Merge remote-tracking branch 'origin/develop' into add-metric-tracker
This commit is contained in:
robcaulk
commit
b236e362ba
10
.github/workflows/ci.yml
vendored
10
.github/workflows/ci.yml
vendored
@ -24,7 +24,7 @@ jobs:
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
|
||||
python-version: ["3.8", "3.9", "3.10.6"]
|
||||
python-version: ["3.8", "3.9", "3.10"]
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
@ -74,7 +74,7 @@ jobs:
|
||||
if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
|
||||
|
||||
- name: Coveralls
|
||||
if: (runner.os == 'Linux' && matrix.python-version == '3.9')
|
||||
if: (runner.os == 'Linux' && matrix.python-version == '3.10' && matrix.os == 'ubuntu-22.04')
|
||||
env:
|
||||
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
|
||||
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
|
||||
@ -121,7 +121,7 @@ jobs:
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ macos-latest ]
|
||||
python-version: ["3.8", "3.9", "3.10.6"]
|
||||
python-version: ["3.8", "3.9", "3.10"]
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
@ -205,7 +205,7 @@ jobs:
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ windows-latest ]
|
||||
python-version: ["3.8", "3.9", "3.10.6"]
|
||||
python-version: ["3.8", "3.9", "3.10"]
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
@ -441,4 +441,4 @@ jobs:
|
||||
with:
|
||||
severity: info
|
||||
details: Deploy Succeeded!
|
||||
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
|
||||
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
|
||||
|
||||
@ -15,8 +15,8 @@ repos:
|
||||
additional_dependencies:
|
||||
- types-cachetools==5.2.1
|
||||
- types-filelock==3.2.7
|
||||
- types-requests==2.28.11
|
||||
- types-tabulate==0.8.11
|
||||
- types-requests==2.28.11.2
|
||||
- types-tabulate==0.9.0.0
|
||||
- types-python-dateutil==2.8.19
|
||||
# stages: [push]
|
||||
|
||||
|
||||
BIN
docs/assets/tensorboard.jpg
Normal file
BIN
docs/assets/tensorboard.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 362 KiB |
@ -215,16 +215,18 @@ Mandatory parameters are marked as **Required**, which means that they are requi
|
||||
| `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`. <br> **Datatype:** float
|
||||
| `telegram.reload` | Allow "reload" buttons on telegram messages. <br>*Defaults to `True`.<br> **Datatype:** boolean
|
||||
| `telegram.notification_settings.*` | Detailed notification settings. Refer to the [telegram documentation](telegram-usage.md) for details.<br> **Datatype:** dictionary
|
||||
| `telegram.allow_custom_messages` | Enable the sending of Telegram messages from strategies via the dataprovider.send_msg() function. <br> **Datatype:** Boolean
|
||||
| | **Webhook**
|
||||
| `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
|
||||
| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.webhookentry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.webhookentrycancel` | Payload to send on entry order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.webhookentryfill` | Payload to send on entry order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.webhookexit` | Payload to send on exit. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.webhookexitcancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.webhookexitfill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.entry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.entry_cancel` | Payload to send on entry order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.entry_fill` | Payload to send on entry order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.exit` | Payload to send on exit. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.exit_cancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.exit_fill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.status` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.allow_custom_messages` | Enable the sending of Webhook messages from strategies via the dataprovider.send_msg() function. <br> **Datatype:** Boolean
|
||||
| | **Rest API / FreqUI / Producer-Consumer**
|
||||
| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
|
||||
| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
|
||||
|
||||
@ -66,11 +66,11 @@ We will keep a compatibility layer for 1-2 versions (so both `buy_tag` and `ente
|
||||
|
||||
#### Naming changes
|
||||
|
||||
Webhook terminology changed from "sell" to "exit", and from "buy" to "entry".
|
||||
Webhook terminology changed from "sell" to "exit", and from "buy" to "entry", removing "webhook" in the process.
