Merge branch 'develop' into spice-rack

.github/workflows/ci.yml

@@ -272,6 +272,16 @@ jobs:
         pip install pyaml
         python build_helpers/pre_commit_update.py
 
+  pre-commit:
+    runs-on: ubuntu-22.04
+    steps:
+    - uses: actions/checkout@v3
+
+    - uses: actions/setup-python@v4
+      with:
+        python-version: "3.10"
+    - uses: pre-commit/action@v3.0.0
+
   docs_check:
     runs-on: ubuntu-20.04
     steps:
@@ -302,7 +312,7 @@ jobs:
 
   # Notify only once - when CI completes (and after deploy) in case it's successfull
   notify-complete:
-    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
     runs-on: ubuntu-20.04
     # Discord notification can't handle schedule events
     if: (github.event_name != 'schedule')
@@ -327,7 +337,7 @@ jobs:
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   deploy:
-    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
     runs-on: ubuntu-20.04
 
     if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'

config_examples/config_freqai.example.json

@@ -77,7 +77,8 @@
             "indicator_periods_candles": [
                 10,
                 20
-            ]
+            ],
+            "plot_feature_importance": false
         },
         "data_split_parameters": {
             "test_size": 0.33,

config_examples/config_full.example.json

@@ -172,7 +172,24 @@
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
         "username": "freqtrader",
-        "password": "SuperSecurePassword"
+        "password": "SuperSecurePassword",
+        "ws_token": "secret_ws_t0ken."
+    },
+    "external_message_consumer": {
+        "enabled": false,
+        "producers": [
+          {
+            "name": "default",
+            "host": "127.0.0.2",
+            "port": 8080,
+            "ws_token": "secret_ws_t0ken."
+          }
+        ],
+        "wait_timeout": 300,
+        "ping_timeout": 10,
+        "sleep_time": 10,
+        "remove_entry_exit_signals": false,
+        "message_size_limit": 8
     },
     "bot_name": "freqtrade",
     "db_url": "sqlite:///tradesv3.sqlite",

docker/Dockerfile.freqai

@@ -6,4 +6,3 @@ FROM ${sourceimage}:${sourcetag}
 COPY requirements-freqai.txt /freqtrade/
 
 RUN pip install -r requirements-freqai.txt --user --no-cache-dir
-

docs/advanced-hyperopt.md

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

docs/configuration.md

@@ -57,7 +57,7 @@ You can specify additional configuration files in `add_config_files`. Files spec
 This is similar to using multiple `--config` parameters, but simpler in usage as you don't have to specify all files for all commands.
 
 !!! Tip "Use multiple configuration files to keep secrets secret"
-    You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.  
+    You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.
     The 2nd file should only specify what you intend to override.
     If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
 
@@ -110,7 +110,7 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
         "stake_amount": "unlimited"
     }
     ```
-    
+
     If multiple files are in the `add_config_files` section, then they will be assumed to be at identical levels, having the last occurrence override the earlier config (unless a parent already defined such a key).
 
 ## Configuration parameters
@@ -225,14 +225,16 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `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
-| | **Rest API / FreqUI**
+| | **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
 | `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
 | `api_server.verbosity` | Logging verbosity. `info` will print all RPC Calls, while "error" will only display errors. <br>**Datatype:** Enum, either `info` or `error`. Defaults to `info`.
 | `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
 | `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `api_server.ws_token` | API token for the Message WebSocket. See the [API Server documentation](rest-api.md) for more details.  <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `bot_name` | Name of the bot. Passed via API to a client - can be shown to distinguish / name bots.<br> *Defaults to `freqtrade`*<br> **Datatype:** String
+| `external_message_consumer` | Enable [Producer/Consumer mode](producer-consumer.md) for more details. <br> **Datatype:** Dict
 | | **Other**
 | `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
 | `force_entry_enable` | Enables the RPC Commands to force a Trade entry. More information below. <br> **Datatype:** Boolean
@@ -659,17 +661,7 @@ You should also make sure to read the [Exchanges](exchanges.md) section of the d
 
 ### Using proxy with Freqtrade
 
-To use a proxy with freqtrade, add the kwarg `"aiohttp_trust_env"=true` to the `"ccxt_async_kwargs"` dict in the exchange section of the configuration.
+To use a proxy with freqtrade, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values.
-
-An example for this can be found in `config_examples/config_full.example.json`
-
-``` json
-"ccxt_async_config": {
-    "aiohttp_trust_env": true
-}
-```
-
-Then, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values
 
 ``` bash
 export HTTP_PROXY="http://addr:port"
@@ -677,6 +669,20 @@ export HTTPS_PROXY="http://addr:port"
 freqtrade
 ```
 
+#### Proxy just exchange requests
+
+To use a proxy just for exchange connections (skips/ignores telegram and coingecko) - you can also define the proxies as part of the ccxt configuration.
+
+``` json
+"ccxt_config": {
+    "aiohttp_proxy": "http://addr:port",
+    "proxies": {
+        "http": "http://addr:port",
+        "https": "http://addr:port"
+    },
+}
+```
+
 ## Next step
 
 Now you have configured your config.json, the next step is to [start your bot](bot-usage.md).

docs/data-download.md

@@ -26,7 +26,7 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                [--timerange TIMERANGE] [--dl-trades]
                                [--exchange EXCHANGE]
                                [-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
-                               [--data-format-ohlcv {json,jsongz,hdf5}]
+                               [--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
                                [--data-format-trades {json,jsongz,hdf5}]
                                [--trading-mode {spot,margin,futures}]
                                [--prepend]
@@ -55,7 +55,7 @@ optional arguments:
                         list. Default: `1m 5m`.
   --erase               Clean all existing data for the selected
                         exchange/pairs/timeframes.
-  --data-format-ohlcv {json,jsongz,hdf5}
+  --data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
                         Storage format for downloaded candle (OHLCV) data.
                         (default: `json`).
   --data-format-trades {json,jsongz,hdf5}
@@ -76,7 +76,7 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
@@ -179,9 +179,11 @@ freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT --
 
 Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
 
-* `json` (plain "text" json files)
+* `json` -  plain "text" json files
-* `jsongz` (a gzip-zipped version of json files)
+* `jsongz` - a gzip-zipped version of json files
-* `hdf5` (a high performance datastore)
+* `hdf5` - a high performance datastore
+* `feather` - a dataformat based on Apache Arrow
+* `parquet` - columnar datastore
 
 By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
 
@@ -200,38 +202,74 @@ If the default data-format has been changed during download, then the keys `data
 !!! Note
     You can convert between data-formats using the [convert-data](#sub-command-convert-data) and [convert-trade-data](#sub-command-convert-trade-data) methods.
 
+#### Dataformat comparison
+
+The following comparisons have been made with the following data, and by using the linux `time` command.
+
+```
+Found 6 pair / timeframe combinations.
++----------+-------------+--------+---------------------+---------------------+
+|     Pair |   Timeframe |   Type |                From |                  To |
+|----------+-------------+--------+---------------------+---------------------|
+| BTC/USDT |          5m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:25:00 |
+| ETH/USDT |          1m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:26:00 |
+| BTC/USDT |          1m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:30:00 |
+| XRP/USDT |          5m |   spot | 2018-05-04 08:10:00 | 2022-09-13 19:15:00 |
+| XRP/USDT |          1m |   spot | 2018-05-04 08:11:00 | 2022-09-13 19:22:00 |
+| ETH/USDT |          5m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:20:00 |
++----------+-------------+--------+---------------------+---------------------+
+```
+
+Timings have been taken in a not very scientific way with the following command, which forces reading the data into memory.
+
+``` bash
+time freqtrade list-data --show-timerange --data-format-ohlcv <dataformat>
+```
+
+|  Format | Size | timing |
+|------------|-------------|-------------|
+| `json` | 149Mb | 25.6s |
+| `jsongz` | 39Mb | 27s |
+| `hdf5` | 145Mb | 3.9s |
+| `feather` | 72Mb | 3.5s |
+| `parquet` | 83Mb | 3.8s |
+
+Size has been taken from the BTC/USDT 1m spot combination for the timerange specified above.
+
+To have a best performance/size mix, we recommend the use of either feather or parquet.
+
 #### Sub-command convert data
 
 ```
 usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                               [-d PATH] [--userdir PATH]
                               [-p PAIRS [PAIRS ...]] --format-from
-                              {json,jsongz,hdf5} --format-to
+                              {json,jsongz,hdf5,feather,parquet} --format-to
-                              {json,jsongz,hdf5} [--erase]
+                              {json,jsongz,hdf5,feather,parquet} [--erase]
-                              [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
                               [--exchange EXCHANGE]
+                              [-t TIMEFRAMES [TIMEFRAMES ...]]
                               [--trading-mode {spot,margin,futures}]
-                              [--candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]]
+                              [--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]]
 
 optional arguments:
   -h, --help            show this help message and exit
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
                         Limit command to these pairs. Pairs are space-
                         separated.
-  --format-from {json,jsongz,hdf5}
+  --format-from {json,jsongz,hdf5,feather,parquet}
                         Source format for data conversion.
-  --format-to {json,jsongz,hdf5}
+  --format-to {json,jsongz,hdf5,feather,parquet}
                         Destination format for data conversion.
   --erase               Clean all existing data for the selected
                         exchange/pairs/timeframes.
-  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
-                        Specify which tickers to download. Space-separated
-                        list. Default: `1m 5m`.
   --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
                         config is provided.
-  --trading-mode {spot,margin,futures}
+  -t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
                         Select Trading mode
-  --candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]
+  --candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]
                         Select candle type to use
 
 Common arguments:
@@ -245,7 +283,7 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
@@ -267,20 +305,24 @@ freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtr
 usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                     [-d PATH] [--userdir PATH]
                                     [-p PAIRS [PAIRS ...]] --format-from
-                                    {json,jsongz,hdf5} --format-to
+                                    {json,jsongz,hdf5,feather,parquet}
-                                    {json,jsongz,hdf5} [--erase]
+                                    --format-to
+                                    {json,jsongz,hdf5,feather,parquet}
+                                    [--erase] [--exchange EXCHANGE]
 
 optional arguments:
   -h, --help            show this help message and exit
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Show profits for only these pairs. Pairs are space-
+                        Limit command to these pairs. Pairs are space-
                         separated.
-  --format-from {json,jsongz,hdf5}
+  --format-from {json,jsongz,hdf5,feather,parquet}
                         Source format for data conversion.
-  --format-to {json,jsongz,hdf5}
+  --format-to {json,jsongz,hdf5,feather,parquet}
                         Destination format for data conversion.
   --erase               Clean all existing data for the selected
                         exchange/pairs/timeframes.
+  --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
+                        config is provided.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -293,7 +335,7 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
@@ -318,9 +360,9 @@ This command will allow you to repeat this last step for additional timeframes w
 usage: freqtrade trades-to-ohlcv [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                                  [-d PATH] [--userdir PATH]
                                  [-p PAIRS [PAIRS ...]]
-                                 [-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
+                                 [-t TIMEFRAMES [TIMEFRAMES ...]]
                                  [--exchange EXCHANGE]
-                                 [--data-format-ohlcv {json,jsongz,hdf5}]
+                                 [--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
                                  [--data-format-trades {json,jsongz,hdf5}]
 
 optional arguments:
@@ -328,12 +370,12 @@ optional arguments:
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
                         Limit command to these pairs. Pairs are space-
                         separated.
-  -t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
+  -t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
                         Specify which tickers to download. Space-separated
                         list. Default: `1m 5m`.
   --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
                         config is provided.
-  --data-format-ohlcv {json,jsongz,hdf5}
+  --data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
                         Storage format for downloaded candle (OHLCV) data.
                         (default: `json`).
   --data-format-trades {json,jsongz,hdf5}
@@ -351,7 +393,7 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
@@ -371,7 +413,7 @@ You can get a list of downloaded data using the `list-data` sub-command.
 ```
 usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                            [--userdir PATH] [--exchange EXCHANGE]
-                           [--data-format-ohlcv {json,jsongz,hdf5}]
+                           [--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
                            [-p PAIRS [PAIRS ...]]
                            [--trading-mode {spot,margin,futures}]
                            [--show-timerange]
@@ -380,13 +422,13 @@ optional arguments:
   -h, --help            show this help message and exit
   --exchange EXCHANGE   Exchange name (default: `bittrex`). Only valid if no
                         config is provided.
-  --data-format-ohlcv {json,jsongz,hdf5}
+  --data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
                         Storage format for downloaded candle (OHLCV) data.
                         (default: `json`).
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
                         Limit command to these pairs. Pairs are space-
                         separated.
-  --trading-mode {spot,margin,futures}
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
                         Select Trading mode
   --show-timerange      Show timerange available for available data. (May take
                         a while to calculate).
@@ -402,7 +444,7 @@ Common arguments:
                         `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
+  -d PATH, --datadir PATH, --data-dir PATH
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.

docs/exchanges.md

@@ -233,7 +233,7 @@ OKX requires a passphrase for each api key, you will therefore need to add this
 
 !!! Warning "Futures"
     OKX Futures has the concept of "position mode" - which can be Net or long/short (hedge mode).
-    Freqtrade supports both modes - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
+    Freqtrade supports both modes (we recommend to use net mode) - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
     OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
 
