, is_short=is_short
-    ))
+    )
 
     assert not freqtrade.protections.stop_per_pair(pair)
     assert not freqtrade.protections.global_stop()
     assert not log_has_re(message, caplog)
     caplog.clear()
     # This trade does not count, as it's closed too long ago
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=250, min_ago_close=100, profit_rate=0.9, is_short=is_short
-    ))
+    )
     # Trade does not count for per pair stop as it's the wrong pair.
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'ETH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=240, min_ago_close=30, profit_rate=0.9, is_short=is_short
-    ))
+    )
     # 3 Trades closed - but the 2nd has been closed too long ago.
     assert not freqtrade.protections.stop_per_pair(pair)
     assert freqtrade.protections.global_stop() != only_per_pair
@@ -216,10 +220,10 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair
     caplog.clear()
 
     # Trade does not count potentially, as it's in the wrong direction
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=150, min_ago_close=25, profit_rate=0.9, is_short=not is_short
-    ))
+    )
     freqtrade.protections.stop_per_pair(pair)
     assert freqtrade.protections.global_stop() != only_per_pair
     assert PairLocks.is_pair_locked(pair, side=check_side) != (only_per_side and only_per_pair)
@@ -231,10 +235,10 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair
     caplog.clear()
 
     # 2nd Trade that counts with correct pair
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=180, min_ago_close=30, profit_rate=0.9, is_short=is_short
-    ))
+    )
 
     freqtrade.protections.stop_per_pair(pair)
     assert freqtrade.protections.global_stop() != only_per_pair
@@ -259,20 +263,20 @@ def test_CooldownPeriod(mocker, default_conf, fee, caplog):
     assert not log_has_re(message, caplog)
     caplog.clear()
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=200, min_ago_close=30,
-    ))
+    )
 
     assert not freqtrade.protections.global_stop()
     assert freqtrade.protections.stop_per_pair('XRP/BTC')
     assert PairLocks.is_pair_locked('XRP/BTC')
     assert not PairLocks.is_global_lock()
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'ETH/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value,
         min_ago_open=205, min_ago_close=35,
-    ))
+    )
 
     assert not freqtrade.protections.global_stop()
     assert not PairLocks.is_pair_locked('ETH/BTC')
@@ -300,10 +304,10 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side):
     assert not log_has_re(message, caplog)
     caplog.clear()
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=800, min_ago_close=450, profit_rate=0.9,
-    ))
+    )
 
     Trade.commit()
     # Not locked with 1 trade
@@ -312,10 +316,10 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side):
     assert not PairLocks.is_pair_locked('XRP/BTC')
     assert not PairLocks.is_global_lock()
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=200, min_ago_close=120, profit_rate=0.9,
-    ))
+    )
 
     Trade.commit()
     # Not locked with 1 trade (first trade is outside of lookback_period)
@@ -325,19 +329,19 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side):
     assert not PairLocks.is_global_lock()
 
     # Add positive trade
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value,
         min_ago_open=20, min_ago_close=10, profit_rate=1.15, is_short=True
-    ))
+    )
     Trade.commit()
     assert freqtrade.protections.stop_per_pair('XRP/BTC') != only_per_side
     assert not PairLocks.is_pair_locked('XRP/BTC', side='*')
     assert PairLocks.is_pair_locked('XRP/BTC', side='long') == only_per_side
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=110, min_ago_close=21, profit_rate=0.8,
-    ))
+    )
     Trade.commit()
 
     # Locks due to 2nd trade
@@ -365,36 +369,38 @@ def test_MaxDrawdown(mocker, default_conf, fee, caplog):
     assert not freqtrade.protections.stop_per_pair('XRP/BTC')
     caplog.clear()
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=1000, min_ago_close=900, profit_rate=1.1,
-    ))
+    )
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'ETH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=1000, min_ago_close=900, profit_rate=1.1,
-    ))
+    )
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'NEO/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=1000, min_ago_close=900, profit_rate=1.1,
-    ))
+    )
+    Trade.commit()
     # No losing trade yet ... so max_drawdown will raise exception
     assert not freqtrade.protections.global_stop()
     assert not freqtrade.protections.stop_per_pair('XRP/BTC')
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=500, min_ago_close=400, profit_rate=0.9,
-    ))
+    )
     # Not locked with one trade
     assert not freqtrade.protections.global_stop()
     assert not freqtrade.protections.stop_per_pair('XRP/BTC')
     assert not PairLocks.is_pair_locked('XRP/BTC')
     assert not PairLocks.is_global_lock()
 
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value,
         min_ago_open=1200, min_ago_close=1100, profit_rate=0.5,
-    ))
+    )
+    Trade.commit()
 
     # Not locked with 1 trade (2nd trade is outside of lookback_period)
     assert not freqtrade.protections.global_stop()
@@ -404,20 +410,22 @@ def test_MaxDrawdown(mocker, default_conf, fee, caplog):
     assert not log_has_re(message, caplog)
 
     # Winning trade ... (should not lock, does not change drawdown!)
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value,
         min_ago_open=320, min_ago_close=410, profit_rate=1.5,
-    ))
+    )
+    Trade.commit()
     assert not freqtrade.protections.global_stop()
     assert not PairLocks.is_global_lock()
 
     caplog.clear()
 
     # Add additional negative trade, causing a loss of > 15%
-    Trade.query.session.add(generate_mock_trade(
+    generate_mock_trade(
         'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value,
         min_ago_open=20, min_ago_close=10, profit_rate=0.8,
-    ))
+    )
+    Trade.commit()
     assert not freqtrade.protections.stop_per_pair('XRP/BTC')
     # local lock not supported
     assert not PairLocks.is_pair_locked('XRP/BTC')

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()
+    result = rpc._rpc_stopentry()
-    assert {'status': 'No more buy will occur from now. Run /reload_config to reset.'} == result
+    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

@@ -423,13 +423,20 @@ 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]
 
 
@@ -2137,10 +2138,11 @@ def test_send_msg_strategy_msg_notification(default_conf, mocker) -> None:
 
 
 def test_send_msg_unknown_type(default_conf, mocker) -> None:
-    telegram, _, _ = get_telegram_testobject(mocker, default_conf)
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
     telegram.send_msg({
         'type': None,
     })
+    msg_mock.call_count == 0
 
 
 @pytest.mark.parametrize('message_type,enter,enter_signal,leverage', [