|
||||
|
||||
* `webhookbuy` -> `webhookentry`
|
||||
* `webhookbuyfill` -> `webhookentryfill`
|
||||
* `webhookbuycancel` -> `webhookentrycancel`
|
||||
* `webhooksell` -> `webhookexit`
|
||||
* `webhooksellfill` -> `webhookexitfill`
|
||||
* `webhooksellcancel` -> `webhookexitcancel`
|
||||
* `webhookbuy`, `webhookentry` -> `entry`
|
||||
* `webhookbuyfill`, `webhookentryfill` -> `entry_fill`
|
||||
* `webhookbuycancel`, `webhookentrycancel` -> `entry_cancel`
|
||||
* `webhooksell`, `webhookexit` -> `exit`
|
||||
* `webhooksellfill`, `webhookexitfill` -> `exit_fill`
|
||||
* `webhooksellcancel`, `webhookexitcancel` -> `exit_cancel`
|
||||
|
||||
@ -204,14 +204,44 @@ If this value is set, FreqAI will initially use the predictions from the trainin
|
||||
|
||||
## Using different prediction models
|
||||
|
||||
FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures.
|
||||
FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `CatBoost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`.
|
||||
|
||||
### Setting classifier targets
|
||||
Regression and classification models differ in what targets they predict - a regression model will predict a target of continuous values, for example what price BTC will be at tomorrow, whilst a classifier will predict a target of discrete values, for example if the price of BTC will go up tomorrow or not. This means that you have to specify your targets differently depending on which model type you are using (see details [below](#setting-model-targets)).
|
||||
|
||||
FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example:
|
||||
All of the aforementioned model libraries implement gradient boosted decision tree algorithms. They all work on the principle of ensemble learning, where predictions from multiple simple learners are combined to get a final prediction that is more stable and generalized. The simple learners in this case are decision trees. Gradient boosting refers to the method of learning, where each simple learner is built in sequence - the subsequent learner is used to improve on the error from the previous learner. If you want to learn more about the different model libraries you can find the information in their respective docs:
|
||||
|
||||
* CatBoost: https://catboost.ai/en/docs/
|
||||
* LightGBM: https://lightgbm.readthedocs.io/en/v3.3.2/#
|
||||
* XGBoost: https://xgboost.readthedocs.io/en/stable/#
|
||||
|
||||
There are also numerous online articles describing and comparing the algorithms. Some relatively light-weight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.
|
||||
|
||||
Apart from the models already available in FreqAI, it is also possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to customize various aspects of the training procedures. You can place custom FreqAI models in `user_data/freqaimodels` - and freqtrade will pick them up from there based on the provided `--freqaimodel` name - which has to correspond to the class name of your custom model.
|
||||
Make sure to use unique names to avoid overriding built-in models.
|
||||
|
||||
### Setting model targets
|
||||
|
||||
#### Regressors
|
||||
|
||||
If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the `CatboostRegressor`via the flag `--freqaimodel CatboostRegressor`. An example of how you could set a regression target for predicting the price 100 candles into the future would be
|
||||
|
||||
```python
|
||||
df['&s-close_price'] = df['close'].shift(-100)
|
||||
```
|
||||
|
||||
If you want to predict multiple targets, you need to define multiple labels using the same syntax as shown above.
|
||||
|
||||
#### Classifiers
|
||||
|
||||
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
|
||||
|
||||
```python
|
||||
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
|
||||
```
|
||||
|
||||
Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column.
|
||||
If you want to predict multiple targets you must specify all labels in the same label column. You could, for example, add the label `same` to define where the price was unchanged by setting
|
||||
|
||||
```python
|
||||
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
|
||||
df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down'])
|
||||
```
|
||||
|
||||
@ -43,7 +43,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
|
||||
| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
|
||||
| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
|
||||
| | **Model training parameters**
|
||||
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. <br> **Datatype:** Dictionary.
|
||||
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. A list of the currently available models can be found [here](freqai-configuration.md#using-different-prediction-models). <br> **Datatype:** Dictionary.
|
||||
| `n_estimators` | The number of boosted trees to fit in the training of the model. <br> **Datatype:** Integer.
|
||||
| `learning_rate` | Boosting learning rate during training of the model. <br> **Datatype:** Float.
|
||||
| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
|
||||
|
||||
@ -142,6 +142,19 @@ dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1
|
||||
|
||||
This specific hyperopt would help you understand the appropriate `DI_values` for your particular parameter space.