 ## Gate.io

docs/freqai.md

@@ -109,11 +109,12 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `indicator_max_period_candles` | **No longer used**. User must use the strategy set `startup_candle_count` which defines the maximum *period* used in `populate_any_indicators()` for indicator creation (timeframe independent). FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
 | `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set. <br> **Datatype:** List of positive integers.
 | `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](#stratifying-the-data-for-training-and-testing-the-model) <br> **Datatype:** Positive integer.
-| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean.
+| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
+| `plot_feature_importance` | Create an interactive feature importance plot for each model.<br> **Datatype:** Boolean.<br> **Datatype:** Boolean, defaults to `False`
 | `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** Positive float (typically < 1).
 | `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
 | `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
-| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan). <br> **Datatype:** Boolean. 
+| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan). <br> **Datatype:** Boolean.
 | `inlier_metric_window` | If set, FreqAI will add the `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`. Details of how the `inlier_metric` is computed can be found [here](#using-the-inliermetric) <br> **Datatype:** int. Default: 0
 | `noise_standard_deviation` | If > 0, FreqAI adds noise to the training features. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. Value should be kept relative to the normalized space between -1 and 1). In other words, since data is always normalized between -1 and 1 in FreqAI, the user can expect a `noise_standard_deviation: 0.05` to see 32% of data randomly increased/decreased by more than 2.5% (i.e. the percent of data falling within the first standard deviation). Good for preventing overfitting. <br> **Datatype:** int. Default: 0
 | `outlier_protection_percentage` | If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection while keeping the original dataset intact. If the outlier protection is triggered, no predictions will be made based on the training data. <br> **Datatype:** Float. Default: `30`
@@ -510,7 +511,7 @@ The FreqAI backtesting module can be executed with the following command:
 freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
 ```
 
-Backtesting mode requires the user to have the data [pre-downloaded](#downloading-data-for-backtesting) (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration). 
+Backtesting mode requires the user to have the data [pre-downloaded](#downloading-data-for-backtesting) (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
 
 If this command has never been executed with the existing config file, it will train a new model
 for each pair, for each backtesting window within the expanded `--timerange`.
@@ -538,7 +539,7 @@ Users need to have the data pre-downloaded in the same fashion as if they were d
 - It's not possible to hyperopt indicators in `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
 - The [Backtesting](#backtesting) instructions also apply to Hyperopt.
 
-The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. 
+The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
 
 A good example of a hyperoptable parameter in FreqAI is a value for `DI_values` beyond which we consider outliers and below which we consider inliers:
 
@@ -563,7 +564,7 @@ FreqAI will train have trained 8 separate models at the end of `--timerange` (be
     Although fractional `backtest_period_days` is allowed, the user should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
 
 ### Downloading data for backtesting
-Live/dry instances will download the data automatically for the user, but users who wish to use backtesting functionality still need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). FreqAI users need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that they have a sufficient amount of training data *before* the start of their backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` ([details](#setting-the-startupcandlecount)) from the beginning of the desired backtesting timerange. 
+Live/dry instances will download the data automatically for the user, but users who wish to use backtesting functionality still need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). FreqAI users need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that they have a sufficient amount of training data *before* the start of their backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` ([details](#setting-the-startupcandlecount)) from the beginning of the desired backtesting timerange.
 
 As an example, if we wish to backtest the `--timerange` above of `20210501-20210701`, and we use the example config which sets `train_period_days` to 15. The startup candle count is 40 on a maximum `include_timeframes` of 1h. We would need 20210501 - 15 days - 40 * 1h / 24 hours = 20210414 (16.7 days earlier than the start of the desired training timerange).
 
@@ -662,13 +663,13 @@ The test data is used to evaluate the performance of the model after training. I
 
 ### Using the `inlier_metric`
 
-The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points. 
+The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points.
 
-User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points). 
+User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points).
 
-This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`. 
+This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`.
 
-FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels. 
+FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels.
 
 This function does **not** remove outliers from the data set.
 

docs/producer-consumer.md

@@ -0,0 +1,163 @@
+# Producer / Consumer mode
+
+freqtrade provides a mechanism whereby an instance (also called `consumer`) may listen to messages from an upstream freqtrade instance (also called `producer`) using the message websocket. Mainly, `analyzed_df` and `whitelist` messages. This allows the reuse of computed indicators (and signals) for pairs in multiple bots without needing to compute them multiple times.
+
+See [Message Websocket](rest-api.md#message-websocket) in the Rest API docs for setting up the `api_server` configuration for your message websocket (this will be your producer).
+
+!!! Note
+    We strongly recommend to set `ws_token` to something random and known only to yourself to avoid unauthorized access to your bot.
+
+## Configuration
+
+Enable subscribing to an instance by adding the `external_message_consumer` section to the consumer's config file.
+
+```json
+{
+    //...
+   "external_message_consumer": {
+        "enabled": true,
+        "producers": [
+            {
+                "name": "default", // This can be any name you'd like, default is "default"
+                "host": "127.0.0.1", // The host from your producer's api_server config
+                "port": 8080, // The port from your producer's api_server config
+                "ws_token": "sercet_Ws_t0ken" // The ws_token from your producer's api_server config
+            }
+        ],
+        // The following configurations are optional, and usually not required
+        // "wait_timeout": 300,
+        // "ping_timeout": 10,
+        // "sleep_time": 10,
+        // "remove_entry_exit_signals": false,
+        // "message_size_limit": 8
+    }
+    //...
+}
+```
+
+|  Parameter | Description |
+|------------|-------------|
+| `enabled` | **Required.** Enable consumer mode. If set to false, all other settings in this section are ignored.<br>*Defaults to `false`.*<br> **Datatype:** boolean .
+| `producers` | **Required.** List of producers <br> **Datatype:** Array.
+| `producers.name` | **Required.** Name of this producer. This name must be used in calls to `get_producer_pairs()` and `get_producer_df()` if more than one producer is used.<br> **Datatype:** string
+| `producers.host` | **Required.** The hostname or IP address from your producer.<br> **Datatype:** string
+| `producers.port` | **Required.** The port matching the above host.<br> **Datatype:** string
+| `producers.ws_token` | **Required.**  `ws_token` as configured on the producer.<br> **Datatype:** string
+| | **Optional settings**
+| `wait_timeout` | Timeout until we ping again if no message is received. <br>*Defaults to `300`.*<br> **Datatype:** Integer - in seconds.
+| `wait_timeout` | Ping timeout <br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
+| `sleep_time` | Sleep time before retrying to connect.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
+| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
+| `message_size_limit` | Size limit per message<br>*Defaults to `8`.*<br> **Datatype:** Integer - Megabytes.
+
+Instead of (or as well as) calculating indicators in `populate_indicators()` the follower instance listens on the connection to a producer instance's messages (or multiple producer instances in advanced configurations) and requests the producer's most recently analyzed dataframes for each pair in the active whitelist.
+
+A consumer instance will then have a full copy of the analyzed dataframes without the need to calculate them itself.
+
+## Examples
+
+### Example - Producer Strategy
+
+A simple strategy with multiple indicators. No special considerations are required in the strategy itself.
+
+```py
+class ProducerStrategy(IStrategy):
+    #...
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Calculate indicators in the standard freqtrade way which can then be broadcast to other instances
+        """
+        dataframe['rsi'] = ta.RSI(dataframe)
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populates the entry signal for the given dataframe
+        """
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
+                (dataframe['tema'] <= dataframe['bb_middleband']) &
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &
+                (dataframe['volume'] > 0)
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+```
+
+!!! Tip "FreqAI"
+    You can use this to setup [FreqAI](freqai.md) on a powerful machine, while you run consumers on simple machines like raspberries, which can interpret the signals generated from the producer in different ways.
+
+
+### Example - Consumer Strategy
+
+A logically equivalent strategy which calculates no indicators itself, but will have the same analyzed dataframes available to make trading decisions based on the indicators calculated in the producer. In this example the consumer has the same entry criteria, however this is not necessary. The consumer may use different logic to enter/exit trades, and only use the indicators as specified.
+
+```py
+class ConsumerStrategy(IStrategy):
+    #...
+    process_only_new_candles = False # required for consumers
+
+    _columns_to_expect = ['rsi_default', 'tema_default', 'bb_middleband_default']
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Use the websocket api to get pre-populated indicators from another freqtrade instance.
+        Use `self.dp.get_producer_df(pair)` to get the dataframe
+        """
+        pair = metadata['pair']
+        timeframe = self.timeframe
+
+        producer_pairs = self.dp.get_producer_pairs()
+        # You can specify which producer to get pairs from via:
+        # self.dp.get_producer_pairs("my_other_producer")
+
+        # This func returns the analyzed dataframe, and when it was analyzed
+        producer_dataframe, _ = self.dp.get_producer_df(pair)
+        # You can get other data if the producer makes it available:
+        # self.dp.get_producer_df(
+        #   pair,
+        #   timeframe="1h",
+        #   candle_type=CandleType.SPOT,
+        #   producer_name="my_other_producer"
+        # )
+
+        if not producer_dataframe.empty:
+            # If you plan on passing the producer's entry/exit signal directly,
+            # specify ffill=False or it will have unintended results
+            merged_dataframe = merge_informative_pair(dataframe, producer_dataframe,
+                                                      timeframe, timeframe,
+                                                      append_timeframe=False,
+                                                      suffix="default")
+            return merged_dataframe
+        else:
+            dataframe[self._columns_to_expect] = 0
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populates the entry signal for the given dataframe
+        """
+        # Use the dataframe columns as if we calculated them ourselves
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi_default'], self.buy_rsi.value)) &
+                (dataframe['tema_default'] <= dataframe['bb_middleband_default']) &
+                (dataframe['tema_default'] > dataframe['tema_default'].shift(1)) &
+                (dataframe['volume'] > 0)
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+```
+
+!!! Tip "Using upstream signals"
+    By setting `remove_entry_exit_signals=false`, you can also use the producer's signals directly. They should be available as `enter_long_default` (assuming `suffix="default"` was used) - and can be used as either signal directly, or as additional indicator.

docs/requirements-docs.txt

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

docs/rest-api.md

@@ -31,7 +31,8 @@ Sample configuration:
         "jwt_secret_key": "somethingrandom",
         "CORS_origins": [],
         "username": "Freqtrader",
-        "password": "SuperSecret1!"
+        "password": "SuperSecret1!",
+        "ws_token": "sercet_Ws_t0ken"
     },
 ```
 
@@ -66,7 +67,7 @@ secrets.token_hex()
 
 !!! Danger "Password selection"
     Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
-    Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!). 
+    Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!).
 
 ### Configuration with docker
 
@@ -93,7 +94,6 @@ Make sure that the following 2 lines are available in your docker-compose file:
 !!! Danger "Security warning"
     By using `8080:8080` in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
 
-
 ## Rest API
 
 ### Consuming the API
@@ -274,7 +274,7 @@ reload_config
 	Reload configuration.
 
 show_config
-	
+
         Returns part of the configuration, relevant for trading operations.
 
