Merge branch 'develop' into strategies

.github/workflows/ci.yml

@@ -24,7 +24,7 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
-        python-version: ["3.8", "3.9", "3.10.6"]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v3
@@ -74,7 +74,7 @@ jobs:
       if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
 
     - name: Coveralls
-      if: (runner.os == 'Linux' && matrix.python-version == '3.9')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.10' && matrix.os == 'ubuntu-22.04')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -121,7 +121,7 @@ jobs:
     strategy:
       matrix:
         os: [ macos-latest ]
-        python-version: ["3.8", "3.9", "3.10.6"]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v3
@@ -205,7 +205,7 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: ["3.8", "3.9", "3.10.6"]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v3
@@ -441,4 +441,4 @@ jobs:
       with:
           severity: info
           details: Deploy Succeeded!
-          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}

.pre-commit-config.yaml

@@ -15,9 +15,9 @@ repos:
         additional_dependencies:
           - types-cachetools==5.2.1
           - types-filelock==3.2.7
-          - types-requests==2.28.11
-          - types-tabulate==0.8.11
-          - types-python-dateutil==2.8.19
+          - types-requests==2.28.11.2
+          - types-tabulate==0.9.0.0
+          - types-python-dateutil==2.8.19.1
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort

config_examples/config_freqai.example.json

@@ -18,13 +18,8 @@
         "name": "binance",
         "key": "",
         "secret": "",
-        "ccxt_config": {
-            "enableRateLimit": true
-        },
-        "ccxt_async_config": {
-            "enableRateLimit": true,
-            "rateLimit": 200
-        },
+        "ccxt_config": {},
+        "ccxt_async_config": {},
         "pair_whitelist": [
             "1INCH/USDT",
             "ALGO/USDT"

docker/Dockerfile.armhf

@@ -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-dev \
+  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-dev libutf8proc-dev libsnappy-dev \
   && apt-get clean \
   && useradd -u 1000 -G sudo -U -m ftuser \
   && chown ftuser:ftuser /freqtrade \
@@ -37,6 +37,7 @@ ENV LD_LIBRARY_PATH /usr/local/lib
 COPY --chown=ftuser:ftuser requirements.txt /freqtrade/
 USER ftuser
 RUN  pip install --user --no-cache-dir numpy \
+  && pip install --user /tmp/pyarrow-*.whl \
   && pip install --user --no-cache-dir -r requirements.txt
 
 # Copy dependencies to runtime-image

docs/configuration.md

@@ -215,16 +215,18 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`. <br> **Datatype:** float
 | `telegram.reload` | Allow "reload" buttons on telegram messages. <br>*Defaults to `True`.<br> **Datatype:** boolean
 | `telegram.notification_settings.*` | Detailed notification settings. Refer to the [telegram documentation](telegram-usage.md) for details.<br> **Datatype:** dictionary
+| `telegram.allow_custom_messages` | Enable the sending of Telegram messages from strategies via the dataprovider.send_msg() function. <br> **Datatype:** Boolean
 | | **Webhook**
 | `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
 | `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookentry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookentrycancel` | Payload to send on entry order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookentryfill` | Payload to send on entry order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookexit` | Payload to send on exit. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookexitcancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookexitfill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.entry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.entry_cancel` | Payload to send on entry order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.entry_fill` | Payload to send on entry order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.exit` | Payload to send on exit. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.exit_cancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.exit_fill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.status` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.allow_custom_messages` | Enable the sending of Webhook messages from strategies via the dataprovider.send_msg() function. <br> **Datatype:** Boolean
 | | **Rest API / FreqUI / Producer-Consumer**
 | `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
 | `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4

docs/deprecated.md

@@ -66,11 +66,11 @@ We will keep a compatibility layer for 1-2 versions (so both `buy_tag` and `ente
 
 #### Naming changes
 
-Webhook terminology changed from "sell" to "exit", and from "buy" to "entry".
+Webhook terminology changed from "sell" to "exit", and from "buy" to "entry", removing "webhook" in the process.
 
-* `webhookbuy` -> `webhookentry`
-* `webhookbuyfill` -> `webhookentryfill`
-* `webhookbuycancel` -> `webhookentrycancel`
-* `webhooksell` -> `webhookexit`
-* `webhooksellfill` -> `webhookexitfill`
-* `webhooksellcancel` -> `webhookexitcancel`
+* `webhookbuy`, `webhookentry` -> `entry`
+* `webhookbuyfill`, `webhookentryfill` -> `entry_fill`
+* `webhookbuycancel`, `webhookentrycancel` -> `entry_cancel`
+* `webhooksell`, `webhookexit` -> `exit`
+* `webhooksellfill`, `webhookexitfill` -> `exit_fill`
+* `webhooksellcancel`, `webhookexitcancel` -> `exit_cancel`

docs/freqai-configuration.md

@@ -192,11 +192,11 @@ dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std
 dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
 ```
 
-To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
+To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_predictions_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
 
 ```json
     "freqai": {
-        "fit_live_prediction_candles": 300,
+        "fit_live_predictions_candles": 300,
     }
 ```
 
@@ -204,14 +204,44 @@ If this value is set, FreqAI will initially use the predictions from the trainin
 
 ## Using different prediction models
 
-FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures.
+FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `CatBoost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`.
 
-### Setting classifier targets
+Regression and classification models differ in what targets they predict - a regression model will predict a target of continuous values, for example what price BTC will be at tomorrow, whilst a classifier will predict a target of discrete values, for example if the price of BTC will go up tomorrow or not. This means that you have to specify your targets differently depending on which model type you are using (see details [below](#setting-model-targets)).
 
-FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example:
+All of the aforementioned model libraries implement gradient boosted decision tree algorithms. They all work on the principle of ensemble learning, where predictions from multiple simple learners are combined to get a final prediction that is more stable and generalized. The simple learners in this case are decision trees. Gradient boosting refers to the method of learning, where each simple learner is built in sequence - the subsequent learner is used to improve on the error from the previous learner. If you want to learn more about the different model libraries you can find the information in their respective docs:
+
+* CatBoost: https://catboost.ai/en/docs/
+* LightGBM: https://lightgbm.readthedocs.io/en/v3.3.2/#
+* XGBoost: https://xgboost.readthedocs.io/en/stable/#
+
+There are also numerous online articles describing and comparing the algorithms. Some relatively light-weight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.
+
+Apart from the models already available in FreqAI, it is also possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to customize various aspects of the training procedures. You can place custom FreqAI models in `user_data/freqaimodels` - and freqtrade will pick them up from there based on the provided `--freqaimodel` name - which has to correspond to the class name of your custom model.
+Make sure to use unique names to avoid overriding built-in models.
+
+### Setting model targets
+
+#### Regressors
+
+If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the `CatboostRegressor`via the flag `--freqaimodel CatboostRegressor`. An example of how you could set a regression target for predicting the price 100 candles into the future would be
+
+```python
+df['&s-close_price'] = df['close'].shift(-100)
+```
+
+If you want to predict multiple targets, you need to define multiple labels using the same syntax as shown above.
+
+#### Classifiers
+
+If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set 
 
 ```python
 df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
 ```
 
-Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column.
+If you want to predict multiple targets you must specify all labels in the same label column. You could, for example, add the label `same` to define where the price was unchanged by setting
+
+```python
+df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
+df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down'])
+```

docs/freqai-parameter-table.md

@@ -37,12 +37,13 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
 | `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
 | `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
+| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
 |  |  **Data split parameters**
 | `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
 | `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
 | `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
 |  |  **Model training parameters**
-| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model.  <br> **Datatype:** Dictionary.
+| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. A list of the currently available models can be found [here](freqai-configuration.md#using-different-prediction-models).  <br> **Datatype:** Dictionary.
 | `n_estimators` | The number of boosted trees to fit in the training of the model. <br> **Datatype:** Integer.
 | `learning_rate` | Boosting learning rate during training of the model. <br> **Datatype:** Float.
 | `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.

docs/freqai-running.md

@@ -142,6 +142,19 @@ dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1
 
 This specific hyperopt would help you understand the appropriate `DI_values` for your particular parameter space.
 
+## Using Tensorboard
+
+CatBoost models benefit from tracking training metrics via Tensorboard. You can take advantage of the FreqAI integration to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
+
+```bash
+cd freqtrade
+tensorboard --logdir user_data/models/unique-id
+```
+
+where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell if you wish to view the output in your browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
+
+![tensorboard](assets/tensorboard.jpg)
+
 ## Setting up a follower
 
 You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining:

docs/requirements-docs.txt

@@ -1,5 +1,5 @@
 markdown==3.3.7
-mkdocs==1.4.0
+mkdocs==1.4.1
 mkdocs-material==8.5.6
 mdx_truly_sane_lists==1.3
 pymdown-extensions==9.6

docs/stoploss.md

@@ -87,7 +87,7 @@ At this stage the bot contains the following stoploss support modes:
 2. Trailing stop loss.
 3. Trailing stop loss, custom positive loss.
 4. Trailing stop loss only once the trade has reached a certain offset.
-5. [Custom stoploss function](strategy-advanced.md#custom-stoploss)
+5. [Custom stoploss function](strategy-callbacks.md#custom-stoploss)
 
 ### Static Stop Loss
 

docs/strategy-customization.md

@@ -655,13 +655,13 @@ This is where calling `self.dp.current_whitelist()` comes in handy.
 # fetch live / historical candle (OHLCV) data for the first informative pair
 inf_pair, inf_timeframe = self.informative_pairs()[0]
 informative = self.dp.get_pair_dataframe(pair=inf_pair,
-                                            timeframe=inf_timeframe)
+                                         timeframe=inf_timeframe)
 ```
 
 !!! Warning "Warning about backtesting"
-    Be careful when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
-    for the backtesting runmode) provides the full time-range in one go,
-    so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode.
+    In backtesting, `dp.get_pair_dataframe()` behavior differs depending on where it's called.
+    Within `populate_*()` methods, `dp.get_pair_dataframe()` returns the full timerange. Please make sure to not "look into the future" to avoid surprises when running in dry/live mode.
+    Within [callbacks](strategy-callbacks.md), you'll get the full timerange up to the current (simulated) candle.
 
 ### *get_analyzed_dataframe(pair, timeframe)*
 
@@ -670,13 +670,13 @@ It can also be used in specific callbacks to get the signal that caused the acti
 
 ``` python
 # fetch current dataframe
-if self.dp.runmode.value in ('live', 'dry_run'):
-    dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
-                                                                timeframe=self.timeframe)
+dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
+                                                         timeframe=self.timeframe)
 ```
 
 !!! Note "No data available"
     Returns an empty dataframe if the requested pair was not cached.
+    You can check for this with `if dataframe.empty:` and handle this case accordingly.
     This should not happen when using whitelisted pairs.
 
 ### *orderbook(pair, maximum)*

docs/strategy_migration.md

@@ -43,19 +43,25 @@ Note : `forcesell`, `forcebuy`, `emergencysell` are changed to `force_exit`, `fo
   * `order_time_in_force` buy -> entry, sell -> exit.
   * `order_types` buy -> entry, sell -> exit.
   * `unfilledtimeout` buy -> entry, sell -> exit.
+  * `ignore_buying_expired_candle_after` -> moved to root level instead of "ask_strategy/exit_pricing"
 * Terminology changes
   * Sell reasons changed to reflect the new naming of "exit" instead of sells. Be careful in your strategy if you're using `exit_reason` checks and eventually update your strategy.
     * `sell_signal` -> `exit_signal`
     * `custom_sell` -> `custom_exit`
     * `force_sell` -> `force_exit`
     * `emergency_sell` -> `emergency_exit`
+  * Order pricing
+    * `bid_strategy` -> `entry_pricing`
+    * `ask_strategy` -> `exit_pricing`
+    * `ask_last_balance` -> `price_last_balance`
+    * `bid_last_balance` -> `price_last_balance`
   * Webhook terminology changed from "sell" to "exit", and from "buy" to entry
-    * `webhookbuy` -> `webhookentry`
-    * `webhookbuyfill` -> `webhookentryfill`
-    * `webhookbuycancel` -> `webhookentrycancel`
-    * `webhooksell` -> `webhookexit`
-    * `webhooksellfill` -> `webhookexitfill`
-    * `webhooksellcancel` -> `webhookexitcancel`
+    * `webhookbuy` -> `entry`
+    * `webhookbuyfill` -> `entry_fill`
+    * `webhookbuycancel` -> `entry_cancel`
+    * `webhooksell` -> `exit`
+    * `webhooksellfill` -> `exit_fill`
+    * `webhooksellcancel` -> `exit_cancel`
   * Telegram notification settings
     * `buy` -> `entry`
     * `buy_fill` -> `entry_fill`
@@ -443,6 +449,7 @@ Please refer to the [pricing documentation](configuration.md#prices-used-for-ord
         "use_order_book": true,
         "order_book_top": 1,
         "bid_last_balance": 0.0
+        "ignore_buying_expired_candle_after": 120
     }
 }
 ```
@@ -466,6 +473,7 @@ after:
         "use_order_book": true,
         "order_book_top": 1,
         "price_last_balance": 0.0
-    }
+    },
+    "ignore_buying_expired_candle_after": 120
 }
 ```

docs/telegram-usage.md

@@ -77,6 +77,7 @@ Example configuration showing the different settings:
     "enabled": true,
     "token": "your_telegram_token",
     "chat_id": "your_telegram_chat_id",
+    "allow_custom_messages": true,
     "notification_settings": {
         "status": "silent",
         "warning": "on",
@@ -115,6 +116,7 @@ Example configuration showing the different settings:
 `show_candle` - show candle values as part of entry/exit messages. Only possible values are `"ohlc"` or `"off"`.
 
 `balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
+`allow_custom_messages` completely disable strategy messages.
 `reload` allows you to disable reload-buttons on selected messages.
 
 ## Create a custom keyboard (command shortcut buttons)

docs/utils.md

@@ -169,6 +169,43 @@ Example: Search dedicated strategy path.
 freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
 ```
 
+## List freqAI models
+
+Use the `list-freqaimodels` subcommand to see all freqAI models available.
+
+This subcommand is useful for finding problems in your environment with loading freqAI models: modules with models that contain errors and failed to load are printed in red (LOAD FAILED), while models with duplicate names are printed in yellow (DUPLICATE NAME).
+
+```
+usage: freqtrade list-freqaimodels [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                   [-d PATH] [--userdir PATH]
+                                   [--freqaimodel-path PATH] [-1] [--no-color]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+  -1, --one-column      Print output in one column.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
+
 ## List Exchanges
 
 Use the `list-exchanges` subcommand to see the exchanges available for the bot.

docs/webhook-config.md

@@ -10,37 +10,37 @@ Sample configuration (tested using IFTTT).
   "webhook": {
         "enabled": true,
         "url": "https://maker.ifttt.com/trigger/<YOUREVENT>/with/key/<YOURKEY>/",
-        "webhookentry": {
+        "entry": {
             "value1": "Buying {pair}",
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}"
         },
-        "webhookentrycancel": {
+        "entry_cancel": {
             "value1": "Cancelling Open Buy Order for {pair}",
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}"
         },
-         "webhookentryfill": {
+         "entry_fill": {
             "value1": "Buy Order for {pair} filled",
             "value2": "at {open_rate:8f}",
             "value3": ""
         },
-        "webhookexit": {
+        "exit": {
             "value1": "Exiting {pair}",
             "value2": "limit {limit:8f}",
             "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
-        "webhookexitcancel": {
+        "exit_cancel": {
             "value1": "Cancelling Open Exit Order for {pair}",
             "value2": "limit {limit:8f}",
             "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
-        "webhookexitfill": {
+        "exit_fill": {
             "value1": "Exit Order for {pair} filled",
             "value2": "at {close_rate:8f}.",
             "value3": ""
         },
-        "webhookstatus": {
+        "status": {
             "value1": "Status: {status}",
             "value2": "",
             "value3": ""
@@ -57,7 +57,7 @@ You can set the POST body format to Form-Encoded (default), JSON-Encoded, or raw
         "enabled": true,
         "url": "https://<YOURSUBDOMAIN>.cloud.mattermost.com/hooks/<YOURHOOK>",
         "format": "json",
-        "webhookstatus": {
+        "status": {
             "text": "Status: {status}"
         }
     },
@@ -88,17 +88,30 @@ Optional parameters are available to enable automatic retries for webhook messag
         "url": "https://<YOURHOOKURL>",
         "retries": 3,
         "retry_delay": 0.2,
-        "webhookstatus": {
+        "status": {
             "status": "Status: {status}"
         }
     },
 ```
 
+Custom messages can be sent to Webhook endpoints via the `self.dp.send_msg()` function from within the strategy. To enable this, set the `allow_custom_messages` option to `true`:
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://<YOURHOOKURL>",
+        "allow_custom_messages": true,
+        "strategy_msg": {
+            "status": "StrategyMessage: {msg}"
+        }
+    },
+```
+
 Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
 
-### Webhookentry
+### Entry
 
-The fields in `webhook.webhookentry` are filled when the bot executes a long/short. Parameters are filled using string.format.
+The fields in `webhook.entry` are filled when the bot executes a long/short. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -118,9 +131,9 @@ Possible parameters are:
 * `current_rate`
 * `enter_tag`
 
-### Webhookentrycancel
+### Entry cancel
 
-The fields in `webhook.webhookentrycancel` are filled when the bot cancels a long/short order. Parameters are filled using string.format.
+The fields in `webhook.entry_cancel` are filled when the bot cancels a long/short order. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -139,9 +152,9 @@ Possible parameters are:
 * `current_rate`
 * `enter_tag`
 
-### Webhookentryfill
+### Entry fill
 
-The fields in `webhook.webhookentryfill` are filled when the bot filled a long/short order. Parameters are filled using string.format.
+The fields in `webhook.entry_fill` are filled when the bot filled a long/short order. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -160,9 +173,9 @@ Possible parameters are:
 * `current_rate`
 * `enter_tag`
 
-### Webhookexit
+### Exit
 
-The fields in `webhook.webhookexit` are filled when the bot exits a trade. Parameters are filled using string.format.
+The fields in `webhook.exit` are filled when the bot exits a trade. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -184,9 +197,9 @@ Possible parameters are:
 * `open_date`
 * `close_date`
 
-### Webhookexitfill
+### Exit fill
 
-The fields in `webhook.webhookexitfill` are filled when the bot fills a exit order (closes a Trade). Parameters are filled using string.format.
+The fields in `webhook.exit_fill` are filled when the bot fills a exit order (closes a Trade). Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -209,9 +222,9 @@ Possible parameters are:
 * `open_date`
 * `close_date`
 
-### Webhookexitcancel
+### Exit cancel
 
-The fields in `webhook.webhookexitcancel` are filled when the bot cancels a exit order. Parameters are filled using string.format.
+The fields in `webhook.exit_cancel` are filled when the bot cancels a exit order. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -234,9 +247,9 @@ Possible parameters are:
 * `open_date`
 * `close_date`
 
-### Webhookstatus
+### Status
 
-The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
+The fields in `webhook.status` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
 
 The only possible value here is `{status}`.
 