|
||||
|
||||
## Using Tensorboard
|
||||
|
||||
CatBoost models benefit from tracking training metrics via Tensorboard. You can take advantage of the FreqAI integration to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
|
||||
|
||||
```bash
|
||||
cd freqtrade
|
||||
tensorboard --logdir user_data/models/unique-id
|
||||
```
|
||||
|
||||
where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell if you wish to view the output in your browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
|
||||
|
||||
|
||||
|
||||
## Setting up a follower
|
||||
|
||||
You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining:
|
||||
|
||||
@ -43,19 +43,25 @@ Note : `forcesell`, `forcebuy`, `emergencysell` are changed to `force_exit`, `fo
|
||||
* `order_time_in_force` buy -> entry, sell -> exit.
|
||||
* `order_types` buy -> entry, sell -> exit.
|
||||
* `unfilledtimeout` buy -> entry, sell -> exit.
|
||||
* `ignore_buying_expired_candle_after` -> moved to root level instead of "ask_strategy/exit_pricing"
|
||||
* Terminology changes
|
||||
* Sell reasons changed to reflect the new naming of "exit" instead of sells. Be careful in your strategy if you're using `exit_reason` checks and eventually update your strategy.
|
||||
* `sell_signal` -> `exit_signal`
|
||||
* `custom_sell` -> `custom_exit`
|
||||
* `force_sell` -> `force_exit`
|
||||
* `emergency_sell` -> `emergency_exit`
|
||||
* Order pricing
|
||||
* `bid_strategy` -> `entry_pricing`
|
||||
* `ask_strategy` -> `exit_pricing`
|
||||
* `ask_last_balance` -> `price_last_balance`
|
||||
* `bid_last_balance` -> `price_last_balance`
|
||||
* Webhook terminology changed from "sell" to "exit", and from "buy" to entry
|
||||
* `webhookbuy` -> `webhookentry`
|
||||
* `webhookbuyfill` -> `webhookentryfill`
|
||||
* `webhookbuycancel` -> `webhookentrycancel`
|
||||
* `webhooksell` -> `webhookexit`
|
||||
* `webhooksellfill` -> `webhookexitfill`
|
||||
* `webhooksellcancel` -> `webhookexitcancel`
|
||||
* `webhookbuy` -> `entry`
|
||||
* `webhookbuyfill` -> `entry_fill`
|
||||
* `webhookbuycancel` -> `entry_cancel`
|
||||
* `webhooksell` -> `exit`
|
||||
* `webhooksellfill` -> `exit_fill`
|
||||
* `webhooksellcancel` -> `exit_cancel`
|
||||
* Telegram notification settings
|
||||
* `buy` -> `entry`
|
||||
* `buy_fill` -> `entry_fill`
|
||||
@ -443,6 +449,7 @@ Please refer to the [pricing documentation](configuration.md#prices-used-for-ord
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"bid_last_balance": 0.0
|
||||
"ignore_buying_expired_candle_after": 120
|
||||
}
|
||||
}
|
||||
```
|
||||
@ -466,6 +473,7 @@ after:
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0
|
||||
}
|
||||
},
|
||||
"ignore_buying_expired_candle_after": 120
|
||||
}
|
||||
```
|
||||
|
||||
@ -77,6 +77,7 @@ Example configuration showing the different settings:
|
||||
"enabled": true,
|
||||
"token": "your_telegram_token",
|
||||
"chat_id": "your_telegram_chat_id",
|
||||
"allow_custom_messages": true,
|
||||
"notification_settings": {
|
||||
"status": "silent",
|
||||
"warning": "on",
|
||||
@ -115,6 +116,7 @@ Example configuration showing the different settings:
|
||||
`show_candle` - show candle values as part of entry/exit messages. Only possible values are `"ohlc"` or `"off"`.
|
||||
|
||||
`balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
|
||||
`allow_custom_messages` completely disable strategy messages.
|
||||
`reload` allows you to disable reload-buttons on selected messages.
|
||||
|
||||
## Create a custom keyboard (command shortcut buttons)
|
||||
|
||||
@ -10,37 +10,37 @@ Sample configuration (tested using IFTTT).