 start
@@ -322,6 +322,73 @@ whitelist
 
 ```
 
+### Message WebSocket
+
+The API Server includes a websocket endpoint for subscribing to RPC messages from the freqtrade Bot.
+This can be used to consume real-time data from your bot, such as entry/exit fill messages, whitelist changes, populated indicators for pairs, and more.
+
+This is also used to setup [Producer/Consumer mode](producer-consumer.md) in Freqtrade.
+
+Assuming your rest API is set to `127.0.0.1` on port `8080`, the endpoint is available at `http://localhost:8080/api/v1/message/ws`.
+
+To access the websocket endpoint, the `ws_token` is required as a query parameter in the endpoint URL.
+
+To generate a safe `ws_token` you can run the following code:
+
+``` python
+>>> import secrets
+>>> secrets.token_urlsafe(25)
+'hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q'
+```
+
+You would then add that token under `ws_token` in your `api_server` config. Like so:
+
+``` json
+"api_server": {
+    "enabled": true,
+    "listen_ip_address": "127.0.0.1",
+    "listen_port": 8080,
+    "verbosity": "error",
+    "enable_openapi": false,
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": [],
+    "username": "Freqtrader",
+    "password": "SuperSecret1!",
+    "ws_token": "hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q" // <-----
+},
+```
+
+You can now connect to the endpoint at `http://localhost:8080/api/v1/message/ws?token=hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q`.
+
+!!! Danger "Reuse of example tokens"
+    Please do not use the above example token. To make sure you are secure, generate a completely new token.
+
+#### Using the WebSocket
+
+Once connected to the WebSocket, the bot will broadcast RPC messages to anyone who is subscribed to them. To subscribe to a list of messages, you must send a JSON request through the WebSocket like the one below. The `data` key must be a list of message type strings.
+
+``` json
+{
+  "type": "subscribe",
+  "data": ["whitelist", "analyzed_df"] // A list of string message types
+}
+```
+
+For a list of message types, please refer to the RPCMessageType enum in `freqtrade/enums/rpcmessagetype.py`
+
+Now anytime those types of RPC messages are sent in the bot, you will receive them through the WebSocket as long as the connection is active. They typically take the same form as the request:
+
+``` json
+{
+  "type": "analyzed_df",
+  "data": {
+      "key": ["NEO/BTC", "5m", "spot"],
+      "df": {}, // The dataframe
+      "la": "2022-09-08 22:14:41.457786+00:00"
+  }
+}
+```
+
 ### OpenAPI interface
 
 To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.

docs/strategy-advanced.md

@@ -106,6 +106,12 @@ def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_r
 !!! Note
     `enter_tag` is limited to 100 characters, remaining data will be truncated.
 
+!!! Warning
+    There is only one `enter_tag` column, which is used for both long and short trades.
+    As a consequence, this column must be treated as "last write wins" (it's just a dataframe column after all).
+    In fancy situations, where multiple signals collide (or if signals are deactivated again based on different conditions), this can lead to odd results with the wrong tag applied to an entry signal.
+    These results are a consequence of the strategy overwriting prior tags - where the last tag will "stick" and will be the one freqtrade will use.
+
 ## Exit tag
 
 Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.

docs/telegram-usage.md

@@ -82,6 +82,8 @@ Example configuration showing the different settings:
         "warning": "on",
         "startup": "off",
         "entry": "silent",
+        "entry_fill": "on",
+        "entry_cancel": "silent",
         "exit": {
             "roi": "silent",
             "emergency_exit": "on",
@@ -93,9 +95,7 @@ Example configuration showing the different settings:
             "custom_exit": "silent",
             "partial_exit": "on"
         },
-        "entry_cancel": "silent",
         "exit_cancel": "on",
-        "entry_fill": "off",
         "exit_fill": "off",
         "protection_trigger": "off",
         "protection_trigger_global": "on",

environment.yml

@@ -34,6 +34,7 @@ dependencies:
     - schedule
     - python-dateutil
     - joblib
+    - pyarrow
 
 
     # ============================

freqtrade/commands/arguments.py

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

freqtrade/commands/build_config_commands.py

@@ -211,6 +211,7 @@ def ask_user_config() -> Dict[str, Any]:
     )
     # Force JWT token to be a random string
     answers['api_server_jwt_key'] = secrets.token_hex()
+    answers['api_server_ws_token'] = secrets.token_urlsafe(25)
 
     return answers
 

freqtrade/commands/cli_options.py

@@ -69,7 +69,7 @@ AVAILABLE_CLI_OPTIONS = {
         metavar='PATH',
     ),
     "datadir": Arg(
-        '-d', '--datadir',
+        '-d', '--datadir', '--data-dir',
         help='Path to directory with historical backtesting data.',
         metavar='PATH',
     ),
@@ -440,7 +440,7 @@ AVAILABLE_CLI_OPTIONS = {
     "dataformat_trades": Arg(
         '--data-format-trades',
         help='Storage format for downloaded trades data. (default: `jsongz`).',
-        choices=constants.AVAILABLE_DATAHANDLERS,
+        choices=constants.AVAILABLE_DATAHANDLERS_TRADES,
     ),
     "show_timerange": Arg(
         '--show-timerange',

freqtrade/commands/deploy_commands.py

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

freqtrade/configuration/check_exchange.py

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

freqtrade/configuration/config_validation.py

@@ -1,4 +1,5 @@
 import logging
+from collections import Counter
 from copy import deepcopy
 from typing import Any, Dict
 
@@ -85,6 +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_consumers(conf)
     validate_migrated_strategy_settings(conf)
 
     # validate configuration before returning
@@ -332,6 +334,23 @@ def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
             'Using analyze-per-epoch parameter is not supported with a FreqAI strategy.')
 
 
+def _validate_consumers(conf: Dict[str, Any]) -> None:
+    emc_conf = conf.get('external_message_consumer', {})
+    if emc_conf.get('enabled', False):
+        if len(emc_conf.get('producers', [])) < 1:
+            raise OperationalException("You must specify at least 1 Producer to connect to.")
+
+        producer_names = [p['name'] for p in emc_conf.get('producers', [])]
+        duplicates = [item for item, count in Counter(producer_names).items() if count > 1]
+        if duplicates:
+            raise OperationalException(
+                f"Producer names must be unique. Duplicate: {', '.join(duplicates)}")
+        if conf.get('process_only_new_candles', True):
+            # Warning here or require it?
+            logger.warning("To receive best performance with external data, "
+                           "please set `process_only_new_candles` to False")
+
+
 def _strategy_settings(conf: Dict[str, Any]) -> None:
 
     process_deprecated_setting(conf, None, 'use_sell_signal', None, 'use_exit_signal')

freqtrade/configuration/configuration.py

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

freqtrade/configuration/deprecated_settings.py

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

freqtrade/configuration/directory_operations.py

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

freqtrade/configuration/load_config.py

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

freqtrade/constants.py

@@ -3,7 +3,7 @@
 """
 bot constants
 """
-from typing import List, Literal, Tuple
+from typing import Any, Dict, List, Literal, Tuple
 
 from freqtrade.enums import CandleType
 
@@ -36,7 +36,8 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
                        'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
                        'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
 AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
-AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
+AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5']
+AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['feather', 'parquet']
 BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
 BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
 BACKTEST_CACHE_DEFAULT = 'day'
@@ -243,6 +244,7 @@ CONF_SCHEMA = {
         'exchange': {'$ref': '#/definitions/exchange'},
         'edge': {'$ref': '#/definitions/edge'},
         'freqai': {'$ref': '#/definitions/freqai'},
+        'external_message_consumer': {'$ref': '#/definitions/external_message_consumer'},
         'experimental': {
             'type': 'object',
             'properties': {
@@ -289,11 +291,12 @@ CONF_SCHEMA = {
                         'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
                         'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
                         'entry': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
-                        'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
+                        'entry_fill': {
-                        'entry_fill': {'type': 'string',
+                            'type': 'string',
-                                       'enum': TELEGRAM_SETTING_OPTIONS,
+                            'enum': TELEGRAM_SETTING_OPTIONS,
-                                       'default': 'off'
+                            'default': 'off'
-                                       },
+                        },
+                        'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS, },
                         'exit': {
                             'type': ['string', 'object'],
                             'additionalProperties': {
@@ -301,12 +304,12 @@ CONF_SCHEMA = {
                                 'enum': TELEGRAM_SETTING_OPTIONS
                             }
                         },
-                        'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
                         'exit_fill': {
                             'type': 'string',
                             'enum': TELEGRAM_SETTING_OPTIONS,
                             'default': 'on'
                         },
+                        'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
                         'protection_trigger': {
                             'type': 'string',
                             'enum': TELEGRAM_SETTING_OPTIONS,
@@ -315,14 +318,17 @@ CONF_SCHEMA = {
                         'protection_trigger_global': {
                             'type': 'string',
                             'enum': TELEGRAM_SETTING_OPTIONS,
+                            'default': 'on'
                         },
                         'show_candle': {
                             'type': 'string',
                             'enum': ['off', 'ohlc'],
+                            'default': 'off'
                         },
                         'strategy_msg': {
                             'type': 'string',
                             'enum': TELEGRAM_SETTING_OPTIONS,
+                            'default': 'on'
                         },
                     }
                 },
@@ -400,6 +406,7 @@ CONF_SCHEMA = {
                 },
                 'username': {'type': 'string'},
                 'password': {'type': 'string'},
+                'ws_token': {'type': ['string', 'array'], 'items': {'type': 'string'}},
                 'jwt_secret_key': {'type': 'string'},
                 'CORS_origins': {'type': 'array', 'items': {'type': 'string'}},
                 'verbosity': {'type': 'string', 'enum': ['error', 'info']},
@@ -428,7 +435,7 @@ CONF_SCHEMA = {
         },
         'dataformat_trades': {
             'type': 'string',
-            'enum': AVAILABLE_DATAHANDLERS,
+            'enum': AVAILABLE_DATAHANDLERS_TRADES,
             'default': 'jsongz'
         },
         'position_adjustment_enable': {'type': 'boolean'},
@@ -484,6 +491,47 @@ CONF_SCHEMA = {
             },
             'required': ['process_throttle_secs', 'allowed_risk']
         },
+        'external_message_consumer': {
+            'type': 'object',
+            'properties': {
+                'enabled': {'type': 'boolean', 'default': False},
+                'producers': {
+                    'type': 'array',
+                    'items': {
+                        'type': 'object',
+                        'properties': {
+                            'name': {'type': 'string'},
+                            'host': {'type': 'string'},
+                            'port': {
+                                'type': 'integer',
+                                'default': 8080,
+                                'minimum': 0,
+                                'maximum': 65535
+                            },
+                            'ws_token': {'type': 'string'},
+                        },
+                        'required': ['name', 'host', 'ws_token']
+                    }
+                },
+                'wait_timeout': {'type': 'integer', 'minimum': 0},
+                'sleep_time': {'type': 'integer', 'minimum': 0},
+                'ping_timeout': {'type': 'integer', 'minimum': 0},
+                'remove_entry_exit_signals': {'type': 'boolean', 'default': False},
+                'initial_candle_limit': {
+                    'type': 'integer',
+                    'minimum': 0,
+                    'maximum': 1500,
+                    'default': 1500
+                },
+                'message_size_limit': {  # In megabytes
+                    'type': 'integer',
+                    'minimum': 1,
+                    'maxmium': 20,
+                    'default': 8,
+                }
+            },
+            'required': ['producers']
+        },
         "freqai": {
             "type": "object",
             "properties": {
@@ -504,6 +552,7 @@ CONF_SCHEMA = {
                         "weight_factor": {"type": "number", "default": 0},
                         "principal_component_analysis": {"type": "boolean", "default": False},
                         "use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
+                        "plot_feature_importance": {"type": "boolean", "default": False},
                         "svm_params": {"type": "object",
                                        "properties": {
                                            "shuffle": {"type": "boolean", "default": False},
@@ -603,3 +652,5 @@ LongShort = Literal['long', 'short']
 EntryExit = Literal['entry', 'exit']
 BuySell = Literal['buy', 'sell']
 MakerTaker = Literal['maker', 'taker']
+
+Config = Dict[str, Any]

freqtrade/data/converter.py

@@ -5,12 +5,12 @@ import itertools
 import logging
 from datetime import datetime, timezone
 from operator import itemgetter
-from typing import Any, Dict, List
+from typing import Dict, List
 
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, Config, TradeList
 from freqtrade.enums import CandleType
 
 
@@ -237,7 +237,7 @@ def trades_to_ohlcv(trades: TradeList, timeframe: str) -> DataFrame:
     return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
 
 
-def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
+def convert_trades_format(config: Config, convert_from: str, convert_to: str, erase: bool):
     """
     Convert trades from one format to another format.
     :param config: Config dictionary
@@ -263,7 +263,7 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
 
 
 def convert_ohlcv_format(
-    config: Dict[str, Any],
+    config: Config,
     convert_from: str,
     convert_to: str,
     erase: bool,
@@ -292,6 +292,7 @@ def convert_ohlcv_format(
                 timeframe,
                 candle_type=candle_type
             ))
+        config['pairs'] = sorted(set(config['pairs']))
     logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
 
     for timeframe in timeframes:
@@ -302,7 +303,7 @@ def convert_ohlcv_format(
                                   drop_incomplete=False,
                                   startup_candles=0,
                                   candle_type=candle_type)
-            logger.info(f"Converting {len(data)} {candle_type} candles for {pair}")
+            logger.info(f"Converting {len(data)} {timeframe} {candle_type} candles for {pair}")
             if len(data) > 0:
                 trg.ohlcv_store(
                     pair=pair,

freqtrade/data/dataprovider.py

@@ -12,11 +12,12 @@ from typing import Any, Dict, List, Optional, Tuple
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
+from freqtrade.constants import Config, ListPairsWithTimeframes, PairWithTimeframe
 from freqtrade.data.history import load_pair_history
-from freqtrade.enums import CandleType, RunMode
+from freqtrade.enums import CandleType, RPCMessageType, RunMode
 from freqtrade.exceptions import ExchangeError, OperationalException
 from freqtrade.exchange import Exchange, timeframe_to_seconds
+from freqtrade.rpc import RPCManager
 from freqtrade.util import PeriodicCache
 
 
@@ -28,17 +29,33 @@ MAX_DATAFRAME_CANDLES = 1000
 
 class DataProvider:
 
-    def __init__(self, config: dict, exchange: Optional[Exchange], pairlists=None) -> None:
+    def __init__(
+        self,
+        config: Config,
+        exchange: Optional[Exchange],
+        pairlists=None,
+        rpc: Optional[RPCManager] = None
+    ) -> None:
         self._config = config
         self._exchange = exchange
         self._pairlists = pairlists
+        self.__rpc = rpc
         self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
         self.__slice_index: Optional[int] = None
         self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {}
+        self.__producer_pairs_df: Dict[str,
+                                       Dict[PairWithTimeframe, Tuple[DataFrame, datetime]]] = {}
+        self.__producer_pairs: Dict[str, List[str]] = {}
         self._msg_queue: deque = deque()
 
+        self._default_candle_type = self._config.get('candle_type_def', CandleType.SPOT)
+        self._default_timeframe = self._config.get('timeframe', '1h')
+
         self.__msg_cache = PeriodicCache(
-            maxsize=1000, ttl=timeframe_to_seconds(self._config.get('timeframe', '1h')))