@@ -280,7 +293,6 @@ You can configure this as follows:
 }
 ```
 
-
 The above represents the default (`exit_fill` and `entry_fill` are optional and will default to the above configuration) - modifications are obviously possible.
 
 Available fields correspond to the fields for webhooks and are documented in the corresponding webhook sections.
@@ -288,3 +300,13 @@ Available fields correspond to the fields for webhooks and are documented in the
 The notifications will look as follows by default.
 
 ![discord-notification](assets/discord_notification.png)
+
+Custom messages can be sent from a strategy to Discord endpoints via the dataprovider.send_msg() function. To enable this, set the `allow_custom_messages` option to `true`:
+
+```json
+  "discord": {
+        "enabled": true,
+        "webhook_url": "https://discord.com/api/webhooks/<Your webhook URL ...>",
+        "allow_custom_messages": true,
+    },
+```

freqtrade/__init__.py

@@ -16,6 +16,6 @@ if 'dev' in __version__:
             from pathlib import Path
             versionfile = Path('./freqtrade_commit')
             if versionfile.is_file():
-                __version__ = f"docker-{versionfile.read_text()[:8]}"
+                __version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"
         except Exception:
             pass

freqtrade/commands/__init__.py

@@ -15,9 +15,9 @@ from freqtrade.commands.db_commands import start_convert_db
 from freqtrade.commands.deploy_commands import (start_create_userdir, start_install_ui,
                                                 start_new_strategy)
 from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show
-from freqtrade.commands.list_commands import (start_list_exchanges, start_list_markets,
-                                              start_list_strategies, start_list_timeframes,
-                                              start_show_trades)
+from freqtrade.commands.list_commands import (start_list_exchanges, start_list_freqAI_models,
+                                              start_list_markets, start_list_strategies,
+                                              start_list_timeframes, start_show_trades)
 from freqtrade.commands.optimize_commands import (start_backtesting, start_backtesting_show,
                                                   start_edge, start_hyperopt)
 from freqtrade.commands.pairlist_commands import start_test_pairlist

freqtrade/commands/arguments.py

@@ -41,6 +41,8 @@ ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
 ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized",
                         "recursive_strategy_search"]
 
+ARGS_LIST_FREQAIMODELS = ["freqaimodel_path", "print_one_column", "print_colorized"]
+
 ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
 
 ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list"]
@@ -106,8 +108,8 @@ ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason
                               "exit_reason_list", "indicator_list"]
 
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
-                    "list-markets", "list-pairs", "list-strategies", "list-data",
-                    "hyperopt-list", "hyperopt-show", "backtest-filter",
+                    "list-markets", "list-pairs", "list-strategies", "list-freqaimodels",
+                    "list-data", "hyperopt-list", "hyperopt-show", "backtest-filter",
                     "plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv"]
 
 NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
@@ -192,10 +194,11 @@ class Arguments:
                                         start_create_userdir, start_download_data, start_edge,
                                         start_hyperopt, start_hyperopt_list, start_hyperopt_show,
                                         start_install_ui, start_list_data, start_list_exchanges,
-                                        start_list_markets, start_list_strategies,
-                                        start_list_timeframes, start_new_config, start_new_strategy,
-                                        start_plot_dataframe, start_plot_profit, start_show_trades,
-                                        start_test_pairlist, start_trading, start_webserver)
+                                        start_list_freqAI_models, start_list_markets,
+                                        start_list_strategies, start_list_timeframes,
+                                        start_new_config, start_new_strategy, start_plot_dataframe,
+                                        start_plot_profit, start_show_trades, start_test_pairlist,
+                                        start_trading, start_webserver)
 
         subparsers = self.parser.add_subparsers(dest='command',
                                                 # Use custom message when no subhandler is added
@@ -362,6 +365,15 @@ class Arguments:
         list_strategies_cmd.set_defaults(func=start_list_strategies)
         self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
 
+        # Add list-freqAI Models subcommand
+        list_freqaimodels_cmd = subparsers.add_parser(
+            'list-freqaimodels',
+            help='Print available freqAI models.',
+            parents=[_common_parser],
+        )
+        list_freqaimodels_cmd.set_defaults(func=start_list_freqAI_models)
+        self._build_args(optionlist=ARGS_LIST_FREQAIMODELS, parser=list_freqaimodels_cmd)
+
         # Add list-timeframes subcommand
         list_timeframes_cmd = subparsers.add_parser(
             'list-timeframes',

freqtrade/commands/list_commands.py

@@ -1,7 +1,6 @@
 import csv
 import logging
 import sys
-from pathlib import Path
 from typing import Any, Dict, List
 
 import rapidjson
@@ -10,7 +9,6 @@ from colorama import init as colorama_init
 from tabulate import tabulate
 
 from freqtrade.configuration import setup_utils_configuration
-from freqtrade.constants import USERPATH_STRATEGIES
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import market_is_active, validate_exchanges
@@ -41,7 +39,7 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
         print(tabulate(exchanges, headers=['Exchange name', 'Valid', 'reason']))
 
 
-def _print_objs_tabular(objs: List, print_colorized: bool, base_dir: Path) -> None:
+def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
     if print_colorized:
         colorama_init(autoreset=True)
         red = Fore.RED
@@ -55,7 +53,7 @@ def _print_objs_tabular(objs: List, print_colorized: bool, base_dir: Path) -> No
     names = [s['name'] for s in objs]
     objs_to_print = [{
         'name': s['name'] if s['name'] else "--",
-        'location': s['location'].relative_to(base_dir),
+        'location': s['location_rel'],
         'status': (red + "LOAD FAILED" + reset if s['class'] is None
                    else "OK" if names.count(s['name']) == 1
                    else yellow + "DUPLICATE NAME" + reset)
@@ -76,9 +74,8 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
     """
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
-    directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
     strategy_objs = StrategyResolver.search_all_objects(
-        directory, not args['print_one_column'], config.get('recursive_strategy_search', False))
+        config, not args['print_one_column'], config.get('recursive_strategy_search', False))
     # Sort alphabetically
     strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
     for obj in strategy_objs:
@@ -90,7 +87,22 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
     if args['print_one_column']:
         print('\n'.join([s['name'] for s in strategy_objs]))
     else:
-        _print_objs_tabular(strategy_objs, config.get('print_colorized', False), directory)
+        _print_objs_tabular(strategy_objs, config.get('print_colorized', False))
+
+
+def start_list_freqAI_models(args: Dict[str, Any]) -> None:
+    """
+    Print files with FreqAI models custom classes available in the directory
+    """
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+    from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
+    model_objs = FreqaiModelResolver.search_all_objects(config, not args['print_one_column'])
+    # Sort alphabetically
+    model_objs = sorted(model_objs, key=lambda x: x['name'])
+    if args['print_one_column']:
+        print('\n'.join([s['name'] for s in model_objs]))
+    else:
+        _print_objs_tabular(model_objs, config.get('print_colorized', False))
 
 
 def start_list_timeframes(args: Dict[str, Any]) -> None:

freqtrade/configuration/config_validation.py

@@ -86,6 +86,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
     _validate_unlimited_amount(conf)
     _validate_ask_orderbook(conf)
     _validate_freqai_hyperopt(conf)
+    _validate_freqai_include_timeframes(conf)
     _validate_consumers(conf)
     validate_migrated_strategy_settings(conf)
 
@@ -334,6 +335,26 @@ def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
             'Using analyze-per-epoch parameter is not supported with a FreqAI strategy.')
 
 
+def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
+    freqai_enabled = conf.get('freqai', {}).get('enabled', False)
+    if freqai_enabled:
+        main_tf = conf.get('timeframe', '5m')
+        freqai_include_timeframes = conf.get('freqai', {}).get('feature_parameters', {}
+                                                               ).get('include_timeframes', [])
+
+        from freqtrade.exchange import timeframe_to_seconds
+        main_tf_s = timeframe_to_seconds(main_tf)
+        offending_lines = []
+        for tf in freqai_include_timeframes:
+            tf_s = timeframe_to_seconds(tf)
+            if tf_s < main_tf_s:
+                offending_lines.append(tf)
+        if offending_lines:
+            raise OperationalException(
+                f"Main timeframe of {main_tf} must be smaller or equal to FreqAI "
+                f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
+
+
 def _validate_consumers(conf: Dict[str, Any]) -> None:
     emc_conf = conf.get('external_message_consumer', {})
     if emc_conf.get('enabled', False):

freqtrade/configuration/directory_operations.py

@@ -3,7 +3,8 @@ import shutil
 from pathlib import Path
 from typing import Optional
 
-from freqtrade.constants import USER_DATA_FILES, Config
+from freqtrade.constants import (USER_DATA_FILES, USERPATH_FREQAIMODELS, USERPATH_HYPEROPTS,
+                                 USERPATH_NOTEBOOKS, USERPATH_STRATEGIES, Config)
 from freqtrade.exceptions import OperationalException
 
 
@@ -49,8 +50,8 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
     :param create_dir: Create directory if it does not exist.
     :return: Path object containing the directory
     """
-    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
-                "notebooks", "plot", "strategies", ]
+    sub_dirs = ["backtest_results", "data", USERPATH_HYPEROPTS, "hyperopt_results", "logs",
+                USERPATH_NOTEBOOKS, "plot", USERPATH_STRATEGIES, USERPATH_FREQAIMODELS]
     folder = Path(directory)
     chown_user_directory(folder)
     if not folder.is_dir():

freqtrade/constants.py

@@ -5,7 +5,7 @@ bot constants
 """
 from typing import Any, Dict, List, Literal, Tuple
 
-from freqtrade.enums import CandleType
+from freqtrade.enums import CandleType, RPCMessageType
 
 
 DEFAULT_CONFIG = 'config.json'
@@ -282,6 +282,7 @@ CONF_SCHEMA = {
                 'enabled': {'type': 'boolean'},
                 'token': {'type': 'string'},
                 'chat_id': {'type': 'string'},
+                'allow_custom_messages': {'type': 'boolean', 'default': True},
                 'balance_dust_level': {'type': 'number', 'minimum': 0.0},
                 'notification_settings': {
                     'type': 'object',
@@ -344,6 +345,8 @@ CONF_SCHEMA = {
                 'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'},
                 'retries': {'type': 'integer', 'minimum': 0},
                 'retry_delay': {'type': 'number', 'minimum': 0},
+                **dict([(x, {'type': 'object'}) for x in RPCMessageType]),
+                # Below -> Deprecated
                 'webhookentry': {'type': 'object'},
                 'webhookentrycancel': {'type': 'object'},
                 'webhookentryfill': {'type': 'object'},
@@ -537,6 +540,8 @@ CONF_SCHEMA = {
             "properties": {
                 "enabled": {"type": "boolean", "default": False},
                 "keras": {"type": "boolean", "default": False},
+                "write_metrics_to_disk": {"type": "boolean", "default": False},
+                "purge_old_models": {"type": "boolean", "default": True},
                 "conv_width": {"type": "integer", "default": 2},
                 "train_period_days": {"type": "integer", "default": 0},
                 "backtest_period_days": {"type": "number", "default": 7},
@@ -653,5 +658,6 @@ LongShort = Literal['long', 'short']
 EntryExit = Literal['entry', 'exit']
 BuySell = Literal['buy', 'sell']
 MakerTaker = Literal['maker', 'taker']
+BidAsk = Literal['bid', 'ask']
 
 Config = Dict[str, Any]

freqtrade/exchange/binance.py

@@ -11,6 +11,7 @@ from freqtrade.enums import CandleType, MarginMode, TradingMode
 from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.common import retrier
+from freqtrade.exchange.types import Tickers
 from freqtrade.misc import deep_merge_dicts, json_load
 
 
@@ -59,7 +60,7 @@ class Binance(Exchange):
                 )
             ))
 
-    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
+    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
         tickers = super().get_tickers(symbols=symbols, cached=cached)
         if self.trading_mode == TradingMode.FUTURES:
             # Binance's future result has no bid/ask values.

freqtrade/exchange/exchange.py

@@ -20,8 +20,8 @@ from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
 from dateutil import parser
 from pandas import DataFrame, concat
 
-from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
-                                 Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
+from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BidAsk,
+                                 BuySell, Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
                                  PairWithTimeframe)
 from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
@@ -31,6 +31,7 @@ from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFun
 from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
                                        EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
                                        remove_credentials, retrier, retrier_async)
+from freqtrade.exchange.types import Ticker, Tickers
 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
@@ -179,7 +180,7 @@ class Exchange:
             exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
 
         logger.info(f'Using Exchange "{self.name}"')
-
+        self.required_candle_call_count = 1
         if validate:
             # Initial markets load
             self._load_markets()
@@ -409,11 +410,13 @@ class Exchange:
         else:
             return DataFrame()
 
-    def get_contract_size(self, pair: str) -> float:
+    def get_contract_size(self, pair: str) -> Optional[float]:
         if self.trading_mode == TradingMode.FUTURES:
-            market = self.markets[pair]
+            market = self.markets.get(pair, {})
             contract_size: float = 1.0
-            if market['contractSize'] is not None:
+            if not market:
+                return None
+            if market.get('contractSize') is not None:
                 # ccxt has contractSize in markets as string
                 contract_size = float(market['contractSize'])
             return contract_size
@@ -1420,14 +1423,17 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
+    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
         """
         :param cached: Allow cached result
         :return: fetch_tickers result
         """
+        tickers: Tickers
+        if not self.exchange_has('fetchTickers'):
+            return {}
         if cached:
             with self._cache_lock:
-                tickers = self._fetch_tickers_cache.get('fetch_tickers')
+                tickers = self._fetch_tickers_cache.get('fetch_tickers')  # type: ignore
             if tickers:
                 return tickers
         try:
@@ -1450,12 +1456,12 @@ class Exchange:
     # Pricing info
 
     @retrier
-    def fetch_ticker(self, pair: str) -> dict:
+    def fetch_ticker(self, pair: str) -> Ticker:
         try:
             if (pair not in self.markets or
                     self.markets[pair].get('active', False) is False):
                 raise ExchangeError(f"Pair {pair} not available")
-            data = self._api.fetch_ticker(pair)
+            data: Ticker = self._api.fetch_ticker(pair)
             return data
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
@@ -1506,7 +1512,7 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
-    def _get_price_side(self, side: str, is_short: bool, conf_strategy: Dict) -> str:
+    def _get_price_side(self, side: str, is_short: bool, conf_strategy: Dict) -> BidAsk:
         price_side = conf_strategy['price_side']
 
         if price_side in ('same', 'other'):
@@ -1525,7 +1531,7 @@ class Exchange:
 
     def get_rate(self, pair: str, refresh: bool,
                  side: EntryExit, is_short: bool,
-                 order_book: Optional[dict] = None, ticker: Optional[dict] = None) -> float:
+                 order_book: Optional[dict] = None, ticker: Optional[Ticker] = None) -> float:
         """
         Calculates bid/ask target
         bid rate - between current ask price and last price
@@ -1852,7 +1858,7 @@ class Exchange:
 
     def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
                          since_ms: Optional[int], cache: bool) -> Coroutine:
-        not_all_data = self.required_candle_call_count > 1
+        not_all_data = cache and self.required_candle_call_count > 1
         if cache and (pair, timeframe, candle_type) in self._klines:
             candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
             min_date = date_minus_candles(timeframe, candle_limit - 5).timestamp()
@@ -1930,6 +1936,7 @@ class Exchange:
                 candle_limit = self.ohlcv_candle_limit(timeframe, self._config['candle_type_def'])
                 # Age out old candles
                 ohlcv_df = ohlcv_df.tail(candle_limit + self._startup_candle_count)
+                ohlcv_df = ohlcv_df.reset_index(drop=True)
                 self._klines[(pair, timeframe, c_type)] = ohlcv_df
             else:
                 self._klines[(pair, timeframe, c_type)] = ohlcv_df
@@ -2018,8 +2025,8 @@ class Exchange:
             candle_limit = self.ohlcv_candle_limit(
                 timeframe, candle_type=candle_type, since_ms=since_ms)
 
-            if candle_type != CandleType.SPOT:
-                params.update({'price': candle_type})
+            if candle_type and candle_type != CandleType.SPOT:
+                params.update({'price': candle_type.value})
             if candle_type != CandleType.FUNDING_RATE:
                 data = await self._api_async.fetch_ohlcv(
                     pair, timeframe=timeframe, since=since_ms,

freqtrade/exchange/kraken.py

@@ -12,6 +12,7 @@ from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, Invali
                                   OperationalException, TemporaryError)
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.common import retrier
+from freqtrade.exchange.types import Tickers
 
 
 logger = logging.getLogger(__name__)
@@ -45,7 +46,7 @@ class Kraken(Exchange):
         return (parent_check and
                 market.get('darkpool', False) is False)
 
-    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
+    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
         # Only fetch tickers for current stake currency
         # Otherwise the request for kraken becomes too large.
         symbols = list(self.get_markets(quote_currencies=[self._config['stake_currency']]))

freqtrade/exchange/types.py

@@ -0,0 +1,16 @@
+from typing import Dict, Optional, TypedDict
+
+
+class Ticker(TypedDict):
+    symbol: str
+    ask: Optional[float]
+    askVolume: Optional[float]
+    bid: Optional[float]
+    bidVolume: Optional[float]
+    last: Optional[float]
+    quoteVolume: Optional[float]
+    baseVolume: Optional[float]
+    # Several more - only listing required.
+
+
+Tickers = Dict[str, Ticker]

freqtrade/freqai/base_models/BaseClassifierModel.py

@@ -51,7 +51,7 @@ class BaseClassifierModel(IFreqaiModel):
                     f"{end_date} --------------------")
         # split data into train/test data.
         data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
-        if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)
@@ -78,7 +78,7 @@ class BaseClassifierModel(IFreqaiModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param: unfiltered_df: Full dataframe for the current backtest period.
+        :param unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

freqtrade/freqai/base_models/BaseRegressionModel.py

@@ -50,7 +50,7 @@ class BaseRegressionModel(IFreqaiModel):
                     f"{end_date} --------------------")
         # split data into train/test data.
         data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
-        if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)
@@ -77,7 +77,7 @@ class BaseRegressionModel(IFreqaiModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param: unfiltered_df: Full dataframe for the current backtest period.
+        :param unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

freqtrade/freqai/base_models/BaseTensorFlowModel.py

@@ -47,7 +47,7 @@ class BaseTensorFlowModel(IFreqaiModel):
                     f"{end_date} --------------------")
         # split data into train/test data.
         data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
-        if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)

freqtrade/freqai/data_drawer.py

@@ -1,14 +1,15 @@
 import collections
-import json
 import logging
 import re
 import shutil
 import threading
+from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Dict, Tuple, TypedDict
 
 import numpy as np
 import pandas as pd
+import psutil
 import rapidjson
 from joblib import dump, load
 from joblib.externals import cloudpickle
@@ -65,6 +66,8 @@ class FreqaiDataDrawer:
         self.pair_dict: Dict[str, pair_info] = {}
         # dictionary holding all actively inferenced models in memory given a model filename
         self.model_dictionary: Dict[str, Any] = {}
+        # all additional metadata that we want to keep in ram
+        self.meta_data_dictionary: Dict[str, Dict[str, Any]] = {}
         self.model_return_values: Dict[str, DataFrame] = {}
         self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
         self.historic_predictions: Dict[str, DataFrame] = {}
@@ -78,30 +81,60 @@ class FreqaiDataDrawer:
         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.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
         self.follow_mode = follow_mode
         if follow_mode:
             self.create_follower_dict()
         self.load_drawer_from_disk()
         self.load_historic_predictions_from_disk()
+        self.load_metric_tracker_from_disk()
         self.training_queue: Dict[str, int] = {}
         self.history_lock = threading.Lock()
         self.save_lock = threading.Lock()
         self.pair_dict_lock = threading.Lock()
+        self.metric_tracker_lock = threading.Lock()
         self.old_DBSCAN_eps: Dict[str, float] = {}
         self.empty_pair_dict: pair_info = {
                 "model_filename": "", "trained_timestamp": 0,
                 "data_path": "", "extras": {}}
+        self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
+
+    def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
+        """
+        General utility for adding and updating custom metrics. Typically used
+        for adding training performance, train timings, inferenc timings, cpu loads etc.
+        """
+        with self.metric_tracker_lock:
+            if pair not in self.metric_tracker:
+                self.metric_tracker[pair] = {}
+            if metric not in self.metric_tracker[pair]:
+                self.metric_tracker[pair][metric] = {'timestamp': [], 'value': []}
+
+            timestamp = int(datetime.now(timezone.utc).timestamp())
+            self.metric_tracker[pair][metric]['value'].append(value)
+            self.metric_tracker[pair][metric]['timestamp'].append(timestamp)
+
+    def collect_metrics(self, time_spent: float, pair: str):
+        """
+        Add metrics to the metric tracker dictionary
+        """
+        load1, load5, load15 = psutil.getloadavg()
+        cpus = psutil.cpu_count()
+        self.update_metric_tracker('train_time', time_spent, pair)
+        self.update_metric_tracker('cpu_load1min', load1 / cpus, pair)
+        self.update_metric_tracker('cpu_load5min', load5 / cpus, pair)
+        self.update_metric_tracker('cpu_load15min', load15 / cpus, pair)
 
     def load_drawer_from_disk(self):
         """
         Locate and load a previously saved data drawer full of all pair model metadata in
         present model folder.
-        :return: bool - whether or not the drawer was located
+        Load any existing metric tracker that may be present.
         """
         exists = self.pair_dictionary_path.is_file()
         if exists:
             with open(self.pair_dictionary_path, "r") as fp:
-                self.pair_dict = json.load(fp)
+                self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
         elif not self.follow_mode:
             logger.info("Could not find existing datadrawer, starting from scratch")
         else:
@@ -110,7 +143,18 @@ class FreqaiDataDrawer:
                 "sending null values back to strategy"
             )
 
-        return exists
+    def load_metric_tracker_from_disk(self):
+        """
+        Tries to load an existing metrics dictionary if the user
+        wants to collect metrics.
+        """
+        if self.freqai_info.get('write_metrics_to_disk', False):
+            exists = self.metric_tracker_path.is_file()
+            if exists:
+                with open(self.metric_tracker_path, "r") as fp:
+                    self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
+            else:
+                logger.info("Could not find existing metric tracker, starting from scratch")
 
     def load_historic_predictions_from_disk(self):
         """
@@ -146,7 +190,7 @@ class FreqaiDataDrawer:
 
     def save_historic_predictions_to_disk(self):
         """
-        Save data drawer full of all pair model metadata in present model folder.
+        Save historic predictions pickle to disk
         """
         with open(self.historic_predictions_path, "wb") as fp:
             cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
@@ -154,6 +198,15 @@ class FreqaiDataDrawer:
         # create a backup
         shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
 
+    def save_metric_tracker_to_disk(self):
+        """
+        Save metric tracker of all pair metrics collected.
+        """
+        with self.save_lock:
+            with open(self.metric_tracker_path, 'w') as fp:
+                rapidjson.dump(self.metric_tracker, fp, default=self.np_encoder,
+                               number_mode=rapidjson.NM_NATIVE)
+
     def save_drawer_to_disk(self):
         """
         Save data drawer full of all pair model metadata in present model folder.
@@ -412,9 +465,8 @@ class FreqaiDataDrawer:
     def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
         """
         Saves all data associated with a model for a single sub-train time range
-        :params:
-        :model: User trained model which can be reused for inferencing to generate
-        predictions
+        :param model: User trained model which can be reused for inferencing to generate
+                      predictions
         """
 
         if not dk.data_path.is_dir():
@@ -454,9 +506,14 @@ class FreqaiDataDrawer:
             )
 
         # if self.live:
+        # store as much in ram as possible to increase performance
         self.model_dictionary[coin] = model
         self.pair_dict[coin]["model_filename"] = dk.model_filename
         self.pair_dict[coin]["data_path"] = str(dk.data_path)
+        if coin not in self.meta_data_dictionary:
+            self.meta_data_dictionary[coin] = {}
+        self.meta_data_dictionary[coin]["train_df"] = dk.data_dictionary["train_features"]
+        self.meta_data_dictionary[coin]["meta_data"] = dk.data
         self.save_drawer_to_disk()
 
         return
@@ -467,7 +524,7 @@ class FreqaiDataDrawer:
         presaved backtesting (prediction file loading).
         """
         with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
-            dk.data = json.load(fp)
+            dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
             dk.training_features_list = dk.data["training_features_list"]
             dk.label_list = dk.data["label_list"]
 
@@ -493,14 +550,19 @@ class FreqaiDataDrawer:
                     / dk.data_path.parts[-1]
                 )
 
-        with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
-            dk.data = json.load(fp)
-            dk.training_features_list = dk.data["training_features_list"]
-            dk.label_list = dk.data["label_list"]
+        if coin in self.meta_data_dictionary:
+            dk.data = self.meta_data_dictionary[coin]["meta_data"]
+            dk.data_dictionary["train_features"] = self.meta_data_dictionary[coin]["train_df"]
+        else:
+            with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
+                dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
 
-        dk.data_dictionary["train_features"] = pd.read_pickle(
-            dk.data_path / f"{dk.model_filename}_trained_df.pkl.bz2"
-        )
+            dk.data_dictionary["train_features"] = pd.read_pickle(
+                dk.data_path / f"{dk.model_filename}_trained_df.pkl.bz2"
+            )
+
+        dk.training_features_list = dk.data["training_features_list"]
+        dk.label_list = dk.data["label_list"]
 
         # try to access model in memory instead of loading object from disk to save time
         if dk.live and coin in self.model_dictionary:
@@ -532,8 +594,7 @@ class FreqaiDataDrawer:
         Append new candles to our stores historic data (in memory) so that
         we do not need to load candle history from disk and we dont need to
         pinging exchange multiple times for the same candle.
-        :params:
-        dataframe: DataFrame = strategy provided dataframe
+        :param dataframe: DataFrame = strategy provided dataframe
         """
         feat_params = self.freqai_info["feature_parameters"]
         with self.history_lock:
@@ -579,9 +640,8 @@ class FreqaiDataDrawer:
         """
         Load pair histories for all whitelist and corr_pairlist pairs.
         Only called once upon startup of bot.
-        :params:
-        timerange: TimeRange = full timerange required to populate all indicators
-        for training according to user defined train_period_days
+        :param timerange: TimeRange = full timerange required to populate all indicators
+                          for training according to user defined train_period_days
         """
         history_data = self.historic_data
 
@@ -604,10 +664,9 @@ class FreqaiDataDrawer:
         """
         Searches through our historic_data in memory and returns the dataframes relevant
         to the present pair.
-        :params:
-        timerange: TimeRange = full timerange required to populate all indicators
-        for training according to user defined train_period_days
-        metadata: dict = strategy furnished pair metadata
+        :param timerange: TimeRange = full timerange required to populate all indicators
+                          for training according to user defined train_period_days
+        :param metadata: dict = strategy furnished pair metadata
         """
         with self.history_lock:
             corr_dataframes: Dict[Any, Any] = {}
@@ -631,22 +690,3 @@ class FreqaiDataDrawer:
                         ).reset_index(drop=True)
 
         return corr_dataframes, base_dataframes
-
-    # to be used if we want to send predictions directly to the follower instead of forcing
-    # follower to load models and inference
-    # def save_model_return_values_to_disk(self) -> None:
-    #     with open(self.full_path / str('model_return_values.json'), "w") as fp:
-    #         json.dump(self.model_return_values, fp, default=self.np_encoder)
-
-    # def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
-    #     exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
-    #     if exists:
-    #         with open(self.full_path / str('model_return_values.json'), "r") as fp:
-    #             self.model_return_values = json.load(fp)
-    #     elif not self.follow_mode:
-    #         logger.info("Could not find existing datadrawer, starting from scratch")
-    #     else:
-    #         logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
-    #                        'sending null values back to strategy')
-
-    #     return exists, dk

freqtrade/freqai/data_kitchen.py

@@ -107,9 +107,8 @@ class FreqaiDataKitchen:
     ) -> None:
         """
         Set the paths to the data for the present coin/botloop
-        :params:
-        metadata: dict = strategy furnished pair metadata
-        trained_timestamp: int = timestamp of most recent training
+        :param metadata: dict = strategy furnished pair metadata
+        :param trained_timestamp: int = timestamp of most recent training
         """
         self.full_path = Path(
             self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
@@ -129,8 +128,8 @@ class FreqaiDataKitchen:
         Given the dataframe for the full history for training, split the data into
         training and test data according to user specified parameters in configuration
         file.
-        :filtered_dataframe: cleaned dataframe ready to be split.
-        :labels: cleaned labels ready to be split.
+        :param filtered_dataframe: cleaned dataframe ready to be split.
+        :param labels: cleaned labels ready to be split.
         """
         feat_dict = self.freqai_config["feature_parameters"]
 
@@ -189,13 +188,14 @@ class FreqaiDataKitchen:
         remove all NaNs. Any row with a NaN is removed from training dataset or replaced with
         0s in the prediction dataset. However, prediction dataset do_predict will reflect any
         row that had a NaN and will shield user from that prediction.
-        :params:
-        :unfiltered_df: the full dataframe for the present training period
-        :training_feature_list: list, the training feature list constructed by
-        self.build_feature_list() according to user specified parameters in the configuration file.
-        :labels: the labels for the dataset
-        :training_filter: boolean which lets the function know if it is training data or
-        prediction data to be filtered.
+
+        :param unfiltered_df: the full dataframe for the present training period
+        :param training_feature_list: list, the training feature list constructed by
+                                      self.build_feature_list() according to user specified
+                                      parameters in the configuration file.
+        :param labels: the labels for the dataset
+        :param training_filter: boolean which lets the function know if it is training data or
+                                prediction data to be filtered.
         :returns:
         :filtered_df: dataframe cleaned of NaNs and only containing the user
         requested feature set.
@@ -241,6 +241,7 @@ class FreqaiDataKitchen:
             self.data["filter_drop_index_training"] = drop_index
 
         else:
+            filtered_df = self.check_pred_labels(filtered_df)
             # we are backtesting so we need to preserve row number to send back to strategy,
             # so now we use do_predict to avoid any prediction based on a NaN
             drop_index = pd.isnull(filtered_df).any(axis=1)
@@ -285,8 +286,8 @@ class FreqaiDataKitchen:
     def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
         """
         Normalize all data in the data_dictionary according to the training dataset
-        :params:
-        :data_dictionary: dictionary containing the cleaned and split training/test data/labels
+        :param data_dictionary: dictionary containing the cleaned and
+                                split training/test data/labels
         :returns:
         :data_dictionary: updated dictionary with standardized values.
         """
@@ -460,6 +461,24 @@ class FreqaiDataKitchen:
 
         return df
 
+    def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
+        """
+        Check that prediction feature labels match training feature labels.
+        :params:
+        :df_predictions: incoming predictions
+        """
+        train_labels = self.data_dictionary["train_features"].columns
+        pred_labels = df_predictions.columns
+        num_diffs = len(pred_labels.difference(train_labels))
+        if num_diffs != 0:
+            df_predictions = df_predictions[train_labels]
+            logger.warning(
+                f"Removed {num_diffs} features from prediction features, "
+                f"these were likely considered constant values during most recent training."
+            )
+
+        return df_predictions
+
     def principal_component_analysis(self) -> None:
         """
         Performs Principal Component Analysis on the data for dimensionality reduction
@@ -516,8 +535,7 @@ class FreqaiDataKitchen:
     def pca_transform(self, filtered_dataframe: DataFrame) -> None:
         """
         Use an existing pca transform to transform data into components
-        :params:
-        filtered_dataframe: DataFrame = the cleaned dataframe
+        :param filtered_dataframe: DataFrame = the cleaned dataframe
         """
         pca_components = self.pca.transform(filtered_dataframe)
         self.data_dictionary["prediction_features"] = pd.DataFrame(
@@ -561,8 +579,7 @@ class FreqaiDataKitchen:
         """
         Build/inference a Support Vector Machine to detect outliers
         in training data and prediction
-        :params:
-        predict: bool = If true, inference an existing SVM model, else construct one
+        :param predict: bool = If true, inference an existing SVM model, else construct one
         """
 
         if self.keras:
@@ -647,11 +664,11 @@ class FreqaiDataKitchen:
         Use DBSCAN to cluster training data and remove "noisy" data (read outliers).
         User controls this via the config param `DBSCAN_outlier_pct` which indicates the
         pct of training data that they want to be considered outliers.
-        :params:
-        predict: bool = If False (training), iterate to find the best hyper parameters to match
-        user requested outlier percent target. If True (prediction), use the parameters
-        determined from the previous training to estimate if the current prediction point
-        is an outlier.
+        :param predict: bool = If False (training), iterate to find the best hyper parameters
+                        to match user requested outlier percent target.
+                        If True (prediction), use the parameters determined from
+                        the previous training to estimate if the current prediction point
+                        is an outlier.
         """
 
         if predict:
@@ -954,6 +971,9 @@ class FreqaiDataKitchen:
             append_df[f"{label}_mean"] = self.data["labels_mean"][label]
             append_df[f"{label}_std"] = self.data["labels_std"][label]
 
+        for extra_col in self.data["extra_returns_per_train"]:
+            append_df["{extra_col}"] = self.data["extra_returns_per_train"][extra_col]
+
         append_df["do_predict"] = do_predict
         if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
             append_df["DI_values"] = self.DI_values
@@ -1132,15 +1152,13 @@ class FreqaiDataKitchen:
         prediction_dataframe: DataFrame = pd.DataFrame(),
     ) -> DataFrame:
         """
-        Use the user defined strategy for populating indicators during
-        retrain
-        :params:
-        strategy: IStrategy = user defined strategy object
-        corr_dataframes: dict = dict containing the informative pair dataframes
-        (for user defined timeframes)
-        base_dataframes: dict = dict containing the current pair dataframes
-        (for user defined timeframes)
-        metadata: dict = strategy furnished pair metadata
+        Use the user defined strategy for populating indicators during retrain
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the informative pair dataframes
+                                (for user defined timeframes)
+        :param base_dataframes: dict = dict containing the current pair dataframes
+                                (for user defined timeframes)
+        :param metadata: dict = strategy furnished pair metadata
         :returns:
         dataframe: DataFrame = dataframe containing populated indicators
         """

freqtrade/freqai/freqai_interface.py

@@ -7,7 +7,7 @@ from collections import deque
 from datetime import datetime, timezone
 from pathlib import Path
 from threading import Lock
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict, List, Literal, Tuple
 
 import numpy as np
 import pandas as pd
@@ -144,7 +144,7 @@ class IFreqaiModel(ABC):
         dataframe = dk.remove_features_from_df(dk.return_dataframe)
         self.clean_up()
         if self.live:
-            self.inference_timer('stop')
+            self.inference_timer('stop', metadata["pair"])
         return dataframe
 
     def clean_up(self):
@@ -196,16 +196,15 @@ class IFreqaiModel(ABC):
             (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
 
             dk = FreqaiDataKitchen(self.config, self.live, pair)
-            dk.set_paths(pair, trained_timestamp)
             (
                 retrain,
                 new_trained_timerange,
                 data_load_timerange,
             ) = dk.check_if_new_training_required(trained_timestamp)
-            dk.set_paths(pair, new_trained_timerange.stopts)
 
             if retrain:
                 self.train_timer('start')
+                dk.set_paths(pair, new_trained_timerange.stopts)
                 try:
                     self.extract_data_and_train_model(
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
@@ -214,12 +213,14 @@ class IFreqaiModel(ABC):
                     logger.warning(f"Training {pair} raised exception {msg.__class__.__name__}. "
                                    f"Message: {msg}, skipping.")
 
-                self.train_timer('stop')
+                self.train_timer('stop', pair)
 
                 # only rotate the queue after the first has been trained.
                 self.train_queue.rotate(-1)
 
                 self.dd.save_historic_predictions_to_disk()
+                if self.freqai_info.get('write_metrics_to_disk', False):
+                    self.dd.save_metric_tracker_to_disk()
 
     def start_backtesting(
         self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
@@ -268,9 +269,7 @@ class IFreqaiModel(ABC):
             )
 
             trained_timestamp_int = int(trained_timestamp.stopts)
-            dk.data_path = Path(
-                dk.full_path / f"sub-train-{pair.split('/')[0]}_{trained_timestamp_int}"
-                )
+            dk.set_paths(pair, trained_timestamp_int)
 
             dk.set_new_model_names(pair, trained_timestamp)
 
@@ -603,11 +602,11 @@ class IFreqaiModel(ABC):
         If the user reuses an identifier on a subsequent instance,
         this function will not be called. In that case, "real" predictions
         will be appended to the loaded set of historic predictions.
-        :param: df: DataFrame = the dataframe containing the training feature data
-        :param: model: Any = A model which was `fit` using a common library such as
-        catboost or lightgbm
-        :param: dk: FreqaiDataKitchen = object containing methods for data analysis
-        :param: pair: str = current pair
+        :param df: DataFrame = the dataframe containing the training feature data
+        :param model: Any = A model which was `fit` using a common library such as
+                      catboost or lightgbm
+        :param dk: FreqaiDataKitchen = object containing methods for data analysis
+        :param pair: str = current pair
         """
 
         self.dd.historic_predictions[pair] = pred_df
@@ -658,7 +657,7 @@ class IFreqaiModel(ABC):
 
         return
 
-    def inference_timer(self, do='start'):
+    def inference_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
         """
         Timer designed to track the cumulative time spent in FreqAI for one pass through
         the whitelist. This will check if the time spent is more than 1/4 the time
@@ -669,7 +668,10 @@ class IFreqaiModel(ABC):
             self.begin_time = time.time()
         elif do == 'stop':
             end = time.time()
-            self.inference_time += (end - self.begin_time)
+            time_spent = (end - self.begin_time)
+            if self.freqai_info.get('write_metrics_to_disk', False):
+                self.dd.update_metric_tracker('inference_time', time_spent, pair)
+            self.inference_time += time_spent
             if self.pair_it == self.total_pairs:
                 logger.info(
                     f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
@@ -680,7 +682,7 @@ class IFreqaiModel(ABC):
                 self.inference_time = 0
         return
 