|
||||
"webhook": {
|
||||
"enabled": true,
|
||||
"url": "https://maker.ifttt.com/trigger/<YOUREVENT>/with/key/<YOURKEY>/",
|
||||
"webhookentry": {
|
||||
"entry": {
|
||||
"value1": "Buying {pair}",
|
||||
"value2": "limit {limit:8f}",
|
||||
"value3": "{stake_amount:8f} {stake_currency}"
|
||||
},
|
||||
"webhookentrycancel": {
|
||||
"entry_cancel": {
|
||||
"value1": "Cancelling Open Buy Order for {pair}",
|
||||
"value2": "limit {limit:8f}",
|
||||
"value3": "{stake_amount:8f} {stake_currency}"
|
||||
},
|
||||
"webhookentryfill": {
|
||||
"entry_fill": {
|
||||
"value1": "Buy Order for {pair} filled",
|
||||
"value2": "at {open_rate:8f}",
|
||||
"value3": ""
|
||||
},
|
||||
"webhookexit": {
|
||||
"exit": {
|
||||
"value1": "Exiting {pair}",
|
||||
"value2": "limit {limit:8f}",
|
||||
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
|
||||
},
|
||||
"webhookexitcancel": {
|
||||
"exit_cancel": {
|
||||
"value1": "Cancelling Open Exit Order for {pair}",
|
||||
"value2": "limit {limit:8f}",
|
||||
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
|
||||
},
|
||||
"webhookexitfill": {
|
||||
"exit_fill": {
|
||||
"value1": "Exit Order for {pair} filled",
|
||||
"value2": "at {close_rate:8f}.",
|
||||
"value3": ""
|
||||
},
|
||||
"webhookstatus": {
|
||||
"status": {
|
||||
"value1": "Status: {status}",
|
||||
"value2": "",
|
||||
"value3": ""
|
||||
@ -57,7 +57,7 @@ You can set the POST body format to Form-Encoded (default), JSON-Encoded, or raw
|
||||
"enabled": true,
|
||||
"url": "https://<YOURSUBDOMAIN>.cloud.mattermost.com/hooks/<YOURHOOK>",
|
||||
"format": "json",
|
||||
"webhookstatus": {
|
||||
"status": {
|
||||
"text": "Status: {status}"
|
||||
}
|
||||
},
|
||||
@ -88,17 +88,30 @@ Optional parameters are available to enable automatic retries for webhook messag
|
||||
"url": "https://<YOURHOOKURL>",
|
||||
"retries": 3,
|
||||
"retry_delay": 0.2,
|
||||
"webhookstatus": {
|
||||
"status": {
|
||||
"status": "Status: {status}"
|
||||
}
|
||||
},
|
||||
```
|
||||
|
||||
Custom messages can be sent to Webhook endpoints via the `self.dp.send_msg()` function from within the strategy. To enable this, set the `allow_custom_messages` option to `true`:
|
||||
|
||||
```json
|
||||
"webhook": {
|
||||
"enabled": true,
|
||||
"url": "https://<YOURHOOKURL>",
|
||||
"allow_custom_messages": true,
|
||||
"strategy_msg": {
|
||||
"status": "StrategyMessage: {msg}"
|
||||
}
|
||||
},
|
||||
```
|
||||
|
||||
Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
|
||||
|
||||
### Webhookentry
|
||||
### Entry
|
||||
|
||||
The fields in `webhook.webhookentry` are filled when the bot executes a long/short. Parameters are filled using string.format.
|
||||
The fields in `webhook.entry` are filled when the bot executes a long/short. Parameters are filled using string.format.
|
||||
Possible parameters are:
|
||||
|
||||
* `trade_id`
|
||||
@ -118,9 +131,9 @@ Possible parameters are:
|
||||
* `current_rate`
|
||||
* `enter_tag`
|
||||
|
||||
### Webhookentrycancel
|
||||
### Entry cancel
|
||||
|
||||
The fields in `webhook.webhookentrycancel` are filled when the bot cancels a long/short order. Parameters are filled using string.format.
|
||||
The fields in `webhook.entry_cancel` are filled when the bot cancels a long/short order. Parameters are filled using string.format.
|
||||
Possible parameters are:
|
||||
|
||||
* `trade_id`
|
||||
@ -139,9 +152,9 @@ Possible parameters are:
|
||||
* `current_rate`
|
||||
* `enter_tag`
|
||||
|
||||
### Webhookentryfill
|
||||
### Entry fill
|
||||
|
||||
The fields in `webhook.webhookentryfill` are filled when the bot filled a long/short order. Parameters are filled using string.format.