+            maxsize=1000, ttl=timeframe_to_seconds(self._default_timeframe))
+
+        self.producers = self._config.get('external_message_consumer', {}).get('producers', [])
+        self.external_data_enabled = len(self.producers) > 0
 
     def _set_dataframe_max_index(self, limit_index: int):
         """
@@ -63,9 +80,110 @@ class DataProvider:
         :param dataframe: analyzed dataframe
         :param candle_type: Any of the enum CandleType (must match trading mode!)
         """
-        self.__cached_pairs[(pair, timeframe, candle_type)] = (
+        pair_key = (pair, timeframe, candle_type)
+        self.__cached_pairs[pair_key] = (
             dataframe, datetime.now(timezone.utc))
 
+    # For multiple producers we will want to merge the pairlists instead of overwriting
+    def _set_producer_pairs(self, pairlist: List[str], producer_name: str = "default"):
+        """
+        Set the pairs received to later be used.
+
+        :param pairlist: List of pairs
+        """
+        self.__producer_pairs[producer_name] = pairlist
+
+    def get_producer_pairs(self, producer_name: str = "default") -> List[str]:
+        """
+        Get the pairs cached from the producer
+
+        :returns: List of pairs
+        """
+        return self.__producer_pairs.get(producer_name, []).copy()
+
+    def _emit_df(
+        self,
+        pair_key: PairWithTimeframe,
+        dataframe: DataFrame
+    ) -> None:
+        """
+        Send this dataframe as an ANALYZED_DF message to RPC
+
+        :param pair_key: PairWithTimeframe tuple
+        :param data: Tuple containing the DataFrame and the datetime it was cached
+        """
+        if self.__rpc:
+            self.__rpc.send_msg(
+                {
+                    'type': RPCMessageType.ANALYZED_DF,
+                    'data': {
+                        'key': pair_key,
+                        'df': dataframe,
+                        'la': datetime.now(timezone.utc)
+                    }
+                }
+            )
+
+    def _add_external_df(
+        self,
+        pair: str,
+        dataframe: DataFrame,
+        last_analyzed: datetime,
+        timeframe: str,
+        candle_type: CandleType,
+        producer_name: str = "default"
+    ) -> None:
+        """
+        Add the pair data to this class from an external source.
+
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        pair_key = (pair, timeframe, candle_type)
+
+        if producer_name not in self.__producer_pairs_df:
+            self.__producer_pairs_df[producer_name] = {}
+
+        _last_analyzed = datetime.now(timezone.utc) if not last_analyzed else last_analyzed
+
+        self.__producer_pairs_df[producer_name][pair_key] = (dataframe, _last_analyzed)
+        logger.debug(f"External DataFrame for {pair_key} from {producer_name} added.")
+
+    def get_producer_df(
+        self,
+        pair: str,
+        timeframe: Optional[str] = None,
+        candle_type: Optional[CandleType] = None,
+        producer_name: str = "default"
+    ) -> Tuple[DataFrame, datetime]:
+        """
+        Get the pair data from producers.
+
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :returns: Tuple of the DataFrame and last analyzed timestamp
+        """
+        _timeframe = self._default_timeframe if not timeframe else timeframe
+        _candle_type = self._default_candle_type if not candle_type else candle_type
+
+        pair_key = (pair, _timeframe, _candle_type)
+
+        # If we have no data from this Producer yet
+        if producer_name not in self.__producer_pairs_df:
+            # We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
+            return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+
+        # If we do have data from that Producer, but no data on this pair_key
+        if pair_key not in self.__producer_pairs_df[producer_name]:
+            # We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
+            return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+
+        # We have it, return this data
+        df, la = self.__producer_pairs_df[producer_name][pair_key]
+        return (df.copy(), la)
+
     def add_pairlisthandler(self, pairlists) -> None:
         """
         Allow adding pairlisthandler after initialization
@@ -90,8 +208,10 @@ class DataProvider:
         if saved_pair not in self.__cached_pairs_backtesting:
             timerange = TimeRange.parse_timerange(None if self._config.get(
                 'timerange') is None else str(self._config.get('timerange')))
-            # Move informative start time respecting startup_candle_count
+
-            startup_candles = self.get_required_startup(str(timeframe))
+            # It is not necessary to add the training candles, as they
+            # were already added at the beginning of the backtest.
+            startup_candles = self.get_required_startup(str(timeframe), False)
             tf_seconds = timeframe_to_seconds(str(timeframe))
             timerange.subtract_start(tf_seconds * startup_candles)
             self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
@@ -105,7 +225,7 @@ class DataProvider:
             )
         return self.__cached_pairs_backtesting[saved_pair].copy()
 
-    def get_required_startup(self, timeframe: str) -> int:
+    def get_required_startup(self, timeframe: str, add_train_candles: bool = True) -> int:
         freqai_config = self._config.get('freqai', {})
         if not freqai_config.get('enabled', False):
             return self._config.get('startup_candle_count', 0)
@@ -115,7 +235,9 @@ class DataProvider:
             # make sure the startupcandles is at least the set maximum indicator periods
             self._config['startup_candle_count'] = max(startup_candles, max(indicator_periods))
             tf_seconds = timeframe_to_seconds(timeframe)
-            train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
+            train_candles = 0
+            if add_train_candles:
+                train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
             total_candles = int(self._config['startup_candle_count'] + train_candles)
             logger.info(f'Increasing startup_candle_count for freqai to {total_candles}')
             return total_candles

freqtrade/data/history/featherdatahandler.py

@@ -0,0 +1,130 @@
+import logging
+from typing import Optional
+
+from pandas import DataFrame, read_feather, to_datetime
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
+from freqtrade.enums import CandleType
+
+from .idatahandler import IDataHandler
+
+
+logger = logging.getLogger(__name__)
+
+
+class FeatherDataHandler(IDataHandler):
+
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    def ohlcv_store(
+            self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :param timeframe: Timeframe - used to generate filename
+        :param data: Dataframe containing OHLCV data
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        self.create_dir_if_needed(filename)
+
+        data.reset_index(drop=True).loc[:, self._columns].to_feather(
+            filename, compression_level=9, compression='lz4')
+
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange], candle_type: CandleType
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(
+            self._datadir, pair, timeframe, candle_type=candle_type)
+        if not filename.exists():
+            # Fallback mode for 1M files
+            filename = self._pair_data_filename(
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
+            if not filename.exists():
+                return DataFrame(columns=self._columns)
+
+        pairdata = read_feather(filename)
+        pairdata.columns = self._columns
+        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
+        return pairdata
+
+    def ohlcv_append(
+        self,
+        pair: str,
+        timeframe: str,
+        data: DataFrame,
+        candle_type: CandleType
+    ) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        raise NotImplementedError()
+
+    def trades_store(self, pair: str, data: TradeList) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        # filename = self._pair_trades_filename(self._datadir, pair)
+
+        raise NotImplementedError()
+        # array = pa.array(data)
+        # array
+        # feather.write_feather(data, filename)
+
+    def trades_append(self, pair: str, data: TradeList):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        raise NotImplementedError()
+
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        raise NotImplementedError()
+        # filename = self._pair_trades_filename(self._datadir, pair)
+        # tradesdata = misc.file_load_json(filename)
+
+        # if not tradesdata:
+        #     return []
+
+        # return tradesdata
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "feather"

freqtrade/data/history/hdf5datahandler.py

@@ -1,7 +1,5 @@
 import logging
-import re
+from typing import Optional
-from pathlib import Path
-from typing import List, Optional
 
 import numpy as np
 import pandas as pd
@@ -20,26 +18,6 @@ class HDF5DataHandler(IDataHandler):
 
     _columns = DEFAULT_DATAFRAME_COLUMNS
 
-    @classmethod
-    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
-        """
-        Returns a list of all pairs with ohlcv data available in this datadir
-        for the specified timeframe
-        :param datadir: Directory to search for ohlcv files
-        :param timeframe: Timeframe to search pairs for
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
-        :return: List of Pairs
-        """
-        candle = ""
-        if candle_type != CandleType.SPOT:
-            datadir = datadir.joinpath('futures')
-            candle = f"-{candle_type}"
-
-        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.h5)', p.name)
-                for p in datadir.glob(f"*{timeframe}{candle}.h5")]
-        # Check if regex found something and only return these results
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
-
     def ohlcv_store(
             self, pair: str, timeframe: str, data: pd.DataFrame, candle_type: CandleType) -> None:
         """
@@ -103,6 +81,7 @@ class HDF5DataHandler(IDataHandler):
             raise ValueError("Wrong dataframe format")
         pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
                                           'low': 'float', 'close': 'float', 'volume': 'float'})
+        pairdata = pairdata.reset_index(drop=True)
         return pairdata
 
     def ohlcv_append(
@@ -121,18 +100,6 @@ class HDF5DataHandler(IDataHandler):
         """
         raise NotImplementedError()
 
-    @classmethod
-    def trades_get_pairs(cls, datadir: Path) -> List[str]:
-        """
-        Returns a list of all pairs for which trade data is available in this
-        :param datadir: Directory to search for ohlcv files
-        :return: List of Pairs
-        """
-        _tmp = [re.search(r'^(\S+)(?=\-trades.h5)', p.name)
-                for p in datadir.glob("*trades.h5")]
-        # Check if regex found something and only return these results to avoid exceptions.
-        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
-
     def trades_store(self, pair: str, data: TradeList) -> None:
         """
         Store trades data (list of Dicts) to file

freqtrade/data/history/idatahandler.py

@@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
 
 class IDataHandler(ABC):
 
-    _OHLCV_REGEX = r'^([a-zA-Z_-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
+    _OHLCV_REGEX = r'^([a-zA-Z_\d-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
 
     def __init__(self, datadir: Path) -> None:
         self._datadir = datadir
@@ -61,7 +61,6 @@ class IDataHandler(ABC):
             ) for match in _tmp if match and len(match.groups()) > 1]
 
     @classmethod
-    @abstractmethod
     def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
         """
         Returns a list of all pairs with ohlcv data available in this datadir
@@ -71,6 +70,15 @@ class IDataHandler(ABC):
         :param candle_type: Any of the enum CandleType (must match trading mode!)
         :return: List of Pairs
         """
+        candle = ""
+        if candle_type != CandleType.SPOT:
+            datadir = datadir.joinpath('futures')
+            candle = f"-{candle_type}"
+        ext = cls._get_file_extension()
+        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + f'.{ext})', p.name)
+                for p in datadir.glob(f"*{timeframe}{candle}.{ext}")]
+        # Check if regex found something and only return these results
+        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
 
     @abstractmethod
     def ohlcv_store(
@@ -144,13 +152,17 @@ class IDataHandler(ABC):
         """
 
     @classmethod
-    @abstractmethod
     def trades_get_pairs(cls, datadir: Path) -> List[str]:
         """
         Returns a list of all pairs for which trade data is available in this
         :param datadir: Directory to search for ohlcv files
         :return: List of Pairs
         """
+        _ext = cls._get_file_extension()
+        _tmp = [re.search(r'^(\S+)(?=\-trades.' + _ext + ')', p.name)
+                for p in datadir.glob(f"*trades.{_ext}")]
+        # Check if regex found something and only return these results to avoid exceptions.
+        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
 
     @abstractmethod
     def trades_store(self, pair: str, data: TradeList) -> None:
@@ -255,7 +267,7 @@ class IDataHandler(ABC):
         Rebuild pair name from filename
         Assumes a asset name of max. 7 length to also support BTC-PERP and BTC-PERP:USD names.
         """
-        res = re.sub(r'^(([A-Za-z]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
+        res = re.sub(r'^(([A-Za-z\d]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
         res = re.sub('_', ':', res, 1)
         return res
 
@@ -363,6 +375,12 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
     elif datatype == 'hdf5':
         from .hdf5datahandler import HDF5DataHandler
         return HDF5DataHandler
+    elif datatype == 'feather':
+        from .featherdatahandler import FeatherDataHandler
+        return FeatherDataHandler
+    elif datatype == 'parquet':
+        from .parquetdatahandler import ParquetDataHandler
+        return ParquetDataHandler
     else:
         raise ValueError(f"No datahandler for datatype {datatype} available.")
 

freqtrade/data/history/jsondatahandler.py

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

freqtrade/data/history/parquetdatahandler.py

@@ -0,0 +1,129 @@
+import logging
+from typing import Optional
+
+from pandas import DataFrame, read_parquet, to_datetime
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
+from freqtrade.enums import CandleType
+
+from .idatahandler import IDataHandler
+
+
+logger = logging.getLogger(__name__)
+
+
+class ParquetDataHandler(IDataHandler):
+
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    def ohlcv_store(
+            self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :param timeframe: Timeframe - used to generate filename
+        :param data: Dataframe containing OHLCV data
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        self.create_dir_if_needed(filename)
+
+        data.reset_index(drop=True).loc[:, self._columns].to_parquet(filename)
+
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange], candle_type: CandleType
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(
+            self._datadir, pair, timeframe, candle_type=candle_type)
+        if not filename.exists():
+            # Fallback mode for 1M files
+            filename = self._pair_data_filename(
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
+            if not filename.exists():
+                return DataFrame(columns=self._columns)
+
+        pairdata = read_parquet(filename)
+        pairdata.columns = self._columns
+        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
+        return pairdata
+
+    def ohlcv_append(
+        self,
+        pair: str,
+        timeframe: str,
+        data: DataFrame,
+        candle_type: CandleType
+    ) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        raise NotImplementedError()
+
+    def trades_store(self, pair: str, data: TradeList) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        # filename = self._pair_trades_filename(self._datadir, pair)
+
+        raise NotImplementedError()
+        # array = pa.array(data)
+        # array
+        # feather.write_feather(data, filename)
+
+    def trades_append(self, pair: str, data: TradeList):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        raise NotImplementedError()
+
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        raise NotImplementedError()
+        # filename = self._pair_trades_filename(self._datadir, pair)
+        # tradesdata = misc.file_load_json(filename)
+
+        # if not tradesdata:
+        #     return []
+
+        # return tradesdata
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "parquet"

freqtrade/edge/edge_positioning.py

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

freqtrade/enums/__init__.py

@@ -6,7 +6,7 @@ from freqtrade.enums.exittype import ExitType
 from freqtrade.enums.hyperoptstate import HyperoptState
 from freqtrade.enums.marginmode import MarginMode
 from freqtrade.enums.ordertypevalue import OrderTypeValues
-from freqtrade.enums.rpcmessagetype import RPCMessageType
+from freqtrade.enums.rpcmessagetype import RPCMessageType, RPCRequestType
 from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
 from freqtrade.enums.signaltype import SignalDirection, SignalTagType, SignalType
 from freqtrade.enums.state import State

freqtrade/enums/rpcmessagetype.py

@@ -1,7 +1,7 @@
 from enum import Enum
 
 
-class RPCMessageType(Enum):
+class RPCMessageType(str, Enum):
     STATUS = 'status'
     WARNING = 'warning'
     STARTUP = 'startup'
@@ -19,8 +19,19 @@ class RPCMessageType(Enum):
 
     STRATEGY_MSG = 'strategy_msg'
 
+    WHITELIST = 'whitelist'
+    ANALYZED_DF = 'analyzed_df'
+
     def __repr__(self):
         return self.value
 