-    def train_timer(self, do='start'):
+    def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
         """
         Timer designed to track the cumulative time spent training the full pairlist in
         FreqAI.
@@ -690,7 +692,11 @@ class IFreqaiModel(ABC):
             self.begin_time_train = time.time()
         elif do == 'stop':
             end = time.time()
-            self.train_time += (end - self.begin_time_train)
+            time_spent = (end - self.begin_time_train)
+            if self.freqai_info.get('write_metrics_to_disk', False):
+                self.dd.collect_metrics(time_spent, pair)
+
+            self.train_time += time_spent
             if self.pair_it_train == self.total_pairs:
                 logger.info(
                     f'Total time spent training pairlist {self.train_time:.2f} seconds')

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -1,4 +1,6 @@
 import logging
+import sys
+from pathlib import Path
 from typing import Any, Dict
 
 from catboost import CatBoostClassifier, Pool
@@ -20,9 +22,8 @@ class CatboostClassifier(BaseClassifierModel):
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :params:
-        :data_dictionary: the dictionary constructed by DataHandler to hold
-        all the training and test data/labels.
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         train_data = Pool(
@@ -30,15 +31,25 @@ class CatboostClassifier(BaseClassifierModel):
             label=data_dictionary["train_labels"],
             weight=data_dictionary["train_weights"],
         )
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            test_data = None
+        else:
+            test_data = Pool(
+                data=data_dictionary["test_features"],
+                label=data_dictionary["test_labels"],
+                weight=data_dictionary["test_weights"],
+            )
 
         cbr = CatBoostClassifier(
-            allow_writing_files=False,
+            allow_writing_files=True,
             loss_function='MultiClass',
+            train_dir=Path(dk.data_path),
             **self.model_training_parameters,
         )
 
         init_model = self.get_init_model(dk.pair)
 
-        cbr.fit(train_data, init_model=init_model)
+        cbr.fit(X=train_data, eval_set=test_data, init_model=init_model,
+                log_cout=sys.stdout, log_cerr=sys.stderr)
 
         return cbr

freqtrade/freqai/prediction_models/CatboostRegressor.py

@@ -1,4 +1,6 @@
 import logging
+import sys
+from pathlib import Path
 from typing import Any, Dict
 
 from catboost import CatBoostRegressor, Pool
@@ -41,10 +43,12 @@ class CatboostRegressor(BaseRegressionModel):
         init_model = self.get_init_model(dk.pair)
 
         model = CatBoostRegressor(
-            allow_writing_files=False,
+            allow_writing_files=True,
+            train_dir=Path(dk.data_path),
             **self.model_training_parameters,
         )
 
-        model.fit(X=train_data, eval_set=test_data, init_model=init_model)
+        model.fit(X=train_data, eval_set=test_data, init_model=init_model,
+                  log_cout=sys.stdout, log_cerr=sys.stderr)
 
         return model

freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

@@ -1,4 +1,6 @@
 import logging
+import sys
+from pathlib import Path
 from typing import Any, Dict
 
 from catboost import CatBoostRegressor, Pool
@@ -26,7 +28,8 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
         """
 
         cbr = CatBoostRegressor(
-            allow_writing_files=False,
+            allow_writing_files=True,
+            train_dir=Path(dk.data_path),
             **self.model_training_parameters,
         )
 
@@ -56,8 +59,10 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
 
         fit_params = []
         for i in range(len(eval_sets)):
-            fit_params.append(
-                {'eval_set': eval_sets[i],  'init_model': init_models[i]})
+            fit_params.append({
+                    'eval_set': eval_sets[i],  'init_model': init_models[i],
+                    'log_cout': sys.stdout, 'log_cerr': sys.stderr,
+                 })
 
         model = FreqaiMultiOutputRegressor(estimator=cbr)
         thread_training = self.freqai_info.get('multitarget_parallel_training', False)

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -20,9 +20,8 @@ class LightGBMClassifier(BaseClassifierModel):
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :params:
-        :data_dictionary: the dictionary constructed by DataHandler to hold
-        all the training and test data/labels.
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:

freqtrade/freqai/prediction_models/XGBoostClassifier.py

@@ -26,9 +26,8 @@ class XGBoostClassifier(BaseClassifierModel):
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :params:
-        :data_dictionary: the dictionary constructed by DataHandler to hold
-        all the training and test data/labels.
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         X = data_dictionary["train_features"].to_numpy()
@@ -65,7 +64,7 @@ class XGBoostClassifier(BaseClassifierModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param: unfiltered_df: Full dataframe for the current backtest period.
+        :param unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

freqtrade/freqai/prediction_models/XGBoostRFClassifier.py

@@ -0,0 +1,85 @@
+import logging
+from typing import Any, Dict, Tuple
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+from pandas import DataFrame
+from pandas.api.types import is_integer_dtype
+from sklearn.preprocessing import LabelEncoder
+from xgboost import XGBRFClassifier
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostRFClassifier(BaseClassifierModel):
+    """
+    User created prediction model. The class needs to override three necessary
+    functions, predict(), train(), fit(). The class inherits ModelHandler which
+    has its own DataHandler where data is held, saved, loaded, and managed.
+    """
+
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :params:
+        :data_dictionary: the dictionary constructed by DataHandler to hold
+        all the training and test data/labels.
+        """
+
+        X = data_dictionary["train_features"].to_numpy()
+        y = data_dictionary["train_labels"].to_numpy()[:, 0]
+
+        le = LabelEncoder()
+        if not is_integer_dtype(y):
+            y = pd.Series(le.fit_transform(y), dtype="int64")
+
+        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
+            eval_set = None
+        else:
+            test_features = data_dictionary["test_features"].to_numpy()
+            test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
+
+            if not is_integer_dtype(test_labels):
+                test_labels = pd.Series(le.transform(test_labels), dtype="int64")
+
+            eval_set = [(test_features, test_labels)]
+
+        train_weights = data_dictionary["train_weights"]
+
+        init_model = self.get_init_model(dk.pair)
+
+        model = XGBRFClassifier(**self.model_training_parameters)
+
+        model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
+                  xgb_model=init_model)
+
+        return model
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param: unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        (pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
+
+        le = LabelEncoder()
+        label = dk.label_list[0]
+        labels_before = list(dk.data['labels_std'].keys())
+        labels_after = le.fit_transform(labels_before).tolist()
+        pred_df[label] = le.inverse_transform(pred_df[label])
+        pred_df = pred_df.rename(
+            columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})
+
+        return (pred_df, dk.do_predict)

freqtrade/freqai/prediction_models/XGBoostRFRegressor.py

@@ -0,0 +1,45 @@
+import logging
+from typing import Any, Dict
+
+from xgboost import XGBRFRegressor
+
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostRFRegressor(BaseRegressionModel):
+    """
+    User created prediction model. The class needs to override three necessary
+    functions, predict(), train(), fit(). The class inherits ModelHandler which
+    has its own DataHandler where data is held, saved, loaded, and managed.
+    """
+
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
+        """
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+        else:
+            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
+            eval_weights = [data_dictionary['test_weights']]
+
+        sample_weight = data_dictionary["train_weights"]
+
+        xgb_model = self.get_init_model(dk.pair)
+
+        model = XGBRFRegressor(**self.model_training_parameters)
+
+        model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
+                  sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
+
+        return model

freqtrade/freqtradebot.py

@@ -1471,12 +1471,13 @@ class FreqtradeBot(LoggingMixin):
         )
         return cancelled
 
-    def _safe_exit_amount(self, pair: str, amount: float) -> float:
+    def _safe_exit_amount(self, trade: Trade, pair: str, amount: float) -> float:
         """
         Get sellable amount.
         Should be trade.amount - but will fall back to the available amount if necessary.
         This should cover cases where get_real_amount() was not able to update the amount
         for whatever reason.
+        :param trade: Trade we're working with
         :param pair: Pair we're trying to sell
         :param amount: amount we expect to be available
         :return: amount to sell
@@ -1495,6 +1496,7 @@ class FreqtradeBot(LoggingMixin):
             return amount
         elif wallet_amount > amount * 0.98:
             logger.info(f"{pair} - Falling back to wallet-amount {wallet_amount} -> {amount}.")
+            trade.amount = wallet_amount
             return wallet_amount
         else:
             raise DependencyException(
@@ -1553,7 +1555,7 @@ class FreqtradeBot(LoggingMixin):
             # Emergency sells (default to market!)
             order_type = self.strategy.order_types.get("emergency_exit", "market")
 
-        amount = self._safe_exit_amount(trade.pair, sub_trade_amt or trade.amount)
+        amount = self._safe_exit_amount(trade, trade.pair, sub_trade_amt or trade.amount)
         time_in_force = self.strategy.order_time_in_force['exit']
 
         if (exit_check.exit_type != ExitType.LIQUIDATION
@@ -1828,7 +1830,7 @@ class FreqtradeBot(LoggingMixin):
         never in base currency.
         """
         self.wallets.update()
-        amount_ = amount
+        amount_ = trade.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

freqtrade/misc.py

@@ -6,7 +6,7 @@ import logging
 import re
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Iterator, List
+from typing import Any, Dict, Iterator, List, Mapping, Union
 from typing.io import IO
 from urllib.parse import urlparse
 
@@ -186,7 +186,10 @@ def safe_value_fallback(obj: dict, key1: str, key2: str, default_value=None):
     return default_value
 
 
-def safe_value_fallback2(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
+dictMap = Union[Dict[str, Any], Mapping[str, Any]]
+
+
+def safe_value_fallback2(dict1: dictMap, dict2: dictMap, key1: str, key2: str, default_value=None):
     """
     Search a value in dict1, return this if it's not None.
     Fall back to dict2 - return key2 from dict2 if it's not None.

freqtrade/optimize/backtesting.py

@@ -151,6 +151,8 @@ class Backtesting:
         self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
         # strategies which define "can_short=True" will fail to load in Spot mode.
         self._can_short = self.trading_mode != TradingMode.SPOT
+        self._position_stacking: bool = self.config.get('position_stacking', False)
+        self.enable_protections: bool = self.config.get('enable_protections', False)
 
         self.init_backtest()
 
@@ -617,13 +619,16 @@ class Backtesting:
                     exit_reason = row[EXIT_TAG_IDX]
                 # Custom exit pricing only for exit-signals
                 if order_type == 'limit':
-                    close_rate = strategy_safe_wrapper(self.strategy.custom_exit_price,
-                                                       default_retval=close_rate)(
+                    rate = strategy_safe_wrapper(self.strategy.custom_exit_price,
+                                                 default_retval=close_rate)(
                         pair=trade.pair,
                         trade=trade,  # type: ignore[arg-type]
                         current_time=exit_candle_time,
                         proposed_rate=close_rate, current_profit=current_profit,
                         exit_tag=exit_reason)
+                    if rate != close_rate:
+                        close_rate = price_to_precision(rate, trade.price_precision,
+                                                        self.precision_mode)
                     # We can't place orders lower than current low.
                     # freqtrade does not support this in live, and the order would fill immediately
                     if trade.is_short:
@@ -660,7 +665,6 @@ class Backtesting:
         # 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,
@@ -674,12 +678,12 @@ class Backtesting:
             side=trade.exit_side,
             order_type=order_type,
             status="open",
-            price=rate,
-            average=rate,
+            price=close_rate,
+            average=close_rate,
             amount=amount,
             filled=0,
             remaining=amount,
-            cost=amount * rate,
+            cost=amount * close_rate,
         )
         trade.orders.append(order)
         return trade
@@ -726,18 +730,21 @@ class Backtesting:
     def get_valid_price_and_stake(
         self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
         direction: LongShort, current_time: datetime, entry_tag: Optional[str],
-        trade: Optional[LocalTrade], order_type: str
+        trade: Optional[LocalTrade], order_type: str, price_precision: Optional[float]
     ) -> Tuple[float, float, float, float]:
 
         if order_type == 'limit':
-            propose_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
-                                                 default_retval=propose_rate)(
+            new_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
+                                             default_retval=propose_rate)(
                 pair=pair, current_time=current_time,
                 proposed_rate=propose_rate, entry_tag=entry_tag,
                 side=direction,
             )  # default value is the open rate
             # We can't place orders higher than current high (otherwise it'd be a stop limit entry)
             # which freqtrade does not support in live.
+            if new_rate != propose_rate:
+                propose_rate = price_to_precision(new_rate, price_precision,
+                                                  self.precision_mode)
             if direction == "short":
                 propose_rate = max(propose_rate, row[LOW_IDX])
             else:
@@ -799,9 +806,11 @@ class Backtesting:
         pos_adjust = trade is not None and requested_rate is None
 
         stake_amount_ = stake_amount or (trade.stake_amount if trade else 0.0)
+        precision_price = self.exchange.get_precision_price(pair)
+
         propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake(
             pair, row, row[OPEN_IDX], stake_amount_, direction, current_time, entry_tag, trade,
-            order_type
+            order_type, precision_price,
         )
 
         # replace proposed rate if another rate was requested
@@ -817,8 +826,6 @@ 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)
-            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)
@@ -914,30 +921,23 @@ class Backtesting:
         return trade
 
     def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
-                         data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
+                         data: Dict[str, List[Tuple]]) -> None:
         """
         Handling of left open trades at the end of backtesting
         """
-        trades = []
         for pair in open_trades.keys():
-            if len(open_trades[pair]) > 0:
-                for trade in open_trades[pair]:
-                    if trade.open_order_id and trade.nr_of_successful_entries == 0:
-                        # Ignore trade if entry-order did not fill yet
-                        continue
-                    exit_row = data[pair][-1]
-                    self._exit_trade(trade, exit_row, exit_row[OPEN_IDX], trade.amount)
-                    trade.orders[-1].close_bt_order(exit_row[DATE_IDX].to_pydatetime(), trade)
+            for trade in list(open_trades[pair]):
+                if trade.open_order_id and trade.nr_of_successful_entries == 0:
+                    # Ignore trade if entry-order did not fill yet
+                    continue
+                exit_row = data[pair][-1]
+                self._exit_trade(trade, exit_row, exit_row[OPEN_IDX], trade.amount)
+                trade.orders[-1].close_bt_order(exit_row[DATE_IDX].to_pydatetime(), trade)
 
-                    trade.close_date = exit_row[DATE_IDX].to_pydatetime()
-                    trade.exit_reason = ExitType.FORCE_EXIT.value
-                    trade.close(exit_row[OPEN_IDX], show_msg=False)
-                    LocalTrade.close_bt_trade(trade)
-                    # Deepcopy object to have wallets update correctly
-                    trade1 = deepcopy(trade)
-                    trade1.is_open = True
-                    trades.append(trade1)
-        return trades
+                trade.close_date = exit_row[DATE_IDX].to_pydatetime()
+                trade.exit_reason = ExitType.FORCE_EXIT.value
+                trade.close(exit_row[OPEN_IDX], show_msg=False)
+                LocalTrade.close_bt_trade(trade)
 
     def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
         # Always allow trades when max_open_trades is enabled.
@@ -961,9 +961,8 @@ class Backtesting:
             return 'short'
         return None
 
-    def run_protections(
-            self, enable_protections, pair: str, current_time: datetime, side: LongShort):
-        if enable_protections:
+    def run_protections(self, pair: str, current_time: datetime, side: LongShort):
+        if self.enable_protections:
             self.protections.stop_per_pair(pair, current_time, side)
             self.protections.global_stop(current_time, side)
 
@@ -1069,10 +1068,78 @@ class Backtesting:
             return None
         return row
 
-    def backtest(self, processed: Dict,  # noqa: max-complexity: 13
+    def backtest_loop(
+            self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
+            max_open_trades: int, open_trade_count_start: int) -> int:
+        """
+        NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
+
+        Backtesting processing for one candle/pair.
+        """
+        for t in list(LocalTrade.bt_trades_open_pp[pair]):
+            # 1. Manage currently open orders of active trades
+            if self.manage_open_orders(t, current_time, row):
+                # Close trade
+                open_trade_count_start -= 1
+                LocalTrade.remove_bt_trade(t)
+                self.wallets.update()
+
+        # 2. Process entries.
+        # without positionstacking, we can only have one open trade per pair.
+        # max_open_trades must be respected
+        # don't open on the last row
+        trade_dir = self.check_for_trade_entry(row)
+        if (
+            (self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
+            and self.trade_slot_available(max_open_trades, open_trade_count_start)
+            and current_time != end_date
+            and trade_dir is not None
+            and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
+        ):
+            trade = self._enter_trade(pair, row, trade_dir)
+            if trade:
+                # TODO: hacky workaround to avoid opening > max_open_trades
+                # This emulates previous behavior - not sure if this is correct
+                # Prevents entering if the trade-slot was freed in this candle
+                open_trade_count_start += 1
+                # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
+                LocalTrade.add_bt_trade(trade)
+                self.wallets.update()
+
+        for trade in list(LocalTrade.bt_trades_open_pp[pair]):
+            # 3. Process entry orders.
+            order = trade.select_order(trade.entry_side, is_open=True)
+            if order and self._get_order_filled(order.price, row):
+                order.close_bt_order(current_time, trade)
+                trade.open_order_id = None
+                self.wallets.update()
+
+                # 4. Create exit orders (if any)
+            if not trade.open_order_id:
+                self._get_exit_trade_entry(trade, row)  # Place exit order if necessary
+
+                # 5. Process exit orders.
+            order = trade.select_order(trade.exit_side, is_open=True)
+            if order and self._get_order_filled(order.price, row):
+                order.close_bt_order(current_time, trade)
+                trade.open_order_id = None
+                sub_trade = order.safe_amount_after_fee != trade.amount
+                if sub_trade:
+                    order.close_bt_order(current_time, trade)
+                    trade.recalc_trade_from_orders()
+                else:
+                    trade.close_date = current_time
+                    trade.close(order.price, show_msg=False)
+
+                    # logger.debug(f"{pair} - Backtesting exit {trade}")
+                    LocalTrade.close_bt_trade(trade)
+                self.wallets.update()
+                self.run_protections(pair, current_time, trade.trade_direction)
+        return open_trade_count_start
+
+    def backtest(self, processed: Dict,
                  start_date: datetime, end_date: datetime,
-                 max_open_trades: int = 0, position_stacking: bool = False,
-                 enable_protections: bool = False) -> Dict[str, Any]:
+                 max_open_trades: int = 0) -> Dict[str, Any]:
         """
         Implement backtesting functionality
 
@@ -1085,12 +1152,9 @@ class Backtesting:
         :param start_date: backtesting timerange start datetime
         :param end_date: backtesting timerange end datetime
         :param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
-        :param position_stacking: do we allow position stacking?
-        :param enable_protections: Should protections be enabled?
         :return: DataFrame with trades (results of backtesting)
         """
-        trades: List[LocalTrade] = []
-        self.prepare_backtest(enable_protections)
+        self.prepare_backtest(self.enable_protections)
         # Ensure wallets are uptodate (important for --strategy-list)
         self.wallets.update()