|
||||
The fields in `webhook.entry_fill` are filled when the bot filled a long/short order. Parameters are filled using string.format.
|
||||
Possible parameters are:
|
||||
|
||||
* `trade_id`
|
||||
@ -160,9 +173,9 @@ Possible parameters are:
|
||||
* `current_rate`
|
||||
* `enter_tag`
|
||||
|
||||
### Webhookexit
|
||||
### Exit
|
||||
|
||||
The fields in `webhook.webhookexit` are filled when the bot exits a trade. Parameters are filled using string.format.
|
||||
The fields in `webhook.exit` are filled when the bot exits a trade. Parameters are filled using string.format.
|
||||
Possible parameters are:
|
||||
|
||||
* `trade_id`
|
||||
@ -184,9 +197,9 @@ Possible parameters are:
|
||||
* `open_date`
|
||||
* `close_date`
|
||||
|
||||
### Webhookexitfill
|
||||
### Exit fill
|
||||
|
||||
The fields in `webhook.webhookexitfill` are filled when the bot fills a exit order (closes a Trade). Parameters are filled using string.format.
|
||||
The fields in `webhook.exit_fill` are filled when the bot fills a exit order (closes a Trade). Parameters are filled using string.format.
|
||||
Possible parameters are:
|
||||
|
||||
* `trade_id`
|
||||
@ -209,9 +222,9 @@ Possible parameters are:
|
||||
* `open_date`
|
||||
* `close_date`
|
||||
|
||||
### Webhookexitcancel
|
||||
### Exit cancel
|
||||
|
||||
The fields in `webhook.webhookexitcancel` are filled when the bot cancels a exit order. Parameters are filled using string.format.
|
||||
The fields in `webhook.exit_cancel` are filled when the bot cancels a exit order. Parameters are filled using string.format.
|
||||
Possible parameters are:
|
||||
|
||||
* `trade_id`
|
||||
@ -234,9 +247,9 @@ Possible parameters are:
|
||||
* `open_date`
|
||||
* `close_date`
|
||||
|
||||
### Webhookstatus
|
||||
### Status
|
||||
|
||||
The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
|
||||
The fields in `webhook.status` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
|
||||
|
||||
The only possible value here is `{status}`.
|
||||
|
||||
@ -280,7 +293,6 @@ You can configure this as follows:
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
The above represents the default (`exit_fill` and `entry_fill` are optional and will default to the above configuration) - modifications are obviously possible.
|
||||
|
||||
Available fields correspond to the fields for webhooks and are documented in the corresponding webhook sections.
|
||||
@ -288,3 +300,13 @@ Available fields correspond to the fields for webhooks and are documented in the
|
||||
The notifications will look as follows by default.
|
||||
|
||||
|
||||
|
||||
Custom messages can be sent from a strategy to Discord endpoints via the dataprovider.send_msg() function. To enable this, set the `allow_custom_messages` option to `true`:
|
||||
|
||||
```json
|
||||
"discord": {
|
||||
"enabled": true,
|
||||
"webhook_url": "https://discord.com/api/webhooks/<Your webhook URL ...>",
|
||||
"allow_custom_messages": true,
|
||||
},
|
||||
```
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
import csv
|
||||
import logging
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import rapidjson
|
||||
@ -10,7 +9,6 @@ from colorama import init as colorama_init
|
||||
from tabulate import tabulate
|
||||
|
||||
from freqtrade.configuration import setup_utils_configuration
|
||||
from freqtrade.constants import USERPATH_STRATEGIES
|
||||
from freqtrade.enums import RunMode
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange import market_is_active, validate_exchanges
|
||||
@ -41,7 +39,7 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
|
||||
print(tabulate(exchanges, headers=['Exchange name', 'Valid', 'reason']))
|
||||
|
||||
|
||||
def _print_objs_tabular(objs: List, print_colorized: bool, base_dir: Path) -> None:
|
||||
def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
|
||||
if print_colorized:
|
||||
colorama_init(autoreset=True)
|
||||
red = Fore.RED
|
||||
@ -55,7 +53,7 @@ def _print_objs_tabular(objs: List, print_colorized: bool, base_dir: Path) -> No
|
||||
names = [s['name'] for s in objs]
|
||||
objs_to_print = [{
|
||||
'name': s['name'] if s['name'] else "--",
|
||||
'location': s['location'].relative_to(base_dir),
|