     def __str__(self):
         return self.value
+
+
+# Enum for parsing requests from ws consumers
+class RPCRequestType(str, Enum):
+    SUBSCRIBE = 'subscribe'
+
+    WHITELIST = 'whitelist'
+    ANALYZED_DF = 'analyzed_df'

freqtrade/exchange/binance.py

@@ -1,5 +1,4 @@
 """ Binance exchange subclass """
-import json
 import logging
 from datetime import datetime
 from pathlib import Path
@@ -12,7 +11,7 @@ from freqtrade.enums import CandleType, MarginMode, TradingMode
 from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.common import retrier
-from freqtrade.misc import deep_merge_dicts
+from freqtrade.misc import deep_merge_dicts, json_load
 
 
 logger = logging.getLogger(__name__)
@@ -200,7 +199,7 @@ class Binance(Exchange):
                     Path(__file__).parent / 'binance_leverage_tiers.json'
                 )
                 with open(leverage_tiers_path) as json_file:
-                    return json.load(json_file)
+                    return json_load(json_file)
             else:
                 try:
                     return self._api.fetch_leverage_tiers()

freqtrade/exchange/binance_leverage_tiers.json

@@ -19209,4 +19209,4 @@
       }
     }
   ]
-}
+}

freqtrade/exchange/exchange.py

@@ -21,7 +21,8 @@ from dateutil import parser
 from pandas import DataFrame
 
 from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
-                                 EntryExit, ListPairsWithTimeframes, MakerTaker, PairWithTimeframe)
+                                 Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
+                                 PairWithTimeframe)
 from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
 from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
@@ -91,7 +92,7 @@ class Exchange:
         # TradingMode.SPOT always supported and not required in this list
     ]
 
-    def __init__(self, config: Dict[str, Any], validate: bool = True,
+    def __init__(self, config: Config, validate: bool = True,
                  load_leverage_tiers: bool = False) -> None:
         """
         Initializes this module with the given config,
@@ -108,7 +109,7 @@ class Exchange:
         self._loop_lock = Lock()
         self.loop = asyncio.new_event_loop()
         asyncio.set_event_loop(self.loop)
-        self._config: Dict = {}
+        self._config: Config = {}
 
         self._config.update(config)
 
@@ -2890,7 +2891,7 @@ def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float:
     :return: num-contracts
     """
     if contract_size and contract_size != 1:
-        return amount / contract_size
+        return float(FtPrecise(amount) / FtPrecise(contract_size))
     else:
         return amount
 
@@ -2904,7 +2905,7 @@ def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) ->
     """
 
     if contract_size and contract_size != 1:
-        return num_contracts * contract_size
+        return float(FtPrecise(num_contracts) * FtPrecise(contract_size))
     else:
         return num_contracts
 

freqtrade/exchange/okx.py

@@ -71,6 +71,7 @@ class Okx(Exchange):
         try:
             if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
                 accounts = self._api.fetch_accounts()
+                self._log_exchange_response('fetch_accounts', accounts)
                 if len(accounts) > 0:
                     self.net_only = accounts[0].get('info', {}).get('posMode') == 'net_mode'
         except ccxt.DDoSProtection as e:

freqtrade/freqai/base_models/BaseClassifierModel.py

@@ -1,4 +1,5 @@
 import logging
+from time import time
 from typing import Any, Tuple
 
 import numpy as np
@@ -32,7 +33,9 @@ class BaseClassifierModel(IFreqaiModel):
         :model: Trained model which can be used to inference (self.predict)
         """
 
-        logger.info("-------------------- Starting training " f"{pair} --------------------")
+        logger.info(f"-------------------- Starting training {pair} --------------------")
+
+        start_time = time()
 
         # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(
@@ -45,10 +48,10 @@ class BaseClassifierModel(IFreqaiModel):
         start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
         end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
         logger.info(f"-------------------- Training on data from {start_date} to "
-                    f"{end_date}--------------------")
+                    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", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)
@@ -57,13 +60,16 @@ class BaseClassifierModel(IFreqaiModel):
         self.data_cleaning_train(dk)
 
         logger.info(
-            f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
+            f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
         )
-        logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
+        logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
 
         model = self.fit(data_dictionary, dk)
 
-        logger.info(f"--------------------done training {pair}--------------------")
+        end_time = time()
+
+        logger.info(f"-------------------- Done training {pair} "
+                    f"({end_time - start_time:.2f} secs) --------------------")
 
         return model
 

freqtrade/freqai/base_models/BaseRegressionModel.py

@@ -1,4 +1,5 @@
 import logging
+from time import time
 from typing import Any, Tuple
 
 import numpy as np
@@ -31,7 +32,9 @@ class BaseRegressionModel(IFreqaiModel):
         :model: Trained model which can be used to inference (self.predict)
         """
 
-        logger.info("-------------------- Starting training " f"{pair} --------------------")
+        logger.info(f"-------------------- Starting training {pair} --------------------")
+
+        start_time = time()
 
         # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(
@@ -44,10 +47,10 @@ class BaseRegressionModel(IFreqaiModel):
         start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
         end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
         logger.info(f"-------------------- Training on data from {start_date} to "
-                    f"{end_date}--------------------")
+                    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", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)
@@ -56,13 +59,16 @@ class BaseRegressionModel(IFreqaiModel):
         self.data_cleaning_train(dk)
 
         logger.info(
-            f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
+            f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
         )
-        logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
+        logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
 
         model = self.fit(data_dictionary, dk)
 
-        logger.info(f"--------------------done training {pair}--------------------")
+        end_time = time()
+
+        logger.info(f"-------------------- Done training {pair} "
+                    f"({end_time - start_time:.2f} secs) --------------------")
 
         return model
 

freqtrade/freqai/base_models/BaseTensorFlowModel.py

@@ -1,4 +1,5 @@
 import logging
+from time import time
 from typing import Any
 
 from pandas import DataFrame
@@ -28,7 +29,9 @@ class BaseTensorFlowModel(IFreqaiModel):
         :model: Trained model which can be used to inference (self.predict)
         """
 
-        logger.info("-------------------- Starting training " f"{pair} --------------------")
+        logger.info(f"-------------------- Starting training {pair} --------------------")
+
+        start_time = time()
 
         # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(
@@ -41,10 +44,10 @@ class BaseTensorFlowModel(IFreqaiModel):
         start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
         end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
         logger.info(f"-------------------- Training on data from {start_date} to "
-                    f"{end_date}--------------------")
+                    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", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)
@@ -53,12 +56,15 @@ class BaseTensorFlowModel(IFreqaiModel):
         self.data_cleaning_train(dk)
 
         logger.info(
-            f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
+            f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
         )
-        logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
+        logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
 
         model = self.fit(data_dictionary, dk)
 
-        logger.info(f"--------------------done training {pair}--------------------")
+        end_time = time()
+
+        logger.info(f"-------------------- Done training {pair} "
+                    f"({end_time - start_time:.2f} secs) --------------------")
 
         return model

freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py

@@ -1,4 +1,3 @@
-
 from joblib import Parallel
 from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
 from sklearn.utils.fixes import delayed

freqtrade/freqai/data_drawer.py

@@ -16,6 +16,7 @@ from numpy.typing import NDArray
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
 from freqtrade.data.history import load_pair_history
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@@ -27,9 +28,7 @@ logger = logging.getLogger(__name__)
 
 class pair_info(TypedDict):
     model_filename: str
-    first: bool
     trained_timestamp: int
-    priority: int
     data_path: str
     extras: dict
 
@@ -58,7 +57,7 @@ class FreqaiDataDrawer:
     Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
     """
 
-    def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
+    def __init__(self, full_path: Path, config: Config, follow_mode: bool = False):
 
         self.config = config
         self.freqai_info = config.get("freqai", {})
@@ -91,7 +90,7 @@ class FreqaiDataDrawer:
         self.old_DBSCAN_eps: Dict[str, float] = {}
         self.empty_pair_dict: pair_info = {
                 "model_filename": "", "trained_timestamp": 0,
-                "priority": 1, "first": True, "data_path": "", "extras": {}}
+                "data_path": "", "extras": {}}
 
     def load_drawer_from_disk(self):
         """
@@ -216,7 +215,6 @@ class FreqaiDataDrawer:
             self.pair_dict[pair] = self.empty_pair_dict.copy()
             model_filename = ""
             trained_timestamp = 0
-            self.pair_dict[pair]["priority"] = len(self.pair_dict)
 
         if not data_path_set and self.follow_mode:
             logger.warning(
@@ -236,18 +234,9 @@ class FreqaiDataDrawer:
             return
         else:
             self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
-            self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
 
             return
 
-    def pair_to_end_of_training_queue(self, pair: str) -> None:
-        # march all pairs up in the queue
-        with self.pair_dict_lock:
-            for p in self.pair_dict:
-                self.pair_dict[p]["priority"] -= 1
-            # send pair to end of queue
-            self.pair_dict[pair]["priority"] = len(self.pair_dict)
-
     def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
         """
         Set the initial return values to the historical predictions dataframe. This avoids needing
@@ -441,6 +430,16 @@ class FreqaiDataDrawer:
 
         return
 
+    def load_metadata(self, dk: FreqaiDataKitchen) -> None:
+        """
+        Load only metadata into datakitchen to increase performance during
+        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.training_features_list = dk.data["training_features_list"]
+            dk.label_list = dk.data["label_list"]
+
     def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
         """
         loads all data required to make a prediction on a sub-train time range

freqtrade/freqai/data_kitchen.py

@@ -18,6 +18,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.neighbors import NearestNeighbors
 
 from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.strategy.interface import IStrategy
@@ -57,7 +58,7 @@ class FreqaiDataKitchen:
 
     def __init__(
         self,
-        config: Dict[str, Any],
+        config: Config,
         live: bool = False,
         pair: str = "",
     ):
@@ -466,27 +467,6 @@ class FreqaiDataKitchen:
 
         return df
 
-    def remove_training_from_backtesting(
-        self
-    ) -> DataFrame:
-        """
-        Function which takes the backtesting time range and
-        remove training data from dataframe, keeping only the
-        startup_candle_count candles
-        """
-        startup_candle_count = self.config.get('startup_candle_count', 0)
-        tf = self.config['timeframe']
-        tr = self.config["timerange"]
-
-        backtesting_timerange = TimeRange.parse_timerange(tr)
-        if startup_candle_count > 0 and backtesting_timerange:
-            backtesting_timerange.subtract_start(timeframe_to_seconds(tf) * startup_candle_count)
-
-        start = datetime.fromtimestamp(backtesting_timerange.startts, tz=timezone.utc)
-        df = self.return_dataframe
-        df = df.loc[df["date"] >= start, :]
-        return df
-
     def principal_component_analysis(self) -> None:
         """
         Performs Principal Component Analysis on the data for dimensionality reduction
@@ -775,12 +755,22 @@ class FreqaiDataKitchen:
 
     def compute_inlier_metric(self, set_='train') -> None:
         """
-
         Compute inlier metric from backwards distance distributions.
         This metric defines how well features from a timepoint fit
         into previous timepoints.
         """
 
+        def normalise(dataframe: DataFrame, key: str) -> DataFrame:
+            if set_ == 'train':
+                min_value = dataframe.min()
+                max_value = dataframe.max()
+                self.data[f'{key}_min'] = min_value
+                self.data[f'{key}_max'] = max_value
+            else:
+                min_value = self.data[f'{key}_min']
+                max_value = self.data[f'{key}_max']
+            return (dataframe - min_value) / (max_value - min_value)
+
         no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"]
 
         if set_ == 'train':
@@ -825,7 +815,12 @@ class FreqaiDataKitchen:
         inliers = pd.DataFrame(index=distances.index)
         for key in distances.keys():
             current_distances = distances[key].dropna()
-            fit_params = stats.weibull_min.fit(current_distances)
+            current_distances = normalise(current_distances, key)
+            if set_ == 'train':
+                fit_params = stats.weibull_min.fit(current_distances)
+                self.data[f'{key}_fit_params'] = fit_params
+            else:
+                fit_params = self.data[f'{key}_fit_params']
             quantiles = stats.weibull_min.cdf(current_distances, *fit_params)
 
             df_inlier = pd.DataFrame(
@@ -979,8 +974,6 @@ class FreqaiDataKitchen:
 
         to_keep = [col for col in dataframe.columns if not col.startswith("&")]
         self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
-
-        # self.return_dataframe = self.remove_training_from_backtesting()
         self.full_df = DataFrame()
 
         return

freqtrade/freqai/freqai_interface.py

@@ -3,6 +3,7 @@ import shutil
 import threading
 import time
 from abc import ABC, abstractmethod
+from collections import deque
 from datetime import datetime, timezone
 from pathlib import Path
 from threading import Lock
@@ -14,12 +15,13 @@ from numpy.typing import NDArray
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DATETIME_PRINT_FORMAT
+from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.freqai.data_drawer import FreqaiDataDrawer
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.utils import plot_feature_importance
 from freqtrade.strategy.interface import IStrategy
 
 
@@ -50,7 +52,7 @@ class IFreqaiModel(ABC):
     Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
     """
 
-    def __init__(self, config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config) -> None:
 
         self.config = config
         self.assert_config(self.config)
@@ -63,7 +65,7 @@ class IFreqaiModel(ABC):
         self.first = True
         self.set_full_path()
         self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
-        self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", False)
+        self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
         if self.save_backtest_models:
             logger.info('Backtesting module configured to save all models.')
         self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
@@ -80,6 +82,7 @@ class IFreqaiModel(ABC):
         self.pair_it = 0
         self.pair_it_train = 0
         self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
+        self.train_queue = self._set_train_queue()
         self.last_trade_database_summary: DataFrame = {}
         self.current_trade_database_summary: DataFrame = {}
         self.analysis_lock = Lock()
@@ -99,7 +102,7 @@ class IFreqaiModel(ABC):
         """
         return ({})
 
-    def assert_config(self, config: Dict[str, Any]) -> None:
+    def assert_config(self, config: Config) -> None:
 
         if not config.get("freqai", {}):
             raise OperationalException("No freqai parameters found in configuration file.")
@@ -181,29 +184,40 @@ class IFreqaiModel(ABC):
         """
         while not self._stop_event.is_set():
             time.sleep(1)
-            for pair in self.config.get("exchange", {}).get("pair_whitelist"):
+            pair = self.train_queue[0]
 
-                (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
+            # ensure pair is avaialble in dp
+            if pair not in strategy.dp.current_whitelist():
+                self.train_queue.popleft()
+                logger.warning(f'{pair} not in current whitelist, removing from train queue.')
+                continue
 
-                if self.dd.pair_dict[pair]["priority"] != 1:
+            (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
-                    continue
-                dk = FreqaiDataKitchen(self.config, self.live, pair)