         # Use dict of lists with data for performance
@@ -1101,15 +1165,12 @@ class Backtesting:
         indexes: Dict = defaultdict(int)
         current_time = start_date + timedelta(minutes=self.timeframe_min)
 
-        open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
-        open_trade_count = 0
-
         self.progress.init_step(BacktestState.BACKTEST, int(
             (end_date - start_date) / timedelta(minutes=self.timeframe_min)))
 
         # Loop timerange and get candle for each pair at that point in time
         while current_time <= end_date:
-            open_trade_count_start = open_trade_count
+            open_trade_count_start = LocalTrade.bt_open_open_trade_count
             self.check_abort()
             for i, pair in enumerate(data):
                 row_index = indexes[pair]
@@ -1121,81 +1182,17 @@ class Backtesting:
                 indexes[pair] = row_index
                 self.dataprovider._set_dataframe_max_index(row_index)
 
-                for t in list(open_trades[pair]):
-                    # 1. Manage currently open orders of active trades
-                    if self.manage_open_orders(t, current_time, row):
-                        # Close trade
-                        open_trade_count -= 1
-                        open_trades[pair].remove(t)
-                        LocalTrade.trades_open.remove(t)
-                        self.wallets.update()
-
-                # 2. Process entries.
-                # without positionstacking, we can only have one open trade per pair.
-                # max_open_trades must be respected
-                # don't open on the last row
-                trade_dir = self.check_for_trade_entry(row)
-                if (
-                    (position_stacking or len(open_trades[pair]) == 0)
-                    and self.trade_slot_available(max_open_trades, open_trade_count_start)
-                    and current_time != end_date
-                    and trade_dir is not None
-                    and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
-                ):
-                    trade = self._enter_trade(pair, row, trade_dir)
-                    if trade:
-                        # TODO: hacky workaround to avoid opening > max_open_trades
-                        # This emulates previous behavior - not sure if this is correct
-                        # Prevents entering if the trade-slot was freed in this candle
-                        open_trade_count_start += 1
-                        open_trade_count += 1
-                        # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
-                        open_trades[pair].append(trade)
-                        LocalTrade.add_bt_trade(trade)
-                        self.wallets.update()
-
-                for trade in list(open_trades[pair]):
-                    # 3. Process entry orders.
-                    order = trade.select_order(trade.entry_side, is_open=True)
-                    if order and self._get_order_filled(order.price, row):
-                        order.close_bt_order(current_time, trade)
-                        trade.open_order_id = None
-                        self.wallets.update()
-
-                    # 4. Create exit orders (if any)
-                    if not trade.open_order_id:
-                        self._get_exit_trade_entry(trade, row)  # Place exit order if necessary
-
-                    # 5. Process exit orders.
-                    order = trade.select_order(trade.exit_side, is_open=True)
-                    if order and self._get_order_filled(order.price, row):
-                        order.close_bt_order(current_time, trade)
-                        trade.open_order_id = None
-                        sub_trade = order.safe_amount_after_fee != trade.amount
-                        if sub_trade:
-                            order.close_bt_order(current_time, trade)
-                            trade.recalc_trade_from_orders()
-                        else:
-                            trade.close_date = current_time
-                            trade.close(order.price, show_msg=False)
-
-                            # logger.debug(f"{pair} - Backtesting exit {trade}")
-                            open_trade_count -= 1
-                            open_trades[pair].remove(trade)
-                            LocalTrade.close_bt_trade(trade)
-                            trades.append(trade)
-                        self.wallets.update()
-                        self.run_protections(
-                            enable_protections, pair, current_time, trade.trade_direction)
+                open_trade_count_start = self.backtest_loop(
+                    row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
 
             # Move time one configured time_interval ahead.
             self.progress.increment()
             current_time += timedelta(minutes=self.timeframe_min)
 
-        trades += self.handle_left_open(open_trades, data=data)
+        self.handle_left_open(LocalTrade.bt_trades_open_pp, data=data)
         self.wallets.update()
 
-        results = trade_list_to_dataframe(trades)
+        results = trade_list_to_dataframe(LocalTrade.trades)
         return {
             'results': results,
             'config': self.strategy.config,
@@ -1248,8 +1245,6 @@ class Backtesting:
             start_date=min_date,
             end_date=max_date,
             max_open_trades=max_open_trades,
-            position_stacking=self.config.get('position_stacking', False),
-            enable_protections=self.config.get('enable_protections', False),
         )
         backtest_end_time = datetime.now(timezone.utc)
         results.update({

freqtrade/optimize/hyperopt.py

@@ -122,7 +122,6 @@ class Hyperopt:
         else:
             logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
             self.max_open_trades = 0
-        self.position_stacking = self.config.get('position_stacking', False)
 
         if HyperoptTools.has_space(self.config, 'sell'):
             # Make sure use_exit_signal is enabled
@@ -258,6 +257,7 @@ class Hyperopt:
             logger.debug("Hyperopt has 'protection' space")
             # Enable Protections if protection space is selected.
             self.config['enable_protections'] = True
+            self.backtesting.enable_protections = True
             self.protection_space = self.custom_hyperopt.protection_space()
 
         if HyperoptTools.has_space(self.config, 'buy'):
@@ -339,8 +339,6 @@ class Hyperopt:
             start_date=self.min_date,
             end_date=self.max_date,
             max_open_trades=self.max_open_trades,
-            position_stacking=self.position_stacking,
-            enable_protections=self.config.get('enable_protections', False),
         )
         backtest_end_time = datetime.now(timezone.utc)
         bt_results.update({

freqtrade/optimize/hyperopt_tools.py

@@ -12,7 +12,7 @@ import tabulate
 from colorama import Fore, Style
 from pandas import isna, json_normalize
 
-from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES, Config
+from freqtrade.constants import FTHYPT_FILEVERSION, Config
 from freqtrade.enums import HyperoptState
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
@@ -50,9 +50,8 @@ class HyperoptTools():
         Get Strategy-location (filename) from strategy_name
         """
         from freqtrade.resolvers.strategy_resolver import StrategyResolver
-        directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
         strategy_objs = StrategyResolver.search_all_objects(
-            directory, False, config.get('recursive_strategy_search', False))
+            config, False, config.get('recursive_strategy_search', False))
         strategies = [s for s in strategy_objs if s['name'] == strategy_name]
         if strategies:
             strategy = strategies[0]

freqtrade/optimize/optimize_reports.py

@@ -408,10 +408,10 @@ def generate_strategy_stats(pairlist: List[str],
 
     exit_reason_stats = generate_exit_reason_stats(max_open_trades=max_open_trades,
                                                    results=results)
-    left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
-                                              starting_balance=start_balance,
-                                              results=results.loc[results['is_open']],
-                                              skip_nan=True)
+    left_open_results = generate_pair_metrics(
+        pairlist, stake_currency=stake_currency, starting_balance=start_balance,
+        results=results.loc[results['exit_reason'] == 'force_exit'], skip_nan=True)
+
     daily_stats = generate_daily_stats(results)
     trade_stats = generate_trading_stats(results)
     best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],

freqtrade/persistence/trade_model.py

@@ -2,6 +2,7 @@
 This module contains the class to persist trades into SQLite
 """
 import logging
+from collections import defaultdict
 from datetime import datetime, timedelta, timezone
 from math import isclose
 from typing import Any, Dict, List, Optional
@@ -255,6 +256,9 @@ class LocalTrade():
     # Trades container for backtesting
     trades: List['LocalTrade'] = []
     trades_open: List['LocalTrade'] = []
+    # Copy of trades_open - but indexed by pair
+    bt_trades_open_pp: Dict[str, List['LocalTrade']] = defaultdict(list)
+    bt_open_open_trade_count: int = 0
     total_profit: float = 0
     realized_profit: float = 0
 
@@ -538,6 +542,8 @@ class LocalTrade():
         """
         LocalTrade.trades = []
         LocalTrade.trades_open = []
+        LocalTrade.bt_trades_open_pp = defaultdict(list)
+        LocalTrade.bt_open_open_trade_count = 0
         LocalTrade.total_profit = 0
 
     def adjust_min_max_rates(self, current_price: float, current_price_low: float) -> None:
@@ -1067,6 +1073,8 @@ class LocalTrade():
     @staticmethod
     def close_bt_trade(trade):
         LocalTrade.trades_open.remove(trade)
+        LocalTrade.bt_trades_open_pp[trade.pair].remove(trade)
+        LocalTrade.bt_open_open_trade_count -= 1
         LocalTrade.trades.append(trade)
         LocalTrade.total_profit += trade.close_profit_abs
 
@@ -1074,9 +1082,17 @@ class LocalTrade():
     def add_bt_trade(trade):
         if trade.is_open:
             LocalTrade.trades_open.append(trade)
+            LocalTrade.bt_trades_open_pp[trade.pair].append(trade)
+            LocalTrade.bt_open_open_trade_count += 1
         else:
             LocalTrade.trades.append(trade)
 
+    @staticmethod
+    def remove_bt_trade(trade):
+        LocalTrade.trades_open.remove(trade)
+        LocalTrade.bt_trades_open_pp[trade.pair].remove(trade)
+        LocalTrade.bt_open_open_trade_count -= 1
+
     @staticmethod
     def get_open_trades() -> List[Any]:
         """
@@ -1092,7 +1108,7 @@ class LocalTrade():
         if Trade.use_db:
             return Trade.query.filter(Trade.is_open.is_(True)).count()
         else:
-            return len(LocalTrade.trades_open)
+            return LocalTrade.bt_open_open_trade_count
 
     @staticmethod
     def stoploss_reinitialization(desired_stoploss):
@@ -1504,3 +1520,87 @@ class Trade(_DECL_BASE, LocalTrade):
             Order.status == 'closed'
         ).scalar()
         return trading_volume
+
+    @staticmethod
+    def from_json(json_str: str) -> 'Trade':
+        """
+        Create a Trade instance from a json string.
+
+        Used for debugging purposes - please keep.
+        :param json_str: json string to parse
+        :return: Trade instance
+        """
+        import rapidjson
+        data = rapidjson.loads(json_str)
+        trade = Trade(
+            id=data["trade_id"],
+            pair=data["pair"],
+            base_currency=data["base_currency"],
+            stake_currency=data["quote_currency"],
+            is_open=data["is_open"],
+            exchange=data["exchange"],
+            amount=data["amount"],
+            amount_requested=data["amount_requested"],
+            stake_amount=data["stake_amount"],
+            strategy=data["strategy"],
+            enter_tag=data["enter_tag"],
+            timeframe=data["timeframe"],
+            fee_open=data["fee_open"],
+            fee_open_cost=data["fee_open_cost"],
+            fee_open_currency=data["fee_open_currency"],
+            fee_close=data["fee_close"],
+            fee_close_cost=data["fee_close_cost"],
+            fee_close_currency=data["fee_close_currency"],
+            open_date=datetime.fromtimestamp(data["open_timestamp"] // 1000, tz=timezone.utc),
+            open_rate=data["open_rate"],
+            open_rate_requested=data["open_rate_requested"],
+            open_trade_value=data["open_trade_value"],
+            close_date=(datetime.fromtimestamp(data["close_timestamp"] // 1000, tz=timezone.utc)
+                        if data["close_timestamp"] else None),
+            realized_profit=data["realized_profit"],
+            close_rate=data["close_rate"],
+            close_rate_requested=data["close_rate_requested"],
+            close_profit=data["close_profit"],
+            close_profit_abs=data["close_profit_abs"],
+            exit_reason=data["exit_reason"],
+            exit_order_status=data["exit_order_status"],
+            stop_loss=data["stop_loss_abs"],
+            stop_loss_pct=data["stop_loss_ratio"],
+            stoploss_order_id=data["stoploss_order_id"],
+            stoploss_last_update=(datetime.fromtimestamp(data["stoploss_last_update"] // 1000,
+                                  tz=timezone.utc) if data["stoploss_last_update"] else None),
+            initial_stop_loss=data["initial_stop_loss_abs"],
+            initial_stop_loss_pct=data["initial_stop_loss_ratio"],
+            min_rate=data["min_rate"],
+            max_rate=data["max_rate"],
+            leverage=data["leverage"],
+            interest_rate=data["interest_rate"],
+            liquidation_price=data["liquidation_price"],
+            is_short=data["is_short"],
+            trading_mode=data["trading_mode"],
+            funding_fees=data["funding_fees"],
+            open_order_id=data["open_order_id"],
+        )
+        for order in data["orders"]:
+
+            order_obj = Order(
+                amount=order["amount"],
+                ft_order_side=order["ft_order_side"],
+                ft_pair=order["pair"],
+                ft_is_open=order["is_open"],
+                order_id=order["order_id"],
+                status=order["status"],
+                average=order["average"],
+                cost=order["cost"],
+                filled=order["filled"],
+                order_date=datetime.strptime(order["order_date"], DATETIME_PRINT_FORMAT),
+                order_filled_date=(datetime.fromtimestamp(
+                    order["order_filled_timestamp"] // 1000, tz=timezone.utc)
+                    if order["order_filled_timestamp"] else None),
+                order_type=order["order_type"],
+                price=order["price"],
+                remaining=order["remaining"],
+            )
+            trade.orders.append(order_obj)
+
+        return trade

freqtrade/plugins/pairlist/AgeFilter.py

@@ -10,6 +10,7 @@ from pandas import DataFrame
 
 from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Tickers
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
 from freqtrade.util import PeriodicCache
@@ -67,10 +68,10 @@ class AgeFilter(IPairList):
             f"{self._max_days_listed} {plural(self._max_days_listed, 'day')}"
         ) if self._max_days_listed else '')
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new allowlist
         """
         needed_pairs: ListPairsWithTimeframes = [

freqtrade/plugins/pairlist/IPairList.py

@@ -4,11 +4,12 @@ PairList Handler base class
 import logging
 from abc import ABC, abstractmethod, abstractproperty
 from copy import deepcopy
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange, market_is_active
+from freqtrade.exchange.types import Ticker, Tickers
 from freqtrade.mixins import LoggingMixin
 
 
@@ -61,7 +62,7 @@ class IPairList(LoggingMixin, ABC):
         -> Please overwrite in subclasses
         """
 
-    def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
+    def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
         """
         Check one pair against Pairlist Handler's specific conditions.
 
@@ -69,12 +70,12 @@ class IPairList(LoggingMixin, ABC):
         filter_pairlist() method.
 
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
+        :param ticker: ticker dict as returned from ccxt.fetch_ticker
         :return: True if the pair can stay, false if it should be removed
         """
         raise NotImplementedError()
 
-    def gen_pairlist(self, tickers: Dict) -> List[str]:
+    def gen_pairlist(self, tickers: Tickers) -> List[str]:
         """
         Generate the pairlist.
 
@@ -85,13 +86,13 @@ class IPairList(LoggingMixin, ABC):
         it will raise the exception if a Pairlist Handler is used at the first
         position in the chain.
 
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: List of pairs
         """
         raise OperationalException("This Pairlist Handler should not be used "
                                    "at the first position in the list of Pairlist Handlers.")
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
 
@@ -103,14 +104,14 @@ class IPairList(LoggingMixin, ABC):
         own filtration.
 
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new whitelist
         """
         if self._enabled:
             # Copy list since we're modifying this list
             for p in deepcopy(pairlist):
                 # Filter out assets
-                if not self._validate_pair(p, tickers[p] if p in tickers else {}):
+                if not self._validate_pair(p, tickers[p] if p in tickers else None):
                     pairlist.remove(p)
 
         return pairlist

freqtrade/plugins/pairlist/OffsetFilter.py

@@ -6,6 +6,7 @@ from typing import Any, Dict, List
 
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Tickers
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -42,12 +43,12 @@ class OffsetFilter(IPairList):
             return f"{self.name} - Taking {self._number_pairs} Pairs, starting from {self._offset}."
         return f"{self.name} - Offsetting pairs by {self._offset}."
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new whitelist
         """
         if self._offset > len(pairlist):

freqtrade/plugins/pairlist/PerformanceFilter.py

@@ -7,6 +7,7 @@ from typing import Any, Dict, List
 import pandas as pd
 
 from freqtrade.constants import Config
+from freqtrade.exchange.types import Tickers
 from freqtrade.persistence import Trade
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -39,12 +40,12 @@ class PerformanceFilter(IPairList):
         """
         return f"{self.name} - Sorting pairs by performance."
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """
         Filters and sorts pairlist and returns the allowlist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new allowlist
         """
         # Get the trading performance for pairs from database

freqtrade/plugins/pairlist/PrecisionFilter.py

@@ -2,10 +2,11 @@
 Precision pair list filter
 """
 import logging
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Ticker
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -44,15 +45,15 @@ class PrecisionFilter(IPairList):
         """
         return f"{self.name} - Filtering untradable pairs."
 
-    def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
+    def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
         """
         Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
         low value pairs.
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
+        :param ticker: ticker dict as returned from ccxt.fetch_ticker
         :return: True if the pair can stay, false if it should be removed
         """
-        if ticker.get('last', None) is None:
+        if not ticker or 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)

freqtrade/plugins/pairlist/PriceFilter.py

@@ -2,10 +2,11 @@
 Price pair list filter
 """
 import logging
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Ticker
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -64,14 +65,16 @@ class PriceFilter(IPairList):
 
         return f"{self.name} - No price filters configured."
 
-    def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
+    def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
         """
         Check if if one price-step (pip) is > than a certain barrier.
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
+        :param ticker: ticker dict as returned from ccxt.fetch_ticker
         :return: True if the pair can stay, false if it should be removed
         """
-        if ticker.get('last', None) is None or ticker.get('last') == 0:
+        if ticker and 'last' in ticker and ticker['last'] is not None and ticker.get('last') != 0:
+            price: float = ticker['last']
+        else:
             self.log_once(f"Removed {pair} from whitelist, because "
                           "ticker['last'] is empty (Usually no trade in the last 24h).",
                           logger.info)
@@ -79,8 +82,8 @@ class PriceFilter(IPairList):
 
         # Perform low_price_ratio check.
         if self._low_price_ratio != 0:
-            compare = self._exchange.price_get_one_pip(pair, ticker['last'])
-            changeperc = compare / ticker['last']
+            compare = self._exchange.price_get_one_pip(pair, price)
+            changeperc = compare / price
             if changeperc > self._low_price_ratio:
                 self.log_once(f"Removed {pair} from whitelist, "
                               f"because 1 unit is {changeperc:.3%}", logger.info)
@@ -88,7 +91,6 @@ class PriceFilter(IPairList):
 
         # Perform low_amount check
         if self._max_value != 0:
-            price = ticker['last']
             market = self._exchange.markets[pair]
             limits = market['limits']
             if (limits['amount']['min'] is not None):
@@ -113,14 +115,14 @@ class PriceFilter(IPairList):
 
         # Perform min_price check.
         if self._min_price != 0:
-            if ticker['last'] < self._min_price:
+            if price < self._min_price:
                 self.log_once(f"Removed {pair} from whitelist, "
                               f"because last price < {self._min_price:.8f}", logger.info)
                 return False
 
         # Perform max_price check.
         if self._max_price != 0:
-            if ticker['last'] > self._max_price:
+            if price > self._max_price:
                 self.log_once(f"Removed {pair} from whitelist, "
                               f"because last price > {self._max_price:.8f}", logger.info)
                 return False

freqtrade/plugins/pairlist/ProducerPairList.py

@@ -7,6 +7,7 @@ import logging
 from typing import Any, Dict, List, Optional
 
 from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Tickers
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -68,10 +69,10 @@ class ProducerPairList(IPairList):
 
         return pairs
 
-    def gen_pairlist(self, tickers: Dict) -> List[str]:
+    def gen_pairlist(self, tickers: Tickers) -> List[str]:
         """
         Generate the pairlist
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: List of pairs
         """
         pairs = self._filter_pairlist(None)
@@ -79,12 +80,12 @@ class ProducerPairList(IPairList):
         pairs = self._whitelist_for_active_markets(self.verify_whitelist(pairs, logger.info))
         return pairs
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new whitelist
         """
         return self._filter_pairlist(pairlist)

freqtrade/plugins/pairlist/ShuffleFilter.py

@@ -7,6 +7,7 @@ from typing import Any, Dict, List
 
 from freqtrade.constants import Config
 from freqtrade.enums import RunMode
+from freqtrade.exchange.types import Tickers
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -47,12 +48,12 @@ class ShuffleFilter(IPairList):
         return (f"{self.name} - Shuffling pairs" +
                 (f", seed = {self._seed}." if self._seed is not None else "."))
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new whitelist
         """
         # Shuffle is done inplace

freqtrade/plugins/pairlist/SpreadFilter.py

@@ -2,10 +2,10 @@
 Spread pair list filter
 """
 import logging
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 from freqtrade.constants import Config
-from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Ticker
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -22,12 +22,6 @@ class SpreadFilter(IPairList):
         self._max_spread_ratio = pairlistconfig.get('max_spread_ratio', 0.005)
         self._enabled = self._max_spread_ratio != 0
 
-        if not self._exchange.exchange_has('fetchTickers'):
-            raise OperationalException(
-                'Exchange does not support fetchTickers, therefore SpreadFilter cannot be used.'
-                'Please edit your config and restart the bot.'
-            )
-
     @property
     def needstickers(self) -> bool:
         """
@@ -44,14 +38,14 @@ class SpreadFilter(IPairList):
         return (f"{self.name} - Filtering pairs with ask/bid diff above "
                 f"{self._max_spread_ratio:.2%}.")
 
-    def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
+    def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
         """
         Validate spread for the ticker
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
+        :param ticker: ticker dict as returned from ccxt.fetch_ticker
         :return: True if the pair can stay, false if it should be removed
         """
-        if 'bid' in ticker and 'ask' in ticker and ticker['ask'] and ticker['bid']:
+        if ticker and 'bid' in ticker and 'ask' in ticker and ticker['ask'] and ticker['bid']:
             spread = 1 - ticker['bid'] / ticker['ask']
             if spread > self._max_spread_ratio:
                 self.log_once(f"Removed {pair} from whitelist, because spread "

freqtrade/plugins/pairlist/StaticPairList.py

@@ -8,6 +8,7 @@ from copy import deepcopy
 from typing import Any, Dict, List
 
 from freqtrade.constants import Config
+from freqtrade.exchange.types import Tickers
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -39,10 +40,10 @@ class StaticPairList(IPairList):
         """
         return f"{self.name}"
 
-    def gen_pairlist(self, tickers: Dict) -> List[str]:
+    def gen_pairlist(self, tickers: Tickers) -> List[str]:
         """
         Generate the pairlist
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: List of pairs
         """
         if self._allow_inactive:
@@ -53,12 +54,12 @@ class StaticPairList(IPairList):
             return self._whitelist_for_active_markets(
                 self.verify_whitelist(self._config['exchange']['pair_whitelist'], logger.info))
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new whitelist
         """
         pairlist_ = deepcopy(pairlist)

freqtrade/plugins/pairlist/VolatilityFilter.py

@@ -13,6 +13,7 @@ from pandas import DataFrame
 
 from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Tickers
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -62,11 +63,11 @@ class VolatilityFilter(IPairList):
                 f"{self._min_volatility}-{self._max_volatility} "
                 f" the last {self._days} {plural(self._days, 'day')}.")
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """
         Validate trading range
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new allowlist
         """
         needed_pairs: ListPairsWithTimeframes = [

freqtrade/plugins/pairlist/VolumePairList.py

@@ -5,13 +5,14 @@ Provides dynamic pair list based on trade volumes
 """
 import logging
 from datetime import datetime, timedelta, timezone
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Literal
 
 from cachetools import TTLCache
 
 from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
+from freqtrade.exchange.types import Tickers
 from freqtrade.misc import format_ms_time
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -36,7 +37,7 @@ class VolumePairList(IPairList):
 
         self._stake_currency = config['stake_currency']
         self._number_pairs = self._pairlistconfig['number_assets']
-        self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
+        self._sort_key: Literal['quoteVolume'] = self._pairlistconfig.get('sort_key', 'quoteVolume')
         self._min_value = self._pairlistconfig.get('min_value', 0)
         self._refresh_period = self._pairlistconfig.get('refresh_period', 1800)
         self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
@@ -110,10 +111,10 @@ class VolumePairList(IPairList):
         """
         return f"{self.name} - top {self._pairlistconfig['number_assets']} volume pairs."
 
-    def gen_pairlist(self, tickers: Dict) -> List[str]:
+    def gen_pairlist(self, tickers: Tickers) -> List[str]:
         """
         Generate the pairlist
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: List of pairs
         """
         # Generate dynamic whitelist
@@ -150,7 +151,7 @@ class VolumePairList(IPairList):
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new whitelist
         """
         if self._use_range:

freqtrade/plugins/pairlist/pairlist_helpers.py

@@ -12,7 +12,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
     :param wildcardpl: List of Pairlists, which may contain regex
     :param available_pairs: List of all available pairs (`exchange.get_markets().keys()`)
     :param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes
-    :return expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
+    :return: expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
     :raises: ValueError if a wildcard is invalid (like '*/BTC' - which should be `.*/BTC`)
     """
     result = []

freqtrade/plugins/pairlist/rangestabilityfilter.py

@@ -11,6 +11,7 @@ from pandas import DataFrame
 
 from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Tickers
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -60,11 +61,11 @@ class RangeStabilityFilter(IPairList):
                 f"{self._min_rate_of_change}{max_rate_desc} over the "
                 f"last {plural(self._days, 'day')}.")
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """
         Validate trading range
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: new allowlist
         """
         needed_pairs: ListPairsWithTimeframes = [

freqtrade/plugins/pairlistmanager.py

@@ -11,6 +11,7 @@ from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.enums import CandleType
 from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Tickers
 from freqtrade.mixins import LoggingMixin
 from freqtrade.plugins.pairlist.IPairList import IPairList
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@@ -45,6 +46,15 @@ class PairListManager(LoggingMixin):
         if not self._pairlist_handlers:
             raise OperationalException("No Pairlist Handlers defined")
 
+        if self._tickers_needed and not self._exchange.exchange_has('fetchTickers'):
+            invalid = ". ".join([p.name for p in self._pairlist_handlers if p.needstickers])
+
+            raise OperationalException(
+                "Exchange does not support fetchTickers, therefore the following pairlists "
+                "cannot be used. Please edit your config and restart the bot.\n"
+                f"{invalid}."
+            )
+
         refresh_period = config.get('pairlist_refresh_period', 3600)
         LoggingMixin.__init__(self, logger, refresh_period)
 
@@ -76,7 +86,7 @@ class PairListManager(LoggingMixin):
         return [{p.name: p.short_desc()} for p in self._pairlist_handlers]
 
     @cached(TTLCache(maxsize=1, ttl=1800))
-    def _get_cached_tickers(self):
+    def _get_cached_tickers(self) -> Tickers:
         return self._exchange.get_tickers()
 
     def refresh_pairlist(self) -> None:

freqtrade/resolvers/freqaimodel_resolver.py

@@ -26,6 +26,7 @@ class FreqaiModelResolver(IResolver):
     initial_search_path = (
         Path(__file__).parent.parent.joinpath("freqai/prediction_models").resolve()
     )
+    extra_path = "freqaimodel_path"
 
     @staticmethod
     def load_freqaimodel(config: Config) -> IFreqaiModel:
@@ -50,7 +51,6 @@ class FreqaiModelResolver(IResolver):
             freqaimodel_name,
             config,
             kwargs={"config": config},
-            extra_dir=config.get("freqaimodel_path"),
         )
 
         return freqaimodel

freqtrade/resolvers/iresolver.py

@@ -42,6 +42,8 @@ class IResolver:
     object_type_str: str
     user_subdir: Optional[str] = None
     initial_search_path: Optional[Path]
+    # Optional config setting containing a path (strategy_path, freqaimodel_path)
+    extra_path: Optional[str] = None
 
     @classmethod
     def build_search_paths(cls, config: Config, user_subdir: Optional[str] = None,
@@ -58,6 +60,9 @@ class IResolver:
         for dir in extra_dirs:
             abs_paths.insert(0, Path(dir).resolve())
 
+        if cls.extra_path and (extra := config.get(cls.extra_path)):
+            abs_paths.insert(0, Path(extra).resolve())
+
         return abs_paths
 
     @classmethod
@@ -183,9 +188,35 @@ class IResolver:
         )
 
     @classmethod
-    def search_all_objects(cls, directory: Path, enum_failed: bool,
+    def search_all_objects(cls, config: Config, enum_failed: bool,
                            recursive: bool = False) -> List[Dict[str, Any]]:
         """
+        Searches for valid objects
+        :param config: Config object
+        :param enum_failed: If True, will return None for modules which fail.
+            Otherwise, failing modules are skipped.
+        :param recursive: Recursively walk directory tree searching for strategies
+        :return: List of dicts containing 'name', 'class' and 'location' entries
+        """
+        result = []
+
+        abs_paths = cls.build_search_paths(config, user_subdir=cls.user_subdir)
+        for path in abs_paths:
+            result.extend(cls._search_all_objects(path, enum_failed, recursive))
+        return result
+
+    @classmethod
+    def _build_rel_location(cls, directory: Path, entry: Path) -> str:
+
+        builtin = cls.initial_search_path == directory
+        return f"<builtin>/{entry.relative_to(directory)}" if builtin else str(
+            entry.relative_to(directory))
+
+    @classmethod
+    def _search_all_objects(
+            cls, directory: Path, enum_failed: bool, recursive: bool = False,
+            basedir: Optional[Path] = None) -> List[Dict[str, Any]]:
+        """
         Searches a directory for valid objects
         :param directory: Path to search
         :param enum_failed: If True, will return None for modules which fail.
@@ -204,7 +235,8 @@ class IResolver:
                 and not entry.name.startswith('__')
                 and not entry.name.startswith('.')
             ):
-                objects.extend(cls.search_all_objects(entry, enum_failed, recursive=recursive))
+                objects.extend(cls._search_all_objects(
+                    entry, enum_failed, recursive, basedir or directory))
             # Only consider python files
             if entry.suffix != '.py':
                 logger.debug('Ignoring %s', entry)
@@ -217,5 +249,6 @@ class IResolver:
                     {'name': obj[0].__name__ if obj is not None else '',
                      'class': obj[0] if obj is not None else None,
                      'location': entry,
+                     'location_rel': cls._build_rel_location(basedir or directory, entry),
                      })
         return objects

freqtrade/resolvers/strategy_resolver.py

@@ -30,6 +30,7 @@ class StrategyResolver(IResolver):
     object_type_str = "Strategy"
     user_subdir = USERPATH_STRATEGIES
     initial_search_path = None
+    extra_path = "strategy_path"
 
     @staticmethod
     def load_strategy(config: Config = None) -> IStrategy:

freqtrade/rpc/api_server/api_backtest.py

@@ -89,6 +89,7 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
             lastconfig['enable_protections'] = btconfig.get('enable_protections')
             lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
 
+            ApiServer._bt.enable_protections = btconfig.get('enable_protections', False)
             ApiServer._bt.strategylist = [strat]
             ApiServer._bt.results = {}
             ApiServer._bt.load_prior_backtest()

freqtrade/rpc/api_server/api_v1.py

@@ -1,13 +1,11 @@
 import logging
 from copy import deepcopy
-from pathlib import Path
 from typing import List, Optional
 
 from fastapi import APIRouter, Depends, Query
 from fastapi.exceptions import HTTPException
 
 from freqtrade import __version__
-from freqtrade.constants import USERPATH_STRATEGIES
 from freqtrade.data.history import get_datahandler
 from freqtrade.enums import CandleType, TradingMode
 from freqtrade.exceptions import OperationalException
@@ -253,11 +251,9 @@ def plot_config(rpc: RPC = Depends(get_rpc)):
 
 @router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])
 def list_strategies(config=Depends(get_config)):
-    directory = Path(config.get(
-        'strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
     from freqtrade.resolvers.strategy_resolver import StrategyResolver
     strategies = StrategyResolver.search_all_objects(
-        directory, False, config.get('recursive_strategy_search', False))
+        config, False, config.get('recursive_strategy_search', False))
     strategies = sorted(strategies, key=lambda x: x['name'])
 
     return {'strategies': [x['name'] for x in strategies]}

freqtrade/rpc/api_server/api_ws.py

@@ -1,9 +1,11 @@
+import asyncio
 import logging
 from typing import Any, Dict
 
 from fastapi import APIRouter, Depends, WebSocketDisconnect
 from fastapi.websockets import WebSocket, WebSocketState
 from pydantic import ValidationError
+from websockets.exceptions import WebSocketException
 
 from freqtrade.enums import RPCMessageType, RPCRequestType
 from freqtrade.rpc.api_server.api_auth import validate_ws_token
@@ -88,6 +90,8 @@ async def _process_consumer_request(
         for _, message in analyzed_df.items():
             response = WSAnalyzedDFMessage(data=message)
             await channel.send(response.dict(exclude_none=True))
+            # Throttle the messages to 50/s
+            await asyncio.sleep(0.02)
 
 
 @router.websocket("/message/ws")
@@ -102,7 +106,6 @@ async def message_endpoint(
     """
     try:
         channel = await channel_manager.on_connect(ws)
-
         if await is_websocket_alive(ws):
 
             logger.info(f"Consumer connected - {channel}")
@@ -115,26 +118,31 @@ async def message_endpoint(
                     # Process the request here
                     await _process_consumer_request(request, channel, rpc)
 
-            except WebSocketDisconnect:
+            except (WebSocketDisconnect, WebSocketException):
                 # Handle client disconnects
                 logger.info(f"Consumer disconnected - {channel}")
-                await channel_manager.on_disconnect(ws)
-            except Exception as e:
-                logger.info(f"Consumer connection failed - {channel}")
-                logger.exception(e)
+            except RuntimeError:
                 # Handle cases like -
                 # RuntimeError('Cannot call "send" once a closed message has been sent')
+                pass
+            except Exception as e:
+                logger.info(f"Consumer connection failed - {channel}: {e}")
+                logger.debug(e, exc_info=e)
+            finally:
                 await channel_manager.on_disconnect(ws)
 
         else:
+            if channel:
+                await channel_manager.on_disconnect(ws)
             await ws.close()
 
     except RuntimeError:
         # WebSocket was closed
-        await channel_manager.on_disconnect(ws)
-
+        # Do nothing
+        pass
     except Exception as e:
         logger.error(f"Failed to serve - {ws.client}")
         # Log tracebacks to keep track of what errors are happening
         logger.exception(e)
+    finally:
         await channel_manager.on_disconnect(ws)

freqtrade/rpc/api_server/webserver.py

@@ -245,6 +245,7 @@ class ApiServer(RPCHandler):
                                   use_colors=False,
                                   log_config=None,
                                   access_log=True if verbosity != 'error' else False,
+                                  ws_ping_interval=None  # We do this explicitly ourselves
                                   )
         try:
             self._server = UvicornServer(uvconfig)

freqtrade/rpc/api_server/ws/channel.py

@@ -1,6 +1,7 @@
+import asyncio
 import logging
 from threading import RLock
-from typing import List, Optional, Type
+from typing import Any, Dict, List, Optional, Type
 from uuid import uuid4
 
 from fastapi import WebSocket as FastAPIWebSocket
@@ -34,6 +35,8 @@ class WebSocketChannel:
         self._serializer_cls = serializer_cls
 
         self._subscriptions: List[str] = []
+        self.queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue(maxsize=32)
+        self._relay_task = asyncio.create_task(self.relay())
 
         # Internal event to signify a closed websocket
         self._closed = False
@@ -48,12 +51,18 @@ class WebSocketChannel:
     def remote_addr(self):
         return self._websocket.remote_addr
 
-    async def send(self, data):
+    async def _send(self, data):
         """
         Send data on the wrapped websocket
         """
         await self._wrapped_ws.send(data)
 
+    async def send(self, data):
+        """
+        Add the data to the queue to be sent
+        """
+        self.queue.put_nowait(data)
+
     async def recv(self):
         """
         Receive data on the wrapped websocket
@@ -72,6 +81,7 @@ class WebSocketChannel:
         """
 
         self._closed = True
+        self._relay_task.cancel()
 
     def is_closed(self) -> bool:
         """
@@ -95,6 +105,26 @@ class WebSocketChannel:
         """
         return message_type in self._subscriptions
 
+    async def relay(self):
+        """
+        Relay messages from the channel's queue and send them out. This is started
+        as a task.
+        """
+        while True:
+            message = await self.queue.get()
+            try:
+                await self._send(message)
+                self.queue.task_done()
+
+                # Limit messages per sec.
+                # Could cause problems with queue size if too low, and
+                # problems with network traffik if too high.
+                # 0.001 = 1000/s
+                await asyncio.sleep(0.001)
+            except RuntimeError:
+                # The connection was closed, just exit the task
+                return
+
 
 class ChannelManager:
     def __init__(self):
@@ -155,12 +185,12 @@ class ChannelManager:
         with self._lock:
             message_type = data.get('type')
             for websocket, channel in self.channels.copy().items():
-                try:
-                    if channel.subscribed_to(message_type):
+                if channel.subscribed_to(message_type):
+                    if not channel.queue.full():
                         await channel.send(data)
-                except RuntimeError:
-                    # Handle cannot send after close cases
-                    await self.on_disconnect(websocket)
+                    else:
+                        logger.info(f"Channel {channel} is too far behind, disconnecting")
+                        await self.on_disconnect(websocket)
 
     async def send_direct(self, channel, data):
         """

freqtrade/rpc/discord.py

@@ -11,13 +11,12 @@ logger = logging.getLogger(__name__)
 
 class Discord(Webhook):
     def __init__(self, rpc: 'RPC', config: Config):
-        # super().__init__(rpc, config)
+        self._config = config
         self.rpc = rpc
-        self.config = config
         self.strategy = config.get('strategy', '')
         self.timeframe = config.get('timeframe', '')
 
-        self._url = self.config['discord']['webhook_url']
+        self._url = config['discord']['webhook_url']
         self._format = 'json'
         self._retries = 1
         self._retry_delay = 0.1
@@ -31,19 +30,21 @@ class Discord(Webhook):
 
     def send_msg(self, msg) -> None:
 
-        if msg['type'].value in self.config['discord']:
+        if msg['type'].value in self._config['discord']:
             logger.info(f"Sending discord message: {msg}")
 
             msg['strategy'] = self.strategy
             msg['timeframe'] = self.timeframe
-            fields = self.config['discord'].get(msg['type'].value)
+            fields = self._config['discord'].get(msg['type'].value)
             color = 0x0000FF
             if msg['type'] in (RPCMessageType.EXIT, RPCMessageType.EXIT_FILL):
                 profit_ratio = msg.get('profit_ratio')
                 color = (0x00FF00 if profit_ratio > 0 else 0xFF0000)
-
+            title = msg['type'].value
+            if 'pair' in msg:
+                title = f"Trade: {msg['pair']} {msg['type'].value}"
             embeds = [{
-                'title': f"Trade: {msg['pair']} {msg['type'].value}",
+                'title': title,
                 'color': color,
                 'fields': [],
 
@@ -51,7 +52,7 @@ class Discord(Webhook):
             for f in fields:
                 for k, v in f.items():
                     v = v.format(**msg)
-                    embeds[0]['fields'].append(  # type: ignore
+                    embeds[0]['fields'].append(
                         {'name': k, 'value': v, 'inline': True})
 
             # Send the message to discord channel

freqtrade/rpc/external_message_consumer.py

@@ -62,7 +62,7 @@ class ExternalMessageConsumer:
         self.enabled = self._emc_config.get('enabled', False)
         self.producers: List[Producer] = self._emc_config.get('producers', [])
 