-                dk.set_paths(pair, trained_timestamp)
-                (
-                    retrain,
-                    new_trained_timerange,
-                    data_load_timerange,
-                ) = dk.check_if_new_training_required(trained_timestamp)
-                dk.set_paths(pair, new_trained_timerange.stopts)
 
-                if retrain:
+            dk = FreqaiDataKitchen(self.config, self.live, pair)
-                    self.train_timer('start')
+            dk.set_paths(pair, trained_timestamp)
+            (
+                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')
+                try:
                     self.extract_data_and_train_model(
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
                     )
-                    self.train_timer('stop')
+                except Exception as msg:
+                    logger.warning(f'Training {pair} raised exception {msg}, skipping.')
 
-            self.dd.save_historic_predictions_to_disk()
+                self.train_timer('stop')
+
+                # only rotate the queue after the first has been trained.
+                self.train_queue.rotate(-1)
+
+                self.dd.save_historic_predictions_to_disk()
 
     def start_backtesting(
         self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
@@ -230,7 +244,8 @@ class IFreqaiModel(ABC):
         # following tr_train. Both of these windows slide through the
         # entire backtest
         for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
-            (_, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
+            pair = metadata["pair"]
+            (_, _, _) = self.dd.get_pair_dict_info(pair)
             train_it += 1
             total_trains = len(dk.backtesting_timeranges)
             self.training_timerange = tr_train
@@ -245,37 +260,37 @@ class IFreqaiModel(ABC):
                                                 tr_train.stopts,
                                                 tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
             logger.info(
-                f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
+                f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
                 f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
                 "trains"
             )
 
             trained_timestamp_int = int(trained_timestamp.stopts)
             dk.data_path = Path(
-                dk.full_path
+                dk.full_path / f"sub-train-{pair.split('/')[0]}_{trained_timestamp_int}"
-                /
-                f"sub-train-{metadata['pair'].split('/')[0]}_{trained_timestamp_int}"
                 )
 
-            dk.set_new_model_names(metadata["pair"], trained_timestamp)
+            dk.set_new_model_names(pair, trained_timestamp)
 
             if dk.check_if_backtest_prediction_exists():
+                self.dd.load_metadata(dk)
+                self.check_if_feature_list_matches_strategy(dataframe_train, dk)
                 append_df = dk.get_backtesting_prediction()
                 dk.append_predictions(append_df)
             else:
                 if not self.model_exists(
-                    metadata["pair"], dk, trained_timestamp=trained_timestamp_int
+                    pair, dk, trained_timestamp=trained_timestamp_int
                 ):
                     dk.find_features(dataframe_train)
-                    self.model = self.train(dataframe_train, metadata["pair"], dk)
+                    self.model = self.train(dataframe_train, pair, dk)
-                    self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
+                    self.dd.pair_dict[pair]["trained_timestamp"] = int(
                         trained_timestamp.stopts)
 
                     if self.save_backtest_models:
                         logger.info('Saving backtest model to disk.')
-                        self.dd.save_data(self.model, metadata["pair"], dk)
+                        self.dd.save_data(self.model, pair, dk)
                 else:
-                    self.model = self.dd.load_data(metadata["pair"], dk)
+                    self.model = self.dd.load_data(pair, dk)
 
                 self.check_if_feature_list_matches_strategy(dataframe_train, dk)
 
@@ -416,14 +431,16 @@ class IFreqaiModel(ABC):
         if "training_features_list_raw" in dk.data:
             feature_list = dk.data["training_features_list_raw"]
         else:
-            feature_list = dk.training_features_list
+            feature_list = dk.data['training_features_list']
         if dk.training_features_list != feature_list:
             raise OperationalException(
                 "Trying to access pretrained model with `identifier` "
                 "but found different features furnished by current strategy."
                 "Change `identifier` to train from scratch, or ensure the"
                 "strategy is furnishing the same features as the pretrained"
-                "model"
+                "model. In case of --strategy-list, please be aware that FreqAI "
+                "requires all strategies to maintain identical "
+                "populate_any_indicator() functions"
             )
 
     def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
@@ -557,11 +574,11 @@ class IFreqaiModel(ABC):
 
         self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
         dk.set_new_model_names(pair, new_trained_timerange)
-        self.dd.pair_dict[pair]["first"] = False
-        if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
-            self.dd.pair_to_end_of_training_queue(pair)
         self.dd.save_data(model, pair, dk)
 
+        if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
+            plot_feature_importance(model, pair, dk)
+
         if self.freqai_info.get("purge_old_models", False):
             self.dd.purge_old_models()
 
@@ -685,6 +702,32 @@ class IFreqaiModel(ABC):
 
         return init_model
 
+    def _set_train_queue(self):
+        """
+        Sets train queue from existing train timestamps if they exist
+        otherwise it sets the train queue based on the provided whitelist.
+        """
+        current_pairlist = self.config.get("exchange", {}).get("pair_whitelist")
+        if not self.dd.pair_dict:
+            logger.info('Set fresh train queue from whitelist. '
+                        f'Queue: {current_pairlist}')
+            return deque(current_pairlist)
+
+        best_queue = deque()
+
+        pair_dict_sorted = sorted(self.dd.pair_dict.items(),
+                                  key=lambda k: k[1]['trained_timestamp'])
+        for pair in pair_dict_sorted:
+            if pair[0] in current_pairlist:
+                best_queue.append(pair[0])
+        for pair in current_pairlist:
+            if pair not in best_queue:
+                best_queue.appendleft(pair)
+
+        logger.info('Set existing queue from trained timestamps. '
+                    f'Best approximation queue: {best_queue}')
+        return best_queue
+
     def spice_rack(self, indicator: str, dataframe: DataFrame,
                    metadata: dict, strategy: IStrategy) -> NDArray:
         if not self.spice_rack_open:

freqtrade/freqai/utils.py

@@ -10,11 +10,13 @@ from scipy.signal import argrelextrema
 from technical import qtpylib
 
 from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange, timeframe_to_seconds
 from freqtrade.exchange.exchange import market_is_active
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
 from freqtrade.strategy import merge_informative_pair
 
@@ -22,7 +24,7 @@ from freqtrade.strategy import merge_informative_pair
 logger = logging.getLogger(__name__)
 
 
-def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
+def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
     """
     Called only once upon start of bot to download the necessary data for
     populating indicators and training the model.
@@ -56,9 +58,7 @@ def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
     )
 
 
-def get_required_data_timerange(
+def get_required_data_timerange(config: Config) -> TimeRange:
-    config: dict
-) -> TimeRange:
     """
     Used to compute the required data download time range
     for auto data-download in FreqAI
@@ -226,7 +226,7 @@ def setup_freqai_spice_rack(config: dict, exchange: Optional[Exchange]) -> Dict[
     return config
 
 # Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
-# def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
+# def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
 #     """
 #     Called only once upon start of bot to download the necessary data for
 #     populating indicators and training a FreqAI model.
@@ -272,3 +272,58 @@ def setup_freqai_spice_rack(config: dict, exchange: Optional[Exchange]) -> Dict[
 #             trading_mode=config.get("trading_mode", "spot"),
 #             prepend=config.get("prepend_data", False),
 #         )
+
+
+def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
+                            count_max: int = 25) -> None:
+    """
+        Plot Best and worst features by importance for a single sub-train.
+        :param model: Any = A model which was `fit` using a common library
+                            such as catboost or lightgbm
+        :param pair: str = pair e.g. BTC/USD
+        :param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
+        :param count_max: int = the amount of features to be loaded per column
+    """
+    from freqtrade.plot.plotting import go, make_subplots, store_plot_file
+
+    # Extract feature importance from model
+    models = {}
+    if 'FreqaiMultiOutputRegressor' in str(model.__class__):
+        for estimator, label in zip(model.estimators_, dk.label_list):
+            models[label] = estimator
+    else:
+        models[dk.label_list[0]] = model
+
+    for label in models:
+        mdl = models[label]
+        if "catboost.core" in str(mdl.__class__):
+            feature_importance = mdl.get_feature_importance()
+        elif "lightgbm.sklearn" or "xgb" in str(mdl.__class__):
+            feature_importance = mdl.feature_importances_
+        else:
+            logger.info('Model type not support for generating feature importances.')
+            return
+
+        # Data preparation
+        fi_df = pd.DataFrame({
+            "feature_names": np.array(dk.training_features_list),
+            "feature_importance": np.array(feature_importance)
+        })
+        fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
+        fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
+
+        # Plotting
+        def add_feature_trace(fig, fi_df, col):
+            return fig.add_trace(
+                go.Bar(
+                    x=fi_df["feature_importance"],
+                    y=fi_df["feature_names"],
+                    orientation='h', showlegend=False
+                ), row=1, col=col
+            )
+        fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
+        fig = add_feature_trace(fig, fi_df_top, 1)
+        fig = add_feature_trace(fig, fi_df_worst, 2)
+        fig.update_layout(title_text=f"Best and worst features by importance {pair}")
+        label = label.replace('&', '').replace('%', '')  # escape two FreqAI specific characters
+        store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)

freqtrade/freqtradebot.py

@@ -11,9 +11,9 @@ from typing import Any, Dict, List, Optional, Tuple
 
 from schedule import Scheduler
 
-from freqtrade import __version__, constants
+from freqtrade import constants
 from freqtrade.configuration import validate_config_consistency
-from freqtrade.constants import BuySell, LongShort
+from freqtrade.constants import BuySell, Config, LongShort
 from freqtrade.data.converter import order_book_to_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
@@ -29,6 +29,7 @@ from freqtrade.plugins.pairlistmanager import PairListManager
 from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.rpc import RPCManager
+from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from freqtrade.util import FtPrecise
@@ -44,7 +45,7 @@ class FreqtradeBot(LoggingMixin):
     This is from here the bot start its logic.
     """
 
-    def __init__(self, config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config) -> None:
         """
         Init all variables and objects the bot needs to work
         :param config: configuration dict, you can use Configuration.get_config()
@@ -52,8 +53,6 @@ class FreqtradeBot(LoggingMixin):
         """
         self.active_pair_whitelist: List[str] = []
 
-        logger.info('Starting freqtrade %s', __version__)
-
         # Init bot state
         self.state = State.STOPPED
 
@@ -74,6 +73,8 @@ class FreqtradeBot(LoggingMixin):
 
         PairLocks.timeframe = self.config['timeframe']
 
+        self.pairlists = PairListManager(self.exchange, self.config)
+
         # RPC runs in separate threads, can start handling external commands just after
         # initialization, even before Freqtradebot has a chance to start its throttling,
         # so anything in the Freqtradebot instance should be ready (initialized), including
@@ -81,9 +82,7 @@ class FreqtradeBot(LoggingMixin):
         # Keep this at the end of this initialization method.
         self.rpc: RPCManager = RPCManager(self)
 
-        self.pairlists = PairListManager(self.exchange, self.config)
+        self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists, self.rpc)
-
-        self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
 
         # Attach Dataprovider to strategy instance
         self.strategy.dp = self.dataprovider
@@ -94,6 +93,10 @@ class FreqtradeBot(LoggingMixin):
         self.edge = Edge(self.config, self.exchange, self.strategy) if \
             self.config.get('edge', {}).get('enabled', False) else None
 
+        # Init ExternalMessageConsumer if enabled
+        self.emc = ExternalMessageConsumer(self.config, self.dataprovider) if \
+            self.config.get('external_message_consumer', {}).get('enabled', False) else None
+
         self.active_pair_whitelist = self._refresh_active_whitelist()
 
         # Set initial bot state from config
@@ -153,9 +156,11 @@ class FreqtradeBot(LoggingMixin):
         finally:
             self.strategy.ft_bot_cleanup()
 
-            self.rpc.cleanup()
+        self.rpc.cleanup()
-            Trade.commit()
+        if self.emc:
-            self.exchange.close()
+            self.emc.shutdown()
+        Trade.commit()
+        self.exchange.close()
 
     def startup(self) -> None:
         """
@@ -256,6 +261,7 @@ class FreqtradeBot(LoggingMixin):
         pairs that have open trades.
         """
         # Refresh whitelist
+        _prev_whitelist = self.pairlists.whitelist
         self.pairlists.refresh_pairlist()
         _whitelist = self.pairlists.whitelist
 
@@ -268,6 +274,11 @@ class FreqtradeBot(LoggingMixin):
             # Extend active-pair whitelist with pairs of open trades
             # It ensures that candle (OHLCV) data are downloaded for open trades as well
             _whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
+
+        # Called last to include the included pairs
+        if _prev_whitelist != _whitelist:
+            self.rpc.send_msg({'type': RPCMessageType.WHITELIST, 'data': _whitelist})
+
         return _whitelist
 
     def get_free_open_trades(self) -> int:
@@ -596,7 +607,7 @@ class FreqtradeBot(LoggingMixin):
                 amount = trade.amount
 
             if amount == 0.0:
-                logger.info("Amount to sell is 0.0 due to exchange limits - not selling.")
+                logger.info("Amount to exit is 0.0 due to exchange limits - not exiting.")
                 return
 
             remaining = (trade.amount - amount) * current_exit_rate
@@ -923,7 +934,7 @@ class FreqtradeBot(LoggingMixin):
             'stake_amount': trade.stake_amount,
             'stake_currency': self.config['stake_currency'],
             'fiat_currency': self.config.get('fiat_display_currency', None),
-            'amount': order.safe_amount_after_fee if fill else order.amount,
+            'amount': order.safe_amount_after_fee if fill else (order.amount or trade.amount),
             'open_date': trade.open_date or datetime.utcnow(),
             'current_rate': current_rate,
             'sub_trade': sub_trade,
@@ -1599,14 +1610,14 @@ class FreqtradeBot(LoggingMixin):
         # second condition is for mypy only; order will always be passed during sub trade
         if sub_trade and order is not None:
             amount = order.safe_filled if fill else order.amount
-            profit_rate = order.safe_price
+            order_rate: float = order.safe_price
 
-            profit = trade.calc_profit(rate=profit_rate, amount=amount, open_rate=trade.open_rate)
+            profit = trade.calc_profit(rate=order_rate, amount=amount, open_rate=trade.open_rate)
-            profit_ratio = trade.calc_profit_ratio(profit_rate, amount, trade.open_rate)
+            profit_ratio = trade.calc_profit_ratio(order_rate, amount, trade.open_rate)
         else:
-            profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
+            order_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
-            profit = trade.calc_profit(rate=profit_rate) + (0.0 if fill else trade.realized_profit)
+            profit = trade.calc_profit(rate=order_rate) + (0.0 if fill else trade.realized_profit)
-            profit_ratio = trade.calc_profit_ratio(profit_rate)
+            profit_ratio = trade.calc_profit_ratio(order_rate)
             amount = trade.amount
         gain = "profit" if profit_ratio > 0 else "loss"
 
@@ -1619,11 +1630,12 @@ class FreqtradeBot(LoggingMixin):
             'leverage': trade.leverage,
             'direction': 'Short' if trade.is_short else 'Long',
             'gain': gain,
-            'limit': profit_rate,
+            'limit': order_rate,  # Deprecated
+            'order_rate': order_rate,
             'order_type': order_type,
             'amount': amount,
             'open_rate': trade.open_rate,
-            'close_rate': profit_rate,
+            'close_rate': order_rate,
             'current_rate': current_rate,
             'profit_amount': profit,
             'profit_ratio': profit_ratio,

freqtrade/loggers.py

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

freqtrade/main.py

@@ -12,6 +12,7 @@ from typing import Any, List
 if sys.version_info < (3, 8):  # pragma: no cover
     sys.exit("Freqtrade requires Python version >= 3.8")
 
+from freqtrade import __version__
 from freqtrade.commands import Arguments
 from freqtrade.exceptions import FreqtradeException, OperationalException
 from freqtrade.loggers import setup_logging_pre
@@ -34,6 +35,7 @@ def main(sysargv: List[str] = None) -> None:
 
         # Call subcommand.
         if 'func' in args:
+            logger.info(f'freqtrade {__version__}')
             return_code = args['func'](args)
         else:
             # No subcommand was issued.

freqtrade/misc.py

@@ -10,9 +10,11 @@ from typing import Any, Iterator, List
 from typing.io import IO
 from urllib.parse import urlparse
 
+import pandas
 import rapidjson
 
 from freqtrade.constants import DECIMAL_PER_COIN_FALLBACK, DECIMALS_PER_COIN
+from freqtrade.enums import SignalTagType, SignalType
 
 
 logger = logging.getLogger(__name__)
@@ -249,3 +251,41 @@ def parse_db_uri_for_logging(uri: str):
         return uri
     pwd = parsed_db_uri.netloc.split(':')[1].split('@')[0]
     return parsed_db_uri.geturl().replace(f':{pwd}@', ':*****@')
+
+
+def dataframe_to_json(dataframe: pandas.DataFrame) -> str:
+    """
+    Serialize a DataFrame for transmission over the wire using JSON
+    :param dataframe: A pandas DataFrame
+    :returns: A JSON string of the pandas DataFrame
+    """
+    return dataframe.to_json(orient='split')
+
+
+def json_to_dataframe(data: str) -> pandas.DataFrame:
+    """
+    Deserialize JSON into a DataFrame
+    :param data: A JSON string
+    :returns: A pandas DataFrame from the JSON string
+    """
+    dataframe = pandas.read_json(data, orient='split')
+    if 'date' in dataframe.columns:
+        dataframe['date'] = pandas.to_datetime(dataframe['date'], unit='ms', utc=True)
+
+    return dataframe
+
+
+def remove_entry_exit_signals(dataframe: pandas.DataFrame):
+    """
+    Remove Entry and Exit signals from a DataFrame
+
+    :param dataframe: The DataFrame to remove signals from
+    """
+    dataframe[SignalType.ENTER_LONG.value] = 0
+    dataframe[SignalType.EXIT_LONG.value] = 0
+    dataframe[SignalType.ENTER_SHORT.value] = 0
+    dataframe[SignalType.EXIT_SHORT.value] = 0
+    dataframe[SignalTagType.ENTER_TAG.value] = None
+    dataframe[SignalTagType.EXIT_TAG.value] = None
+
+    return dataframe

freqtrade/optimize/backtesting.py

@@ -15,7 +15,7 @@ from pandas import DataFrame
 
 from freqtrade import constants
 from freqtrade.configuration import TimeRange, validate_config_consistency
-from freqtrade.constants import DATETIME_PRINT_FORMAT, LongShort
+from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, LongShort
 from freqtrade.data import history
 from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
@@ -70,7 +70,7 @@ class Backtesting:
     backtesting.start()
     """
 
-    def __init__(self, config: Dict[str, Any]) -> None:
+    def __init__(self, config: Config) -> None:
 
         LoggingMixin.show_output = False
         self.config = config
@@ -95,8 +95,8 @@ class Backtesting:
 
         if self.config.get('strategy_list'):
             if self.config.get('freqai', {}).get('enabled', False):
-                raise OperationalException(
+                logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies "
-                    "You can't use strategy_list and freqai at the same time.")
+                               "to have identical populate_any_indicators.")
             for strat in list(self.config['strategy_list']):
                 stratconf = deepcopy(self.config)
                 stratconf['strategy'] = strat
@@ -143,9 +143,14 @@ class Backtesting:
 
         # Get maximum required startup period
         self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
+        self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
+
+        if self.config.get('freqai', {}).get('enabled', False):
+            # For FreqAI, increase the required_startup to includes the training data
+            self.required_startup = self.dataprovider.get_required_startup(self.timeframe)
+
         # Add maximum startup candle count to configuration for informative pairs support
         self.config['startup_candle_count'] = self.required_startup
-        self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
 
         self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
         # strategies which define "can_short=True" will fail to load in Spot mode.
@@ -221,7 +226,7 @@ class Backtesting:
             pairs=self.pairlists.whitelist,
             timeframe=self.timeframe,
             timerange=self.timerange,
-            startup_candles=self.dataprovider.get_required_startup(self.timeframe),
+            startup_candles=self.config['startup_candle_count'],
             fail_without_data=True,
             data_format=self.config.get('dataformat_ohlcv', 'json'),
             candle_type=self.config.get('candle_type_def', CandleType.SPOT)

freqtrade/optimize/edge_cli.py

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

freqtrade/optimize/hyperopt.py

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

freqtrade/optimize/hyperopt_interface.py

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

freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py

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

freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py

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

freqtrade/optimize/hyperopt_loss_interface.py

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

freqtrade/optimize/hyperopt_tools.py

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

freqtrade/optimize/optimize_reports.py

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

freqtrade/plot/plotting.py

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

freqtrade/plugins/pairlist/AgeFilter.py

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

freqtrade/plugins/pairlist/IPairList.py

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

freqtrade/plugins/pairlist/OffsetFilter.py

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

freqtrade/plugins/pairlist/PerformanceFilter.py

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

freqtrade/plugins/pairlist/PrecisionFilter.py

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

freqtrade/plugins/pairlist/PriceFilter.py

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

freqtrade/plugins/pairlist/ShuffleFilter.py

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

freqtrade/plugins/pairlist/SpreadFilter.py

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

freqtrade/plugins/pairlist/StaticPairList.py

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

freqtrade/plugins/pairlist/VolatilityFilter.py

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

freqtrade/plugins/pairlist/VolumePairList.py

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

freqtrade/plugins/pairlist/pairlist_helpers.py

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

freqtrade/plugins/pairlist/rangestabilityfilter.py

@@ -9,7 +9,7 @@ import arrow
 from cachetools import TTLCache
 from pandas import DataFrame
 
-from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 class RangeStabilityFilter(IPairList):
 
     def __init__(self, exchange, pairlistmanager,
-                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 config: Config, pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 
@@ -100,23 +100,19 @@ class RangeStabilityFilter(IPairList):
         if cached_res is not None:
             return cached_res
 
-        result = False
+        result = True
         if daily_candles is not None and not daily_candles.empty:
             highest_high = daily_candles['high'].max()
             lowest_low = daily_candles['low'].min()
             pct_change = ((highest_high - lowest_low) / lowest_low) if lowest_low > 0 else 0
-            if pct_change >= self._min_rate_of_change:
+            if pct_change < self._min_rate_of_change:
-                result = True
-            else:
                 self.log_once(f"Removed {pair} from whitelist, because rate of change "
                               f"over {self._days} {plural(self._days, 'day')} is {pct_change:.3f}, "
                               f"which is below the threshold of {self._min_rate_of_change}.",
                               logger.info)
                 result = False
             if self._max_rate_of_change:
-                if pct_change <= self._max_rate_of_change:
+                if pct_change > self._max_rate_of_change:
-                    result = True
-                else:
                     self.log_once(
                         f"Removed {pair} from whitelist, because rate of change "
                         f"over {self._days} {plural(self._days, 'day')} is {pct_change:.3f}, "

freqtrade/plugins/pairlistmanager.py

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

freqtrade/plugins/protectionmanager.py

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

freqtrade/plugins/protections/iprotection.py

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

freqtrade/plugins/protections/low_profit_pairs.py

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

freqtrade/plugins/protections/max_drawdown_protection.py

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

freqtrade/plugins/protections/stoploss_guard.py

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

freqtrade/resolvers/exchange_resolver.py

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

freqtrade/resolvers/freqaimodel_resolver.py

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

freqtrade/resolvers/hyperopt_resolver.py

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

freqtrade/resolvers/iresolver.py

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

freqtrade/resolvers/pairlist_resolver.py

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

freqtrade/resolvers/protection_resolver.py

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

freqtrade/resolvers/strategy_resolver.py

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

freqtrade/rpc/api_server/api_auth.py

@@ -1,8 +1,10 @@
+import logging
 import secrets
 from datetime import datetime, timedelta
+from typing import Any, Dict, Union
 
 import jwt
-from fastapi import APIRouter, Depends, HTTPException, status
+from fastapi import APIRouter, Depends, HTTPException, Query, WebSocket, status
 from fastapi.security import OAuth2PasswordBearer
 from fastapi.security.http import HTTPBasic, HTTPBasicCredentials
 
@@ -10,6 +12,8 @@ from freqtrade.rpc.api_server.api_schemas import AccessAndRefreshToken, AccessTo
 from freqtrade.rpc.api_server.deps import get_api_config
 
 
+logger = logging.getLogger(__name__)
+
 ALGORITHM = "HS256"
 
 router_login = APIRouter()
@@ -25,7 +29,7 @@ httpbasic = HTTPBasic(auto_error=False)
 oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
 
 
-def get_user_from_token(token, secret_key: str, token_type: str = "access"):
+def get_user_from_token(token, secret_key: str, token_type: str = "access") -> str:
     credentials_exception = HTTPException(
         status_code=status.HTTP_401_UNAUTHORIZED,
         detail="Could not validate credentials",
@@ -44,6 +48,45 @@ def get_user_from_token(token, secret_key: str, token_type: str = "access"):
     return username
 
 
+# This should be reimplemented to better realign with the existing tools provided
+# by FastAPI regarding API Tokens
+# https://github.com/tiangolo/fastapi/blob/master/fastapi/security/api_key.py
+async def validate_ws_token(
+    ws: WebSocket,
+    ws_token: Union[str, None] = Query(default=None, alias="token"),
+    api_config: Dict[str, Any] = Depends(get_api_config)
+):
+    secret_ws_token = api_config.get('ws_token', None)
+    secret_jwt_key = api_config.get('jwt_secret_key', 'super-secret')
+
+    # Check if ws_token is/in secret_ws_token
+    if ws_token and secret_ws_token:
+        is_valid_ws_token = False
+        if isinstance(secret_ws_token, str):
+            is_valid_ws_token = secrets.compare_digest(secret_ws_token, ws_token)
+        elif isinstance(secret_ws_token, list):
+            is_valid_ws_token = any([
+                secrets.compare_digest(potential, ws_token)
+                for potential in secret_ws_token
+            ])
+
+        if is_valid_ws_token:
+            return ws_token
+
+    # Check if ws_token is a JWT
+    try:
+        user = get_user_from_token(ws_token, secret_jwt_key)
+        return user
+    # If the token is a jwt, and it's valid return the user
+    except HTTPException:
+        pass
+
+    # No checks passed, deny the connection
+    logger.debug("Denying websocket request.")
+    # If it doesn't match, close the websocket connection
+    await ws.close(code=status.WS_1008_POLICY_VIOLATION)
+
+
 def create_token(data: dict, secret_key: str, token_type: str = "access") -> str:
     to_encode = data.copy()
     if token_type == "access":

freqtrade/rpc/api_server/api_v1.py

@@ -38,7 +38,8 @@ logger = logging.getLogger(__name__)
 # 2.15: Add backtest history endpoints
 # 2.16: Additional daily metrics
 # 2.17: Forceentry - leverage, partial force_exit
-API_VERSION = 2.17
+# 2.20: Add websocket endpoints
+API_VERSION = 2.20
 
 # Public API, requires no auth.
 router_public = APIRouter()

freqtrade/rpc/api_server/api_ws.py

@@ -0,0 +1,140 @@
+import logging
+from typing import Any, Dict
+
+from fastapi import APIRouter, Depends, WebSocketDisconnect
+from fastapi.websockets import WebSocket, WebSocketState
+from pydantic import ValidationError
+
+from freqtrade.enums import RPCMessageType, RPCRequestType
+from freqtrade.rpc.api_server.api_auth import validate_ws_token
+from freqtrade.rpc.api_server.deps import get_channel_manager, get_rpc
+from freqtrade.rpc.api_server.ws import WebSocketChannel
+from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema,
+                                                 WSRequestSchema, WSWhitelistMessage)
+from freqtrade.rpc.rpc import RPC
+
+
+logger = logging.getLogger(__name__)
+
+# Private router, protected by API Key authentication
+router = APIRouter()
+
+
+async def is_websocket_alive(ws: WebSocket) -> bool:
+    """
+    Check if a FastAPI Websocket is still open
+    """
+    if (
+        ws.application_state == WebSocketState.CONNECTED and
+        ws.client_state == WebSocketState.CONNECTED
+    ):
+        return True
+    return False
+
+
+async def _process_consumer_request(
+    request: Dict[str, Any],
+    channel: WebSocketChannel,
+    rpc: RPC
+):
+    """
+    Validate and handle a request from a websocket consumer
+    """
+    # Validate the request, makes sure it matches the schema
+    try:
+        websocket_request = WSRequestSchema.parse_obj(request)
+    except ValidationError as e:
+        logger.error(f"Invalid request from {channel}: {e}")
+        return
+
+    type, data = websocket_request.type, websocket_request.data
+    response: WSMessageSchema
+
+    logger.debug(f"Request of type {type} from {channel}")
+
+    # If we have a request of type SUBSCRIBE, set the topics in this channel
+    if type == RPCRequestType.SUBSCRIBE:
+        # If the request is empty, do nothing
+        if not data:
+            return
+
+        # If all topics passed are a valid RPCMessageType, set subscriptions on channel
+        if all([any(x.value == topic for x in RPCMessageType) for topic in data]):
+            channel.set_subscriptions(data)
+
+        # We don't send a response for subscriptions
+        return
+
+    elif type == RPCRequestType.WHITELIST:
+        # Get whitelist
+        whitelist = rpc._ws_request_whitelist()
+
+        # Format response
+        response = WSWhitelistMessage(data=whitelist)
+        # Send it back
+        await channel.send(response.dict(exclude_none=True))
+
+    elif type == RPCRequestType.ANALYZED_DF:
+        limit = None
+
+        if data:
+            # Limit the amount of candles per dataframe to 'limit' or 1500
+            limit = max(data.get('limit', 1500), 1500)
+
+        # They requested the full historical analyzed dataframes
+        analyzed_df = rpc._ws_request_analyzed_df(limit)
+
+        # For every dataframe, send as a separate message
+        for _, message in analyzed_df.items():
+            response = WSAnalyzedDFMessage(data=message)
+            await channel.send(response.dict(exclude_none=True))
+
+
+@router.websocket("/message/ws")
+async def message_endpoint(
+    ws: WebSocket,
+    rpc: RPC = Depends(get_rpc),
+    channel_manager=Depends(get_channel_manager),
+    token: str = Depends(validate_ws_token)
+):
+    """
+    Message WebSocket endpoint, facilitates sending RPC messages
+    """
+    try:
+        channel = await channel_manager.on_connect(ws)
+
+        if await is_websocket_alive(ws):
+
+            logger.info(f"Consumer connected - {channel}")
+
+            # Keep connection open until explicitly closed, and process requests
+            try:
+                while not channel.is_closed():
+                    request = await channel.recv()
+
+                    # Process the request here
+                    await _process_consumer_request(request, channel, rpc)
+
+            except WebSocketDisconnect:
+                # 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)
+                # Handle cases like -
+                # RuntimeError('Cannot call "send" once a closed message has been sent')
+                await channel_manager.on_disconnect(ws)
+
+        else:
+            await ws.close()
+
+    except RuntimeError:
+        # WebSocket was closed
+        await channel_manager.on_disconnect(ws)
+
+    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)
+        await channel_manager.on_disconnect(ws)

freqtrade/rpc/api_server/deps.py

@@ -41,6 +41,10 @@ def get_exchange(config=Depends(get_config)):
     return ApiServer._exchange
 
 
+def get_channel_manager():
+    return ApiServer._ws_channel_manager