-        self.wait_timeout = self._emc_config.get('wait_timeout', 300)  # in seconds
+        self.wait_timeout = self._emc_config.get('wait_timeout', 30)  # in seconds
         self.ping_timeout = self._emc_config.get('ping_timeout', 10)  # in seconds
         self.sleep_time = self._emc_config.get('sleep_time', 10)  # in seconds
 
@@ -174,6 +174,7 @@ class ExternalMessageConsumer:
         :param producer: Dictionary containing producer info
         :param lock: An asyncio Lock
         """
+        channel = None
         while self._running:
             try:
                 host, port = producer['host'], producer['port']
@@ -182,7 +183,11 @@ class ExternalMessageConsumer:
                 ws_url = f"ws://{host}:{port}/api/v1/message/ws?token={token}"
 
                 # This will raise InvalidURI if the url is bad
-                async with websockets.connect(ws_url, max_size=self.message_size_limit) as ws:
+                async with websockets.connect(
+                    ws_url,
+                    max_size=self.message_size_limit,
+                    ping_interval=None
+                ) as ws:
                     channel = WebSocketChannel(ws, channel_id=name)
 
                     logger.info(f"Producer connection success - {channel}")
@@ -224,6 +229,10 @@ class ExternalMessageConsumer:
                 logger.exception(e)
                 continue
 
+            finally:
+                if channel:
+                    await channel.close()
+
     async def _receive_messages(
         self,
         channel: WebSocketChannel,

freqtrade/rpc/rpc_manager.py

@@ -88,10 +88,13 @@ class RPCManager:
         """
         while queue:
             msg = queue.popleft()
-            self.send_msg({
-                'type': RPCMessageType.STRATEGY_MSG,
-                'msg': msg,
-            })
+            logger.info('Sending rpc strategy_msg: %s', msg)
+            for mod in self.registered_modules:
+                if mod._config.get(mod.name, {}).get('allow_custom_messages', False):
+                    mod.send_msg({
+                        'type': RPCMessageType.STRATEGY_MSG,
+                        'msg': msg,
+                    })
 
     def startup_messages(self, config: Config, pairlist, protections) -> None:
         if config['dry_run']:

freqtrade/rpc/webhook.py

@@ -3,7 +3,7 @@ This module manages webhook communication
 """
 import logging
 import time
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 from requests import RequestException, post
 
@@ -41,36 +41,44 @@ class Webhook(RPCHandler):
         """
         pass
 
+    def _get_value_dict(self, msg: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        whconfig = self._config['webhook']
+        # Deprecated 2022.10 - only keep generic method.
+        if msg['type'] in [RPCMessageType.ENTRY]:
+            valuedict = whconfig.get('webhookentry')
+        elif msg['type'] in [RPCMessageType.ENTRY_CANCEL]:
+            valuedict = whconfig.get('webhookentrycancel')
+        elif msg['type'] in [RPCMessageType.ENTRY_FILL]:
+            valuedict = whconfig.get('webhookentryfill')
+        elif msg['type'] == RPCMessageType.EXIT:
+            valuedict = whconfig.get('webhookexit')
+        elif msg['type'] == RPCMessageType.EXIT_FILL:
+            valuedict = whconfig.get('webhookexitfill')
+        elif msg['type'] == RPCMessageType.EXIT_CANCEL:
+            valuedict = whconfig.get('webhookexitcancel')
+        elif msg['type'] in (RPCMessageType.STATUS,
+                             RPCMessageType.STARTUP,
+                             RPCMessageType.WARNING):
+            valuedict = whconfig.get('webhookstatus')
+        elif msg['type'].value in whconfig:
+            # Allow all types ...
+            valuedict = whconfig.get(msg['type'].value)
+        elif msg['type'] in (
+                RPCMessageType.PROTECTION_TRIGGER,
+                RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
+                RPCMessageType.WHITELIST,
+                RPCMessageType.ANALYZED_DF,
+                RPCMessageType.STRATEGY_MSG):
+            # Don't fail for non-implemented types
+            return None
+        return valuedict
+
     def send_msg(self, msg: Dict[str, Any]) -> None:
         """ Send a message to telegram channel """
         try:
-            whconfig = self._config['webhook']
-            if msg['type'] in [RPCMessageType.ENTRY]:
-                valuedict = whconfig.get('webhookentry')
-            elif msg['type'] in [RPCMessageType.ENTRY_CANCEL]:
-                valuedict = whconfig.get('webhookentrycancel')
-            elif msg['type'] in [RPCMessageType.ENTRY_FILL]:
-                valuedict = whconfig.get('webhookentryfill')
-            elif msg['type'] == RPCMessageType.EXIT:
-                valuedict = whconfig.get('webhookexit')
-            elif msg['type'] == RPCMessageType.EXIT_FILL:
-                valuedict = whconfig.get('webhookexitfill')
-            elif msg['type'] == RPCMessageType.EXIT_CANCEL:
-                valuedict = whconfig.get('webhookexitcancel')
-            elif msg['type'] in (RPCMessageType.STATUS,
-                                 RPCMessageType.STARTUP,
-                                 RPCMessageType.WARNING):
-                valuedict = whconfig.get('webhookstatus')
-            elif msg['type'] in (
-                    RPCMessageType.PROTECTION_TRIGGER,
-                    RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
-                    RPCMessageType.WHITELIST,
-                    RPCMessageType.ANALYZED_DF,
-                    RPCMessageType.STRATEGY_MSG):
-                # Don't fail for non-implemented types
-                return
-            else:
-                raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
+
+            valuedict = self._get_value_dict(msg)
+
             if not valuedict:
                 logger.info("Message type '%s' not configured for webhooks", msg['type'])
                 return

freqtrade/strategy/interface.py

@@ -49,7 +49,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
     _ft_params_from_file: Dict
     # associated minimal roi
-    minimal_roi: Dict = {}
+    minimal_roi: Dict = {"0": 10.0}
 
     # associated stoploss
     stoploss: float
@@ -1085,9 +1085,7 @@ class IStrategy(ABC, HyperStrategyMixin):
             else:
                 logger.warning("CustomStoploss function did not return valid stoploss")
 
-        sl_lower_long = (trade.stop_loss < (low or current_rate) and not trade.is_short)
-        sl_higher_short = (trade.stop_loss > (high or current_rate) and trade.is_short)
-        if self.trailing_stop and (sl_lower_long or sl_higher_short):
+        if self.trailing_stop and dir_correct:
             # trailing stoploss handling
             sl_offset = self.trailing_stop_positive_offset
 
@@ -1101,7 +1099,7 @@ class IStrategy(ABC, HyperStrategyMixin):
                 if self.trailing_stop_positive is not None and bound_profit > sl_offset:
                     stop_loss_value = self.trailing_stop_positive
                     logger.debug(f"{trade.pair} - Using positive stoploss: {stop_loss_value} "
-                                 f"offset: {sl_offset:.4g} profit: {current_profit:.2%}")
+                                 f"offset: {sl_offset:.4g} profit: {bound_profit:.2%}")
 
                 trade.adjust_stop_loss(bound or current_rate, stop_loss_value)
 

requirements-dev.txt

@@ -8,23 +8,23 @@
 coveralls==3.3.1
 flake8==5.0.4
 flake8-tidy-imports==4.8.0
-mypy==0.981
+mypy==0.982
 pre-commit==2.20.0
 pytest==7.1.3
 pytest-asyncio==0.19.0
 pytest-cov==4.0.0
-pytest-mock==3.9.0
+pytest-mock==3.10.0
 pytest-random-order==1.0.4
 isort==5.10.1
 # For datetime mocking
 time-machine==2.8.2
 
 # Convert jupyter notebooks to markdown documents
-nbconvert==7.0.0
+nbconvert==7.2.1
 
 # mypy types
 types-cachetools==5.2.1
 types-filelock==3.2.7
-types-requests==2.28.11
-types-tabulate==0.8.11
-types-python-dateutil==2.8.19
+types-requests==2.28.11.2
+types-tabulate==0.9.0.0
+types-python-dateutil==2.8.19.1

requirements-freqai.txt

@@ -5,5 +5,6 @@
 scikit-learn==1.1.2
 joblib==1.2.0
 catboost==1.1; platform_machine != 'aarch64'
-lightgbm==3.3.2
+lightgbm==3.3.3
 xgboost==1.6.2
+tensorboard==2.10.1

requirements-hyperopt.txt

@@ -2,7 +2,7 @@
 -r requirements.txt
 
 # Required for hyperopt
-scipy==1.9.1
+scipy==1.9.2
 scikit-learn==1.1.2
 scikit-optimize==0.9.0
 filelock==3.8.0

requirements.txt

@@ -1,14 +1,14 @@
-numpy==1.23.3
+numpy==1.23.4
 pandas==1.5.0; platform_machine != 'armv7l'
 # Piwheels doesn't have 1.5.0 yet.
 pandas==1.4.3; platform_machine == 'armv7l'
 pandas-ta==0.3.14b
 
-ccxt==1.95.2
+ccxt==2.0.25
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==38.0.1
 aiohttp==3.8.3
-SQLAlchemy==1.4.41
+SQLAlchemy==1.4.42
 python-telegram-bot==13.14
 arrow==1.2.3
 cachetools==4.2.2
@@ -17,7 +17,7 @@ urllib3==1.26.12
 jsonschema==4.16.0
 TA-Lib==0.4.25
 technical==1.3.0
-tabulate==0.8.10
+tabulate==0.9.0
 pycoingecko==3.0.0
 jinja2==3.1.2
 tables==3.7.0
@@ -37,7 +37,7 @@ orjson==3.8.0
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.85.0
+fastapi==0.85.1
 pydantic>=1.8.0
 uvicorn==0.18.3
 pyjwt==2.5.0

scripts/ws_client.py

@@ -82,7 +82,7 @@ def readable_timedelta(delta):
     """
     attrs = ['years', 'months', 'days', 'hours', 'minutes', 'seconds', 'microseconds']
     return ", ".join([
-        '%d %s' % (getattr(delta, attr), attr if getattr(delta, attr) > 1 else attr[:-1])
+        '%d %s' % (getattr(delta, attr), attr if getattr(delta, attr) > 0 else attr[:-1])
         for attr in attrs if getattr(delta, attr)
     ])
 
@@ -170,7 +170,7 @@ class ClientProtocol:
 
     def _calculate_time_difference(self):
         old_last_received_at = self._LAST_RECEIVED_AT
-        self._LAST_RECEIVED_AT = time.time() * 1000
+        self._LAST_RECEIVED_AT = time.time() * 1e6
         time_delta = relativedelta(microseconds=(self._LAST_RECEIVED_AT - old_last_received_at))
 
         return readable_timedelta(time_delta)
@@ -238,7 +238,7 @@ async def create_client(
 
                     except (
                         asyncio.TimeoutError,
-                        websockets.exceptions.ConnectionClosed
+                        websockets.exceptions.WebSocketException
                     ):
                         # Try pinging
                         try:
@@ -298,7 +298,7 @@ async def _main(args):
     producers = emc_config.get('producers', [])
     producer = producers[0]
 
-    wait_timeout = emc_config.get('wait_timeout', 300)
+    wait_timeout = emc_config.get('wait_timeout', 30)
     ping_timeout = emc_config.get('ping_timeout', 10)
     sleep_time = emc_config.get('sleep_time', 10)
     message_size_limit = (emc_config.get('message_size_limit', 8) << 20)
@@ -311,7 +311,8 @@ async def _main(args):
         sleep_time=sleep_time,
         ping_timeout=ping_timeout,
         wait_timeout=wait_timeout,
-        max_size=message_size_limit
+        max_size=message_size_limit,
+        ping_interval=None
     )
 
 

tests/commands/test_commands.py

@@ -18,6 +18,7 @@ from freqtrade.commands import (start_backtesting_show, start_convert_data, star
 from freqtrade.commands.db_commands import start_convert_db
 from freqtrade.commands.deploy_commands import (clean_ui_subdir, download_and_install_ui,
                                                 get_ui_download_url, read_ui_version)
+from freqtrade.commands.list_commands import start_list_freqAI_models
 from freqtrade.configuration import setup_utils_configuration
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
@@ -944,6 +945,34 @@ def test_start_list_strategies(capsys):
     assert str(Path("broken_strats/broken_futures_strategies.py")) in captured.out
 
 
+def test_start_list_freqAI_models(capsys):
+
+    args = [
+        "list-freqaimodels",
+        "-1"
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_list_freqAI_models(pargs)
+    captured = capsys.readouterr()
+    assert "LightGBMClassifier" in captured.out
+    assert "LightGBMRegressor" in captured.out
+    assert "XGBoostRegressor" in captured.out
+    assert "<builtin>/LightGBMRegressor.py" not in captured.out
+
+    args = [
+        "list-freqaimodels",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    start_list_freqAI_models(pargs)
+    captured = capsys.readouterr()
+    assert "LightGBMClassifier" in captured.out
+    assert "LightGBMRegressor" in captured.out
+    assert "XGBoostRegressor" in captured.out
+    assert "<builtin>/LightGBMRegressor.py" in captured.out
+
+
 def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
     patch_exchange(mocker, mock_markets=True)
     mocker.patch.multiple('freqtrade.exchange.Exchange',

tests/exchange/test_binance.py

@@ -5,7 +5,7 @@ from unittest.mock import MagicMock, PropertyMock
 import ccxt
 import pytest
 
-from freqtrade.enums import MarginMode, TradingMode
+from freqtrade.enums import CandleType, MarginMode, TradingMode
 from freqtrade.exceptions import DependencyException, InvalidOrderException, OperationalException
 from tests.conftest import get_mock_coro, get_patched_exchange, log_has_re
 from tests.exchange.test_exchange import ccxt_exceptionhandlers
@@ -542,7 +542,7 @@ def test__set_leverage_binance(mocker, default_conf):
 
 
 @pytest.mark.asyncio
-@pytest.mark.parametrize('candle_type', ['mark', ''])
+@pytest.mark.parametrize('candle_type', [CandleType.MARK, ''])
 async def test__async_get_historic_ohlcv_binance(default_conf, mocker, caplog, candle_type):
     ohlcv = [
         [

tests/exchange/test_ccxt_compat.py

@@ -56,7 +56,7 @@ EXCHANGES = {
         'leverage_in_spot_market': True,
     },
     'kucoin': {
-        'pair': 'BTC/USDT',
+        'pair': 'XRP/USDT',
         'stake_currency': 'USDT',
         'hasQuoteVolume': True,
         'timeframe': '5m',
@@ -268,9 +268,8 @@ class TestCCXTExchange():
         now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2))
         assert exchange.klines(pair_tf).iloc[-1]['date'] >= timeframe_to_prev_date(timeframe, now)
 
-    def ccxt__async_get_candle_history(self, exchange, exchangename, pair, timeframe):
+    def ccxt__async_get_candle_history(self, exchange, exchangename, pair, timeframe, candle_type):
 
-        candle_type = CandleType.SPOT
         timeframe_ms = timeframe_to_msecs(timeframe)
         now = timeframe_to_prev_date(
                 timeframe, datetime.now(timezone.utc))
@@ -302,7 +301,8 @@ class TestCCXTExchange():
             return
         pair = EXCHANGES[exchangename]['pair']
         timeframe = EXCHANGES[exchangename]['timeframe']
-        self.ccxt__async_get_candle_history(exchange, exchangename, pair, timeframe)
+        self.ccxt__async_get_candle_history(
+            exchange, exchangename, pair, timeframe, CandleType.SPOT)
 
     def test_ccxt__async_get_candle_history_futures(self, exchange_futures):
         exchange, exchangename = exchange_futures
@@ -311,7 +311,8 @@ class TestCCXTExchange():
             return
         pair = EXCHANGES[exchangename].get('futures_pair', EXCHANGES[exchangename]['pair'])
         timeframe = EXCHANGES[exchangename]['timeframe']
-        self.ccxt__async_get_candle_history(exchange, exchangename, pair, timeframe)
+        self.ccxt__async_get_candle_history(
+            exchange, exchangename, pair, timeframe, CandleType.FUTURES)
 
     def test_ccxt_fetch_funding_rate_history(self, exchange_futures):
         exchange, exchangename = exchange_futures

tests/exchange/test_exchange.py

@@ -1834,6 +1834,7 @@ def test_get_tickers(default_conf, mocker, exchange_name):
         'last': 41,
     }
     }
+    mocker.patch('freqtrade.exchange.exchange.Exchange.exchange_has', return_value=True)
     api_mock.fetch_tickers = MagicMock(return_value=tick)
     api_mock.fetch_bids_asks = MagicMock(return_value={})
     exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
@@ -1883,6 +1884,11 @@ def test_get_tickers(default_conf, mocker, exchange_name):
     assert api_mock.fetch_tickers.call_count == 1
     assert api_mock.fetch_bids_asks.call_count == (1 if exchange_name == 'binance' else 0)
 
+    api_mock.fetch_tickers.reset_mock()
+    api_mock.fetch_bids_asks.reset_mock()
+    mocker.patch('freqtrade.exchange.exchange.Exchange.exchange_has', return_value=False)
+    assert exchange.get_tickers() == {}
+
 
 @pytest.mark.parametrize("exchange_name", EXCHANGES)
 def test_fetch_ticker(default_conf, mocker, exchange_name):
@@ -2190,6 +2196,9 @@ def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_mach
     time_machine.move_to(start + timedelta(hours=99, minutes=30))
 
     exchange = get_patched_exchange(mocker, default_conf)
+    mocker.patch("freqtrade.exchange.Exchange.ohlcv_candle_limit", return_value=100)
+    assert exchange._startup_candle_count == 0
+
     exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
     pair1 = ('IOTA/ETH', '1h', candle_type)
     pair2 = ('XRP/ETH', '1h', candle_type)
@@ -2230,30 +2239,36 @@ def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_mach
     assert len(res) == 2
     assert len(res[pair1]) == 99
     assert len(res[pair2]) == 99
+    assert res[pair2].at[0, 'open']
     assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-1][0] // 1000
     refresh_pior = exchange._pairs_last_refresh_time[pair1]
 
-    # New candle on exchange - only return 50 candles (but one candle further)
-    new_startdate = (start + timedelta(hours=51)).strftime('%Y-%m-%d %H:%M')
-    ohlcv = generate_test_data_raw('1h', 50, new_startdate)
+    # New candle on exchange - return 100 candles - but skip one candle so we actually get 2 candles
+    # in one go
+    new_startdate = (start + timedelta(hours=2)).strftime('%Y-%m-%d %H:%M')
+    # mocker.patch("freqtrade.exchange.Exchange.ohlcv_candle_limit", return_value=100)
+    ohlcv = generate_test_data_raw('1h', 100, new_startdate)
     exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
     res = exchange.refresh_latest_ohlcv(pairs)
     assert exchange._api_async.fetch_ohlcv.call_count == 2
     assert len(res) == 2
     assert len(res[pair1]) == 100
     assert len(res[pair2]) == 100
+    # Verify index starts at 0
+    assert res[pair2].at[0, 'open']
     assert refresh_pior != exchange._pairs_last_refresh_time[pair1]
 
     assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-1][0] // 1000
     assert exchange._pairs_last_refresh_time[pair2] == ohlcv[-1][0] // 1000
     exchange._api_async.fetch_ohlcv.reset_mock()
 
-    # Retry same call - no action.
+    # Retry same call - from cache
     res = exchange.refresh_latest_ohlcv(pairs)
     assert exchange._api_async.fetch_ohlcv.call_count == 0
     assert len(res) == 2
     assert len(res[pair1]) == 100
     assert len(res[pair2]) == 100
+    assert res[pair2].at[0, 'open']
 
     # Move to distant future (so a 1 call would cause a hole in the data)
     time_machine.move_to(start + timedelta(hours=2000))
@@ -2266,6 +2281,7 @@ def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_mach
     # Cache eviction - new data.
     assert len(res[pair1]) == 99
     assert len(res[pair2]) == 99
+    assert res[pair2].at[0, 'open']
 
 
 @pytest.mark.asyncio
@@ -2339,7 +2355,8 @@ async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog):
     for _ in range(3):
         with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
             await exchange._async_get_candle_history(
-                "ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
+                "ETH/BTC", "5m", CandleType.SPOT,
+                since_ms=(arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
     assert num_log_has_re(msg, caplog) == 3
 
     caplog.clear()
@@ -2355,7 +2372,8 @@ async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog):
         for _ in range(3):
             with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
                 await exchange._async_get_candle_history(
-                    "ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
+                    "ETH/BTC", "5m", CandleType.SPOT,
+                    (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
         # Expect the "returned exception" message 12 times (4 retries * 3 (loop))
         assert num_log_has_re(msg, caplog) == 12
         assert num_log_has_re(msg2, caplog) == 9
@@ -4333,9 +4351,10 @@ def test__fetch_and_calculate_funding_fees_datetime_called(
     ('XLTCUSDT', 1, 'spot'),
     ('LTC/USD', 1, 'futures'),
     ('XLTCUSDT', 0.01, 'futures'),
-    ('ETH/USDT:USDT', 10, 'futures')
+    ('ETH/USDT:USDT', 10, 'futures'),
+    ('TORN/USDT:USDT', None, 'futures'),  # Don't fail for unavailable pairs.
 ])
-def est__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode):
+def test__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'

tests/freqai/conftest.py

@@ -107,6 +107,8 @@ def make_unfiltered_dataframe(mocker, freqai_conf):
     unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
                 strategy, corr_dataframes, base_dataframes, freqai.dk.pair
             )
+    for i in range(5):
+        unfiltered_dataframe[f'constant_{i}'] = i
 
     unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
 

tests/freqai/test_freqai_backtesting.py

@@ -26,7 +26,7 @@ def test_freqai_backtest_start_backtest_list(freqai_conf, mocker, testdatadir, c
         '--config', 'config.json',
         '--datadir', str(testdatadir),
         '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
-        '--timeframe', '1h',
+        '--timeframe', '1m',
         '--strategy-list', CURRENT_TEST_STRATEGY
     ]
     args = get_args(args)

tests/freqai/test_freqai_interface.py

@@ -30,6 +30,7 @@ def is_mac() -> bool:
 @pytest.mark.parametrize('model', [
     'LightGBMRegressor',
     'XGBoostRegressor',
+    'XGBoostRFRegressor',
     'CatboostRegressor',
     ])
 def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
@@ -55,10 +56,17 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
 
     data_load_timerange = TimeRange.parse_timerange("20180125-20180130")
     new_timerange = TimeRange.parse_timerange("20180127-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
+    freqai.train_timer("start", "ADA/BTC")
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
+    freqai.train_timer("stop", "ADA/BTC")
+    freqai.dd.save_metric_tracker_to_disk()
+    freqai.dd.save_drawer_to_disk()
 
+    assert Path(freqai.dk.full_path / "metric_tracker.json").is_file()
+    assert Path(freqai.dk.full_path / "pair_dictionary.json").is_file()
     assert Path(freqai.dk.data_path /
                 f"{freqai.dk.model_filename}_model.{model_save_ext}").is_file()
     assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
@@ -93,6 +101,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
@@ -111,6 +120,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
     'LightGBMClassifier',
     'CatboostClassifier',
     'XGBoostClassifier',
+    'XGBoostRFClassifier',
     ])
 def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
     if is_arm() and model == 'CatboostClassifier':
@@ -134,6 +144,7 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
     freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
                                         strategy, freqai.dk, data_load_timerange)
@@ -157,7 +168,7 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
         ("CatboostClassifier", 6, "freqai_test_classifier")
     ],
     )
-def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
+def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
     freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
     freqai_conf['runmode'] = RunMode.BACKTEST
     Trade.use_db = False
@@ -181,12 +192,23 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
     corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
 
     df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
+    for i in range(5):
+        df[f'%-constant_{i}'] = i
+        # df.loc[:, f'%-constant_{i}'] = i
 
     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) == num_files
+    assert log_has_re(
+        "Removed features ",
+        caplog,
+    )
+    assert log_has_re(
+        "Removed 5 features from prediction features, ",
+        caplog,
+    )
     Backtesting.cleanup()
     shutil.rmtree(Path(freqai.dk.full_path))
 
@@ -256,6 +278,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
     corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
 
     df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
+
     freqai.start_backtesting(df, metadata, freqai.dk)
 
     assert log_has_re(
@@ -312,6 +335,7 @@ def test_follow_mode(mocker, freqai_conf):
     freqai.dd.load_all_pair_histories(timerange, freqai.dk)
 
     df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
+
     freqai.start_live(df, metadata, strategy, freqai.dk)
 
     assert len(freqai.dk.return_dataframe.index) == 5702

tests/optimize/test_backtesting.py

@@ -97,7 +97,6 @@ def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
         'start_date': min_date,
         'end_date': max_date,
         'max_open_trades': 10,
-        'position_stacking': False,
     }
 
 
@@ -735,7 +734,6 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
         start_date=min_date,
         end_date=max_date,
         max_open_trades=10,
-        position_stacking=False,
     )
     results = result['results']
     assert not results.empty
@@ -799,6 +797,34 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
                 t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
 
 
+def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None:
+    # This strategy intentionally places unfillable orders.
+    default_conf['strategy'] = 'StrategyTestV3CustomEntryPrice'
+    default_conf['startup_candle_count'] = 0
+    # Cancel unfilled order after 4 minutes on 5m timeframe.
+    default_conf["unfilledtimeout"] = {"entry": 4}
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    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)
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    # Testing dataframe contains 11 candles. Expecting 10 timed out orders.
+    timerange = TimeRange('date', 'date', 1517227800, 1517231100)
+    data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
+                             timerange=timerange)
+    min_date, max_date = get_timerange(data)
+
+    result = backtesting.backtest(
+        processed=deepcopy(data),
+        start_date=min_date,
+        end_date=max_date,
+        max_open_trades=1,
+    )
+
+    assert result['timedout_entry_orders'] == 10
+
+
 def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None:
     default_conf['use_exit_signal'] = False
     mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
@@ -819,7 +845,6 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
         start_date=min_date,
         end_date=max_date,
         max_open_trades=1,
-        position_stacking=False,
     )
     assert not results['results'].empty
     assert len(results['results']) == 1
@@ -851,7 +876,6 @@ def test_backtest_trim_no_data_left(default_conf, fee, mocker, testdatadir) -> N
         start_date=min_date,
         end_date=max_date,
         max_open_trades=10,
-        position_stacking=False,
     )
 
 
@@ -906,7 +930,6 @@ def test_backtest_dataprovider_analyzed_df(default_conf, fee, mocker, testdatadi
         start_date=min_date,
         end_date=max_date,
         max_open_trades=10,
-        position_stacking=False,
     )
     assert count == 5
 
@@ -950,8 +973,6 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
             start_date=min_date,
             end_date=max_date,
             max_open_trades=1,
-            position_stacking=False,
-            enable_protections=default_conf.get('enable_protections', False),
         )
         assert len(results['results']) == numres
 
@@ -994,8 +1015,6 @@ def test_backtest_pricecontours(default_conf, fee, mocker, testdatadir,
         start_date=min_date,
         end_date=max_date,
         max_open_trades=1,
-        position_stacking=False,
-        enable_protections=default_conf.get('enable_protections', False),
     )
     assert len(results['results']) == expected
 
@@ -1107,7 +1126,6 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
         'start_date': min_date,
         'end_date': max_date,
         'max_open_trades': 3,
-        'position_stacking': False,
     }
 
     results = backtesting.backtest(**backtest_conf)
@@ -1130,7 +1148,6 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
         'start_date': min_date,
         'end_date': max_date,
         'max_open_trades': 1,
-        'position_stacking': False,
     }
     results = backtesting.backtest(**backtest_conf)
     assert len(evaluate_result_multi(results['results'], '5m', 1)) == 0

tests/optimize/test_backtesting_adjust_position.py

@@ -42,7 +42,6 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
         start_date=min_date,
         end_date=max_date,
         max_open_trades=10,
-        position_stacking=False,
     )
     results = result['results']
     assert not results.empty

tests/optimize/test_hyperopt.py

@@ -336,7 +336,7 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
     assert hasattr(hyperopt.backtesting.strategy, "advise_entry")
     assert hasattr(hyperopt, "max_open_trades")
     assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
-    assert hasattr(hyperopt, "position_stacking")
+    assert hasattr(hyperopt.backtesting, "_position_stacking")
 
 
 def test_hyperopt_format_results(hyperopt):
@@ -704,7 +704,7 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
     assert hasattr(hyperopt.backtesting.strategy, "advise_entry")
     assert hasattr(hyperopt, "max_open_trades")
     assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
-    assert hasattr(hyperopt, "position_stacking")
+    assert hasattr(hyperopt.backtesting, "_position_stacking")
 
 
 def test_simplified_interface_all_failed(mocker, hyperopt_conf, caplog) -> None:
@@ -778,7 +778,7 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
     assert hasattr(hyperopt.backtesting.strategy, "advise_entry")
     assert hasattr(hyperopt, "max_open_trades")
     assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
-    assert hasattr(hyperopt, "position_stacking")
+    assert hasattr(hyperopt.backtesting, "_position_stacking")
 
 
 def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
@@ -821,7 +821,7 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
     assert hasattr(hyperopt.backtesting.strategy, "advise_entry")
     assert hasattr(hyperopt, "max_open_trades")
     assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
-    assert hasattr(hyperopt, "position_stacking")
+    assert hasattr(hyperopt.backtesting, "_position_stacking")
 
 
 @pytest.mark.parametrize("space", [
@@ -910,8 +910,9 @@ def test_in_strategy_auto_hyperopt_with_parallel(mocker, hyperopt_conf, tmpdir,
     })
     hyperopt = Hyperopt(hyperopt_conf)
     hyperopt.backtesting.exchange.get_max_leverage = lambda *x, **xx: 1.0
-    hyperopt.backtesting.exchange.get_min_pair_stake_amount = lambda *x, **xx: 1.0
+    hyperopt.backtesting.exchange.get_min_pair_stake_amount = lambda *x, **xx: 0.00001
     hyperopt.backtesting.exchange.get_max_pair_stake_amount = lambda *x, **xx: 100.0
+    hyperopt.backtesting.exchange._markets = get_markets()
 
     assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto)
     assert isinstance(hyperopt.backtesting.strategy.buy_rsi, IntParameter)

tests/persistence/test_persistence.py

@@ -2404,8 +2404,10 @@ def test_Trade_object_idem():
         'get_enter_tag_performance',
         'get_mix_tag_performance',
         'get_trading_volume',
-
+        'from_json',
     )
+    EXCLUDES2 = ('trades', 'trades_open', 'bt_trades_open_pp', 'bt_open_open_trade_count',
+                 'total_profit')
 
     # Parent (LocalTrade) should have the same attributes
     for item in trade:
@@ -2416,7 +2418,7 @@ def test_Trade_object_idem():
     # Fails if only a column is added without corresponding parent field
     for item in localtrade:
         if (not item.startswith('__')
-                and item not in ('trades', 'trades_open', 'total_profit')
+                and item not in EXCLUDES2
                 and type(getattr(LocalTrade, item)) not in (property, FunctionType)):
             assert item in trade
 

tests/persistence/test_trade_fromjson.py

@@ -0,0 +1,181 @@
+from datetime import datetime, timezone
+
+from freqtrade.persistence.trade_model import Trade
+
+
+def test_trade_fromjson():
+    """Test the Trade.from_json() method."""
+    trade_string = """{
+        "trade_id": 25,
+        "pair": "ETH/USDT",
+        "base_currency": "ETH",
+        "quote_currency": "USDT",
+        "is_open": false,
+        "exchange": "binance",
+        "amount": 407.0,
+        "amount_requested": 102.92547026,
+        "stake_amount": 102.7494348,
+        "strategy": "SampleStrategy55",
+        "buy_tag": "Strategy2",
+        "enter_tag": "Strategy2",
+        "timeframe": 5,
+        "fee_open": 0.001,
+        "fee_open_cost": 0.1027494,
+        "fee_open_currency": "ETH",
+        "fee_close": 0.001,
+        "fee_close_cost": 0.1054944,
+        "fee_close_currency": "USDT",
+        "open_date": "2022-10-18 09:12:42",
+        "open_timestamp": 1666084362912,
+        "open_rate": 0.2518998249562391,
+        "open_rate_requested": 0.2516,
+        "open_trade_value": 102.62575199,
+        "close_date": "2022-10-18 09:45:22",
+        "close_timestamp": 1666086322208,
+        "realized_profit": 2.76315361,
+        "close_rate": 0.2592,
+        "close_rate_requested": 0.2592,
+        "close_profit": 0.026865,
+        "close_profit_pct": 2.69,
+        "close_profit_abs": 2.76315361,
+        "trade_duration_s": 1959,
+        "trade_duration": 32,
+        "profit_ratio": 0.02686,
+        "profit_pct": 2.69,
+        "profit_abs": 2.76315361,
+        "sell_reason": "no longer good",
+        "exit_reason": "no longer good",
+        "exit_order_status": "closed",
+        "stop_loss_abs": 0.1981,
+        "stop_loss_ratio": -0.216,
+        "stop_loss_pct": -21.6,
+        "stoploss_order_id": null,
+        "stoploss_last_update": null,
+        "stoploss_last_update_timestamp": null,
+        "initial_stop_loss_abs": 0.1981,
+        "initial_stop_loss_ratio": -0.216,
+        "initial_stop_loss_pct": -21.6,
+        "min_rate": 0.2495,
+        "max_rate": 0.2592,
+        "leverage": 1.0,
+        "interest_rate": 0.0,
+        "liquidation_price": null,
+        "is_short": false,
+        "trading_mode": "spot",
+        "funding_fees": 0.0,
+        "open_order_id": null,
+        "orders": [
+            {
+                "amount": 102.0,
+                "safe_price": 0.2526,
+                "ft_order_side": "buy",
+                "order_filled_timestamp": 1666084370887,
+                "ft_is_entry": true,
+                "pair": "ETH/USDT",
+                "order_id": "78404228",
+                "status": "closed",
+                "average": 0.2526,
+                "cost": 25.7652,
+                "filled": 102.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:12:42",
+                "order_timestamp": 1666084362684,
+                "order_filled_date": "2022-10-18 09:12:50",
+                "order_type": "limit",
+                "price": 0.2526,
+                "remaining": 0.0
+            },
+            {
+                "amount": 102.0,
+                "safe_price": 0.2517,
+                "ft_order_side": "buy",
+                "order_filled_timestamp": 1666084379056,
+                "ft_is_entry": true,
+                "pair": "ETH/USDT",
+                "order_id": "78405139",
+                "status": "closed",
+                "average": 0.2517,
+                "cost": 25.6734,
+                "filled": 102.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:12:57",
+                "order_timestamp": 1666084377681,
+                "order_filled_date": "2022-10-18 09:12:59",
+                "order_type": "limit",
+                "price": 0.2517,
+                "remaining": 0.0
+            },
+            {
+                "amount": 102.0,
+                "safe_price": 0.2517,
+                "ft_order_side": "buy",
+                "order_filled_timestamp": 1666084389644,
+                "ft_is_entry": true,
+                "pair": "ETH/USDT",
+                "order_id": "78405265",
+                "status": "closed",
+                "average": 0.2517,
+                "cost": 25.6734,
+                "filled": 102.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:13:03",
+                "order_timestamp": 1666084383295,
+                "order_filled_date": "2022-10-18 09:13:09",
+                "order_type": "limit",
+                "price": 0.2517,
+                "remaining": 0.0
+            },
+            {
+                "amount": 102.0,
+                "safe_price": 0.2516,
+                "ft_order_side": "buy",
+                "order_filled_timestamp": 1666084723521,
+                "ft_is_entry": true,
+                "pair": "ETH/USDT",
+                "order_id": "78405395",
+                "status": "closed",
+                "average": 0.2516,
+                "cost": 25.6632,
+                "filled": 102.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:13:13",
+                "order_timestamp": 1666084393920,
+                "order_filled_date": "2022-10-18 09:18:43",
+                "order_type": "limit",
+                "price": 0.2516,
+                "remaining": 0.0
+            },
+            {
+                "amount": 407.0,
+                "safe_price": 0.2592,
+                "ft_order_side": "sell",
+                "order_filled_timestamp": 1666086322198,
+                "ft_is_entry": false,
+                "pair": "ETH/USDT",
+                "order_id": "78432649",
+                "status": "closed",
+                "average": 0.2592,
+                "cost": 105.4944,
+                "filled": 407.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:45:21",
+                "order_timestamp": 1666086321435,
+                "order_filled_date": "2022-10-18 09:45:22",
+                "order_type": "market",
+                "price": 0.2592,
+                "remaining": 0.0
+            }
+        ]
+    }"""
+    trade = Trade.from_json(trade_string)
+
+    assert trade.id == 25
+    assert trade.pair == 'ETH/USDT'
+    assert trade.open_date == datetime(2022, 10, 18, 9, 12, 42, tzinfo=timezone.utc)
+    assert isinstance(trade.open_date, datetime)
+    assert trade.exit_reason == 'no longer good'
+
+    assert len(trade.orders) == 5
+    last_o = trade.orders[-1]
+    assert last_o.order_filled_date == datetime(2022, 10, 18, 9, 45, 22, tzinfo=timezone.utc)
+    assert isinstance(last_o.order_date, datetime)

tests/rpc/test_rpc_apiserver.py

@@ -1443,8 +1443,9 @@ def test_api_plot_config(botclient):
     assert isinstance(rc.json()['subplots'], dict)
 
 
-def test_api_strategies(botclient):
+def test_api_strategies(botclient, tmpdir):
     ftbot, client = botclient
+    ftbot.config['user_data_dir'] = Path(tmpdir)
 
     rc = client_get(client, f"{BASE_URI}/strategies")
 
@@ -1456,6 +1457,7 @@ def test_api_strategies(botclient):
         'InformativeDecoratorTest',
         'StrategyTestV2',
         'StrategyTestV3',
+        'StrategyTestV3CustomEntryPrice',
         'StrategyTestV3Futures',
         'freqai_test_classifier',
         'freqai_test_multimodel_strat',

tests/rpc/test_rpc_manager.py

@@ -99,6 +99,7 @@ def test_send_msg_telegram_error(mocker, default_conf, caplog) -> None:
 
 def test_process_msg_queue(mocker, default_conf, caplog) -> None:
     telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg')
+    default_conf['telegram']['allow_custom_messages'] = True
     mocker.patch('freqtrade.rpc.telegram.Telegram._init')
 
     freqtradebot = get_patched_freqtradebot(mocker, default_conf)
@@ -108,8 +109,8 @@ def test_process_msg_queue(mocker, default_conf, caplog) -> None:
     queue.append('Test message 2')
     rpc_manager.process_msg_queue(queue)
 
-    assert log_has("Sending rpc message: {'type': strategy_msg, 'msg': 'Test message'}", caplog)
-    assert log_has("Sending rpc message: {'type': strategy_msg, 'msg': 'Test message 2'}", caplog)
+    assert log_has("Sending rpc strategy_msg: Test message", caplog)
+    assert log_has("Sending rpc strategy_msg: Test message 2", caplog)
     assert telegram_mock.call_count == 2
 
 

tests/rpc/test_rpc_webhook.py

@@ -3,7 +3,6 @@
 from datetime import datetime, timedelta
 from unittest.mock import MagicMock
 
-import pytest
 from requests import RequestException
 
 from freqtrade.enums import ExitType, RPCMessageType
@@ -337,34 +336,18 @@ def test_exception_send_msg(default_conf, mocker, caplog):
                    caplog)
 
     default_conf["webhook"] = get_webhook_dict()
-    default_conf["webhook"]["webhookentry"]["value1"] = "{DEADBEEF:8f}"
+    default_conf["webhook"]["strategy_msg"] = {"value1": "{DEADBEEF:8f}"}
     msg_mock = MagicMock()
     mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
     webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
     msg = {
-        'type': RPCMessageType.ENTRY,
-        'exchange': 'Binance',
-        'pair': 'ETH/BTC',
-        'limit': 0.005,
-        'order_type': 'limit',
-        'stake_amount': 0.8,
-        'stake_amount_fiat': 500,
-        'stake_currency': 'BTC',
-        'fiat_currency': 'EUR'
+        'type': RPCMessageType.STRATEGY_MSG,
+        'msg': 'hello world',
     }
     webhook.send_msg(msg)
     assert log_has("Problem calling Webhook. Please check your webhook configuration. "
                    "Exception: 'DEADBEEF'", caplog)
 
-    msg_mock = MagicMock()
-    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
-    msg = {
-        'type': 'DEADBEEF',
-        'status': 'whatever'
-    }
-    with pytest.raises(NotImplementedError):
-        webhook.send_msg(msg)
-
     # Test no failure for not implemented but known messagetypes
     for e in RPCMessageType:
         msg = {

tests/strategy/strats/strategy_test_v3_custom_entry_price.py

@@ -0,0 +1,37 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+from datetime import datetime
+from typing import Optional
+
+from pandas import DataFrame
+from strategy_test_v3 import StrategyTestV3
+
+
+class StrategyTestV3CustomEntryPrice(StrategyTestV3):
+    """
+    Strategy used by tests freqtrade bot.
+    Please do not modify this strategy, it's  intended for internal use only.
+    Please look at the SampleStrategy in the user_data/strategy directory
+    or strategy repository https://github.com/freqtrade/freqtrade-strategies
+    for samples and inspiration.
+    """
+    new_entry_price: float = 0.001
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            dataframe['volume'] > 0,
+            'enter_long'] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return dataframe
+
+    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
+                           entry_tag: Optional[str], side: str, **kwargs) -> float:
+
+        return self.new_entry_price

tests/strategy/test_strategy_loading.py

@@ -32,24 +32,25 @@ def test_search_strategy():
 
 def test_search_all_strategies_no_failed():
     directory = Path(__file__).parent / "strats"
-    strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
+    strategies = StrategyResolver._search_all_objects(directory, enum_failed=False)
     assert isinstance(strategies, list)
-    assert len(strategies) == 9
+    assert len(strategies) == 10
     assert isinstance(strategies[0], dict)
 
 
 def test_search_all_strategies_with_failed():
     directory = Path(__file__).parent / "strats"
-    strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
+    strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
     assert isinstance(strategies, list)
-    assert len(strategies) == 10
+    assert len(strategies) == 11
     # with enum_failed=True search_all_objects() shall find 2 good strategies
     # and 1 which fails to load
-    assert len([x for x in strategies if x['class'] is not None]) == 9
+    assert len([x for x in strategies if x['class'] is not None]) == 10
+
     assert len([x for x in strategies if x['class'] is None]) == 1
 
     directory = Path(__file__).parent / "strats_nonexistingdir"
-    strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
+    strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
     assert len(strategies) == 0
 
 
@@ -77,10 +78,9 @@ def test_load_strategy_base64(dataframe_1m, caplog, default_conf):
 
 
 def test_load_strategy_invalid_directory(caplog, default_conf):
-    default_conf['strategy'] = 'StrategyTestV3'
     extra_dir = Path.cwd() / 'some/path'
-    with pytest.raises(OperationalException):
-        StrategyResolver._load_strategy(CURRENT_TEST_STRATEGY, config=default_conf,
+    with pytest.raises(OperationalException, match=r"Impossible to load Strategy.*"):
+        StrategyResolver._load_strategy('StrategyTestV333', config=default_conf,
                                         extra_dir=extra_dir)
 
     assert log_has_re(r'Path .*' + r'some.*path.*' + r'.* does not exist', caplog)
@@ -102,8 +102,8 @@ def test_load_strategy_noname(default_conf):
         StrategyResolver.load_strategy(default_conf)
 
 
-@pytest.mark.filterwarnings("ignore:deprecated")
-@pytest.mark.parametrize('strategy_name', ['StrategyTestV2'])
+@ pytest.mark.filterwarnings("ignore:deprecated")
+@ pytest.mark.parametrize('strategy_name', ['StrategyTestV2'])
 def test_strategy_pre_v3(dataframe_1m, default_conf, strategy_name):
     default_conf.update({'strategy': strategy_name})
 
@@ -349,7 +349,7 @@ def test_strategy_override_use_exit_profit_only(caplog, default_conf):
     assert log_has("Override strategy 'exit_profit_only' with value in config file: True.", caplog)
 
 
-@pytest.mark.filterwarnings("ignore:deprecated")
+@ pytest.mark.filterwarnings("ignore:deprecated")
 def test_missing_implements(default_conf, caplog):
 
     default_location = Path(__file__).parent / "strats"