+
+
 def is_webserver_mode(config=Depends(get_config)):
     if config['runmode'] != RunMode.WEBSERVER:
         raise RPCException('Bot is not in the correct state')

freqtrade/rpc/api_server/webserver.py

@@ -1,15 +1,21 @@
+import asyncio
 import logging
 from ipaddress import IPv4Address
+from threading import Thread
 from typing import Any, Dict
 
 import orjson
 import uvicorn
 from fastapi import Depends, FastAPI
 from fastapi.middleware.cors import CORSMiddleware
+# Look into alternatives
+from janus import Queue as ThreadedQueue
 from starlette.responses import JSONResponse
 
+from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
+from freqtrade.rpc.api_server.ws import ChannelManager
 from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
 
 
@@ -37,12 +43,16 @@ class ApiServer(RPCHandler):
     _bt = None
     _bt_data = None
     _bt_timerange = None
-    _bt_last_config: Dict[str, Any] = {}
+    _bt_last_config: Config = {}
     _has_rpc: bool = False
     _bgtask_running: bool = False
-    _config: Dict[str, Any] = {}
+    _config: Config = {}
     # Exchange - only available in webserver mode.
     _exchange = None
+    # websocket message queue stuff
+    _ws_channel_manager = None
+    _ws_thread = None
+    _ws_loop = None
 
     def __new__(cls, *args, **kwargs):
         """
@@ -54,23 +64,27 @@ class ApiServer(RPCHandler):
             ApiServer.__initialized = False
         return ApiServer.__instance
 
-    def __init__(self, config: Dict[str, Any], standalone: bool = False) -> None:
+    def __init__(self, config: Config, standalone: bool = False) -> None:
         ApiServer._config = config
         if self.__initialized and (standalone or self._standalone):
             return
         self._standalone: bool = standalone
         self._server = None
+        self._ws_queue = None
+        self._ws_background_task = None
+
         ApiServer.__initialized = True
 
         api_config = self._config['api_server']
 
+        ApiServer._ws_channel_manager = ChannelManager()
+
         self.app = FastAPI(title="Freqtrade API",
                            docs_url='/docs' if api_config.get('enable_openapi', False) else None,
                            redoc_url=None,
                            default_response_class=FTJSONResponse,
                            )
         self.configure_app(self.app, self._config)
-
         self.start_api()
 
     def add_rpc_handler(self, rpc: RPC):
@@ -92,6 +106,19 @@ class ApiServer(RPCHandler):
             logger.info("Stopping API Server")
             self._server.cleanup()
 
+        if self._ws_thread and self._ws_loop:
+            logger.info("Stopping API Server background tasks")
+
+            if self._ws_background_task:
+                # Cancel the queue task
+                self._ws_background_task.cancel()
+
+            self._ws_thread.join()
+
+        self._ws_thread = None
+        self._ws_loop = None
+        self._ws_background_task = None
+
     @classmethod
     def shutdown(cls):
         cls.__initialized = False
@@ -101,7 +128,9 @@ class ApiServer(RPCHandler):
         cls._rpc = None
 
     def send_msg(self, msg: Dict[str, str]) -> None:
-        pass
+        if self._ws_queue:
+            sync_q = self._ws_queue.sync_q
+            sync_q.put(msg)
 
     def handle_rpc_exception(self, request, exc):
         logger.exception(f"API Error calling: {exc}")
@@ -115,6 +144,7 @@ class ApiServer(RPCHandler):
         from freqtrade.rpc.api_server.api_backtest import router as api_backtest
         from freqtrade.rpc.api_server.api_v1 import router as api_v1
         from freqtrade.rpc.api_server.api_v1 import router_public as api_v1_public
+        from freqtrade.rpc.api_server.api_ws import router as ws_router
         from freqtrade.rpc.api_server.web_ui import router_ui
 
         app.include_router(api_v1_public, prefix="/api/v1")
@@ -125,6 +155,7 @@ class ApiServer(RPCHandler):
         app.include_router(api_backtest, prefix="/api/v1",
                            dependencies=[Depends(http_basic_or_jwt_token)],
                            )
+        app.include_router(ws_router, prefix="/api/v1")
         app.include_router(router_login, prefix="/api/v1", tags=["auth"])
         # UI Router MUST be last!
         app.include_router(router_ui, prefix='')
@@ -139,6 +170,48 @@ class ApiServer(RPCHandler):
 
         app.add_exception_handler(RPCException, self.handle_rpc_exception)
 
+    def start_message_queue(self):
+        if self._ws_thread:
+            return
+
+        # Create a new loop, as it'll be just for the background thread
+        self._ws_loop = asyncio.new_event_loop()
+
+        # Start the thread
+        self._ws_thread = Thread(target=self._ws_loop.run_forever)
+        self._ws_thread.start()
+
+        # Finally, submit the coro to the thread
+        self._ws_background_task = asyncio.run_coroutine_threadsafe(
+            self._broadcast_queue_data(), loop=self._ws_loop)
+
+    async def _broadcast_queue_data(self):
+        # Instantiate the queue in this coroutine so it's attached to our loop
+        self._ws_queue = ThreadedQueue()
+        async_queue = self._ws_queue.async_q
+
+        try:
+            while True:
+                logger.debug("Getting queue messages...")
+                # Get data from queue
+                message = await async_queue.get()
+                logger.debug(f"Found message of type: {message.get('type')}")
+                # Broadcast it
+                await self._ws_channel_manager.broadcast(message)
+                # Sleep, make this configurable?
+                await asyncio.sleep(0.1)
+        except asyncio.CancelledError:
+            pass
+
+        # For testing, shouldn't happen when stable
+        except Exception as e:
+            logger.exception(f"Exception happened in background task: {e}")
+
+        finally:
+            # Disconnect channels and stop the loop on cancel
+            await self._ws_channel_manager.disconnect_all()
+            self._ws_loop.stop()
+
     def start_api(self):
         """
         Start API ... should be run in thread.
@@ -176,6 +249,7 @@ class ApiServer(RPCHandler):
             if self._standalone:
                 self._server.run()
             else:
+                self.start_message_queue()
                 self._server.run_in_thread()
         except Exception:
             logger.exception("Api server failed to start.")

freqtrade/rpc/api_server/ws/__init__.py

@@ -0,0 +1,6 @@
+# flake8: noqa: F401
+# isort: off
+from freqtrade.rpc.api_server.ws.types import WebSocketType
+from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
+from freqtrade.rpc.api_server.ws.serializer import HybridJSONWebSocketSerializer
+from freqtrade.rpc.api_server.ws.channel import ChannelManager, WebSocketChannel

freqtrade/rpc/api_server/ws/channel.py

@@ -0,0 +1,178 @@
+import logging
+from threading import RLock
+from typing import List, Optional, Type
+from uuid import uuid4
+
+from fastapi import WebSocket as FastAPIWebSocket
+
+from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
+from freqtrade.rpc.api_server.ws.serializer import (HybridJSONWebSocketSerializer,
+                                                    WebSocketSerializer)
+from freqtrade.rpc.api_server.ws.types import WebSocketType
+
+
+logger = logging.getLogger(__name__)
+
+
+class WebSocketChannel:
+    """
+    Object to help facilitate managing a websocket connection
+    """
+
+    def __init__(
+        self,
+        websocket: WebSocketType,
+        channel_id: Optional[str] = None,
+        serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer
+    ):
+
+        self.channel_id = channel_id if channel_id else uuid4().hex[:8]
+
+        # The WebSocket object
+        self._websocket = WebSocketProxy(websocket)
+        # The Serializing class for the WebSocket object
+        self._serializer_cls = serializer_cls
+
+        self._subscriptions: List[str] = []
+
+        # Internal event to signify a closed websocket
+        self._closed = False
+
+        # Wrap the WebSocket in the Serializing class
+        self._wrapped_ws = self._serializer_cls(self._websocket)
+
+    def __repr__(self):
+        return f"WebSocketChannel({self.channel_id}, {self.remote_addr})"
+
+    @property
+    def remote_addr(self):
+        return self._websocket.remote_addr
+
+    async def send(self, data):
+        """
+        Send data on the wrapped websocket
+        """
+        await self._wrapped_ws.send(data)
+
+    async def recv(self):
+        """
+        Receive data on the wrapped websocket
+        """
+        return await self._wrapped_ws.recv()
+
+    async def ping(self):
+        """
+        Ping the websocket
+        """
+        return await self._websocket.ping()
+
+    async def close(self):
+        """
+        Close the WebSocketChannel
+        """
+
+        self._closed = True
+
+    def is_closed(self) -> bool:
+        """
+        Closed flag
+        """
+        return self._closed
+
+    def set_subscriptions(self, subscriptions: List[str] = []) -> None:
+        """
+        Set which subscriptions this channel is subscribed to
+
+        :param subscriptions: List of subscriptions, List[str]
+        """
+        self._subscriptions = subscriptions
+
+    def subscribed_to(self, message_type: str) -> bool:
+        """
+        Check if this channel is subscribed to the message_type
+
+        :param message_type: The message type to check
+        """
+        return message_type in self._subscriptions
+
+
+class ChannelManager:
+    def __init__(self):
+        self.channels = dict()
+        self._lock = RLock()  # Re-entrant Lock
+
+    async def on_connect(self, websocket: WebSocketType):
+        """
+        Wrap websocket connection into Channel and add to list
+
+        :param websocket: The WebSocket object to attach to the Channel
+        """
+        if isinstance(websocket, FastAPIWebSocket):
+            try:
+                await websocket.accept()
+            except RuntimeError:
+                # The connection was closed before we could accept it
+                return
+
+        ws_channel = WebSocketChannel(websocket)
+
+        with self._lock:
+            self.channels[websocket] = ws_channel
+
+        return ws_channel
+
+    async def on_disconnect(self, websocket: WebSocketType):
+        """
+        Call close on the channel if it's not, and remove from channel list
+
+        :param websocket: The WebSocket objet attached to the Channel
+        """
+        with self._lock:
+            channel = self.channels.get(websocket)
+            if channel:
+                if not channel.is_closed():
+                    await channel.close()
+
+                del self.channels[websocket]
+
+    async def disconnect_all(self):
+        """
+        Disconnect all Channels
+        """
+        with self._lock:
+            for websocket, channel in self.channels.items():
+                if not channel.is_closed():
+                    await channel.close()
+
+            self.channels = dict()
+
+    async def broadcast(self, data):
+        """
+        Broadcast data on all Channels
+
+        :param data: The data to send
+        """
+        with self._lock:
+            message_type = data.get('type')
+            for websocket, channel in self.channels.items():
+                try:
+                    if channel.subscribed_to(message_type):
+                        await channel.send(data)
+                except RuntimeError:
+                    # Handle cannot send after close cases
+                    await self.on_disconnect(websocket)
+
+    async def send_direct(self, channel, data):
+        """
+        Send data directly through direct_channel only
+
+        :param direct_channel: The WebSocketChannel object to send data through
+        :param data: The data to send
+        """
+        await channel.send(data)
+
+    def has_channels(self):
+        """
+        Flag for more than 0 channels
+        """
+        return len(self.channels) > 0

freqtrade/rpc/api_server/ws/proxy.py

@@ -0,0 +1,69 @@
+from typing import Any, Tuple, Union
+
+from fastapi import WebSocket as FastAPIWebSocket
+from websockets.client import WebSocketClientProtocol as WebSocket
+
+from freqtrade.rpc.api_server.ws.types import WebSocketType
+
+
+class WebSocketProxy:
+    """
+    WebSocketProxy object to bring the FastAPIWebSocket and websockets.WebSocketClientProtocol
+    under the same API
+    """
+
+    def __init__(self, websocket: WebSocketType):
+        self._websocket: Union[FastAPIWebSocket, WebSocket] = websocket
+
+    @property
+    def remote_addr(self) -> Tuple[Any, ...]:
+        if isinstance(self._websocket, WebSocket):
+            return self._websocket.remote_address
+        elif isinstance(self._websocket, FastAPIWebSocket):
+            if self._websocket.client:
+                client, port = self._websocket.client.host, self._websocket.client.port
+                return (client, port)
+        return ("unknown", 0)
+
+    async def send(self, data):
+        """
+        Send data on the wrapped websocket
+        """
+        if hasattr(self._websocket, "send_text"):
+            await self._websocket.send_text(data)
+        else:
+            await self._websocket.send(data)
+
+    async def recv(self):
+        """
+        Receive data on the wrapped websocket
+        """
+        if hasattr(self._websocket, "receive_text"):
+            return await self._websocket.receive_text()
+        else:
+            return await self._websocket.recv()
+
+    async def ping(self):
+        """
+        Ping the websocket, not supported by FastAPI WebSockets
+        """
+        if hasattr(self._websocket, "ping"):
+            return await self._websocket.ping()
+        return False
+
+    async def close(self, code: int = 1000):
+        """
+        Close the websocket connection, only supported by FastAPI WebSockets
+        """
+        if hasattr(self._websocket, "close"):
+            try:
+                return await self._websocket.close(code)
+            except RuntimeError:
+                pass
+
+    async def accept(self):
+        """
+        Accept the WebSocket connection, only support by FastAPI WebSockets
+        """
+        if hasattr(self._websocket, "accept"):
+            return await self._websocket.accept()

freqtrade/rpc/api_server/ws/serializer.py

@@ -0,0 +1,62 @@
+import logging
+from abc import ABC, abstractmethod
+
+import orjson
+import rapidjson
+from pandas import DataFrame
+
+from freqtrade.misc import dataframe_to_json, json_to_dataframe
+from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
+
+
+logger = logging.getLogger(__name__)
+
+
+class WebSocketSerializer(ABC):
+    def __init__(self, websocket: WebSocketProxy):
+        self._websocket: WebSocketProxy = websocket
+
+    @abstractmethod
+    def _serialize(self, data):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def _deserialize(self, data):
+        raise NotImplementedError()
+
+    async def send(self, data: bytes):
+        await self._websocket.send(self._serialize(data))
+
+    async def recv(self) -> bytes:
+        data = await self._websocket.recv()
+
+        return self._deserialize(data)
+
+    async def close(self, code: int = 1000):
+        await self._websocket.close(code)
+
+
+class HybridJSONWebSocketSerializer(WebSocketSerializer):
+    def _serialize(self, data) -> str:
+        return str(orjson.dumps(data, default=_json_default), "utf-8")
+
+    def _deserialize(self, data: str):
+        # RapidJSON expects strings
+        return rapidjson.loads(data, object_hook=_json_object_hook)
+
+
+# Support serializing pandas DataFrames
+def _json_default(z):
+    if isinstance(z, DataFrame):
+        return {
+            '__type__': 'dataframe',
+            '__value__': dataframe_to_json(z)
+        }
+    raise TypeError
+
+
+# Support deserializing JSON to pandas DataFrames
+def _json_object_hook(z):
+    if z.get('__type__') == 'dataframe':
+        return json_to_dataframe(z.get('__value__'))
+    return z

freqtrade/rpc/api_server/ws/types.py

@@ -0,0 +1,8 @@
+from typing import Any, Dict, TypeVar
+
+from fastapi import WebSocket as FastAPIWebSocket
+from websockets.client import WebSocketClientProtocol as WebSocket
+
+
+WebSocketType = TypeVar("WebSocketType", FastAPIWebSocket, WebSocket)
+MessageType = Dict[str, Any]

freqtrade/rpc/api_server/ws_schemas.py

@@ -0,0 +1,63 @@
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+from pandas import DataFrame
+from pydantic import BaseModel
+
+from freqtrade.constants import PairWithTimeframe
+from freqtrade.enums.rpcmessagetype import RPCMessageType, RPCRequestType
+
+
+class BaseArbitraryModel(BaseModel):
+    class Config:
+        arbitrary_types_allowed = True
+
+
+class WSRequestSchema(BaseArbitraryModel):
+    type: RPCRequestType
+    data: Optional[Any] = None
+
+
+class WSMessageSchema(BaseArbitraryModel):
+    type: RPCMessageType
+    data: Optional[Any] = None
+
+    class Config:
+        extra = 'allow'
+
+
+# ------------------------------ REQUEST SCHEMAS ----------------------------
+
+
+class WSSubscribeRequest(WSRequestSchema):
+    type: RPCRequestType = RPCRequestType.SUBSCRIBE
+    data: List[RPCMessageType]
+
+
+class WSWhitelistRequest(WSRequestSchema):
+    type: RPCRequestType = RPCRequestType.WHITELIST
+    data: None = None
+
+
+class WSAnalyzedDFRequest(WSRequestSchema):
+    type: RPCRequestType = RPCRequestType.ANALYZED_DF
+    data: Dict[str, Any] = {"limit": 1500}
+
+
+# ------------------------------ MESSAGE SCHEMAS ----------------------------
+
+class WSWhitelistMessage(WSMessageSchema):
+    type: RPCMessageType = RPCMessageType.WHITELIST
+    data: List[str]
+
+
+class WSAnalyzedDFMessage(WSMessageSchema):
+    class AnalyzedDFData(BaseArbitraryModel):
+        key: PairWithTimeframe
+        df: DataFrame
+        la: datetime
+
+    type: RPCMessageType = RPCMessageType.ANALYZED_DF
+    data: AnalyzedDFData
+
+# --------------------------------------------------------------------------

freqtrade/rpc/discord.py

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