||||
'location': s['location_rel'],
|
||||
'status': (red + "LOAD FAILED" + reset if s['class'] is None
|
||||
else "OK" if names.count(s['name']) == 1
|
||||
else yellow + "DUPLICATE NAME" + reset)
|
||||
@ -76,9 +74,8 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
|
||||
"""
|
||||
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
|
||||
|
||||
directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
|
||||
strategy_objs = StrategyResolver.search_all_objects(
|
||||
directory, not args['print_one_column'], config.get('recursive_strategy_search', False))
|
||||
config, not args['print_one_column'], config.get('recursive_strategy_search', False))
|
||||
# Sort alphabetically
|
||||
strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
|
||||
for obj in strategy_objs:
|
||||
@ -90,7 +87,7 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
|
||||
if args['print_one_column']:
|
||||
print('\n'.join([s['name'] for s in strategy_objs]))
|
||||
else:
|
||||
_print_objs_tabular(strategy_objs, config.get('print_colorized', False), directory)
|
||||
_print_objs_tabular(strategy_objs, config.get('print_colorized', False))
|
||||
|
||||
|
||||
def start_list_timeframes(args: Dict[str, Any]) -> None:
|
||||
|
||||
@ -3,7 +3,8 @@ import shutil
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from freqtrade.constants import USER_DATA_FILES, Config
|
||||
from freqtrade.constants import (USER_DATA_FILES, USERPATH_FREQAIMODELS, USERPATH_HYPEROPTS,
|
||||
USERPATH_NOTEBOOKS, USERPATH_STRATEGIES, Config)
|
||||
from freqtrade.exceptions import OperationalException
|
||||
|
||||
|
||||
@ -49,8 +50,8 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
|
||||
:param create_dir: Create directory if it does not exist.
|
||||
:return: Path object containing the directory
|
||||
"""
|
||||
sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
|
||||
"notebooks", "plot", "strategies", ]
|
||||
sub_dirs = ["backtest_results", "data", USERPATH_HYPEROPTS, "hyperopt_results", "logs",
|
||||
USERPATH_NOTEBOOKS, "plot", USERPATH_STRATEGIES, USERPATH_FREQAIMODELS]
|
||||
folder = Path(directory)
|
||||
chown_user_directory(folder)
|
||||
if not folder.is_dir():
|
||||
|
||||
@ -5,7 +5,7 @@ bot constants
|
||||
"""
|
||||
from typing import Any, Dict, List, Literal, Tuple
|
||||
|
||||
from freqtrade.enums import CandleType
|
||||
from freqtrade.enums import CandleType, RPCMessageType
|
||||
|
||||
|
||||
DEFAULT_CONFIG = 'config.json'
|
||||
@ -282,6 +282,7 @@ CONF_SCHEMA = {
|
||||
'enabled': {'type': 'boolean'},
|
||||
'token': {'type': 'string'},
|
||||
'chat_id': {'type': 'string'},
|
||||
'allow_custom_messages': {'type': 'boolean', 'default': True},
|
||||
'balance_dust_level': {'type': 'number', 'minimum': 0.0},
|
||||
'notification_settings': {
|
||||
'type': 'object',
|
||||
@ -344,6 +345,8 @@ CONF_SCHEMA = {
|
||||
'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'},
|
||||
'retries': {'type': 'integer', 'minimum': 0},
|
||||
'retry_delay': {'type': 'number', 'minimum': 0},
|
||||
**dict([(x, {'type': 'object'}) for x in RPCMessageType]),
|
||||
# Below -> Deprecated
|
||||
'webhookentry': {'type': 'object'},
|
||||
'webhookentrycancel': {'type': 'object'},
|
||||
'webhookentryfill': {'type': 'object'},
|
||||
@ -655,5 +658,6 @@ LongShort = Literal['long', 'short']
|
||||
EntryExit = Literal['entry', 'exit']
|
||||
BuySell = Literal['buy', 'sell']
|
||||
MakerTaker = Literal['maker', 'taker']
|
||||
BidAsk = Literal['bid', 'ask']
|
||||
|
||||
Config = Dict[str, Any]
|
||||
|
||||
@ -11,6 +11,7 @@ from freqtrade.enums import CandleType, MarginMode, TradingMode
|
||||
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
|
||||
from freqtrade.exchange import Exchange
|
||||
from freqtrade.exchange.common import retrier
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.misc import deep_merge_dicts, json_load
|
||||
|
||||
|
||||
@ -59,7 +60,7 @@ class Binance(Exchange):
|
||||
)
|
||||
))
|
||||
|
||||
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
|
||||
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
|
||||
tickers = super().get_tickers(symbols=symbols, cached=cached)
|
||||
if self.trading_mode == TradingMode.FUTURES:
|
||||
# Binance's future result has no bid/ask values.
|
||||
|
||||
@ -20,8 +20,8 @@ from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
|
||||
from dateutil import parser
|
||||
from pandas import DataFrame, concat
|
||||
|
||||
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
|
||||
Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
|
||||
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BidAsk,
|
||||
BuySell, Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
|
||||
PairWithTimeframe)
|
||||
from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
|
||||
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
|
||||
@ -31,6 +31,7 @@ from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFun
|
||||
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
|
||||
EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
|
||||
remove_credentials, retrier, retrier_async)
|
||||
from freqtrade.exchange.types import Ticker, Tickers
|
||||
from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
|
||||
safe_value_fallback2)
|
||||
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
|
||||
@ -1420,14 +1421,17 @@ class Exchange:
|
||||
raise OperationalException(e) from e
|
||||
|
||||
@retrier
|
||||
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
|
||||
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
|
||||
"""
|
||||
:param cached: Allow cached result
|
||||
:return: fetch_tickers result
|
||||
"""
|
||||
tickers: Tickers
|
||||
if not self.exchange_has('fetchTickers'):
|
||||
return {}
|
||||
if cached:
|
||||
with self._cache_lock:
|
||||
tickers = self._fetch_tickers_cache.get('fetch_tickers')
|
||||
tickers = self._fetch_tickers_cache.get('fetch_tickers') # type: ignore
|
||||
if tickers:
|
||||
return tickers
|
||||
try:
|
||||
@ -1450,12 +1454,12 @@ class Exchange:
|
||||
# Pricing info
|
||||
|
||||
@retrier
|
||||
def fetch_ticker(self, pair: str) -> dict:
|
||||
def fetch_ticker(self, pair: str) -> Ticker:
|
||||
try:
|
||||
if (pair not in self.markets or
|
||||
self.markets[pair].get('active', False) is False):
|
||||
raise ExchangeError(f"Pair {pair} not available")
|
||||
data = self._api.fetch_ticker(pair)
|
||||
data: Ticker = self._api.fetch_ticker(pair)
|
||||
return data
|
||||
except ccxt.DDoSProtection as e:
|
||||
raise DDosProtection(e) from e
|
||||
@ -1506,7 +1510,7 @@ class Exchange:
|
||||
except ccxt.BaseError as e:
|
||||
raise OperationalException(e) from e
|
||||
|
||||
def _get_price_side(self, side: str, is_short: bool, conf_strategy: Dict) -> str:
|
||||
def _get_price_side(self, side: str, is_short: bool, conf_strategy: Dict) -> BidAsk:
|
||||
price_side = conf_strategy['price_side']
|
||||
|
||||
if price_side in ('same', 'other'):
|
||||
@ -1525,7 +1529,7 @@ class Exchange:
|
||||
|
||||
def get_rate(self, pair: str, refresh: bool,
|
||||
side: EntryExit, is_short: bool,
|
||||
order_book: Optional[dict] = None, ticker: Optional[dict] = None) -> float:
|
||||
order_book: Optional[dict] = None, ticker: Optional[Ticker] = None) -> float:
|
||||
"""
|
||||
Calculates bid/ask target
|
||||
bid rate - between current ask price and last price
|
||||
|
||||
@ -12,6 +12,7 @@ from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, Invali
|
||||
OperationalException, TemporaryError)
|
||||
from freqtrade.exchange import Exchange
|
||||
from freqtrade.exchange.common import retrier
|
||||
from freqtrade.exchange.types import Tickers
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -45,7 +46,7 @@ class Kraken(Exchange):
|
||||
return (parent_check and
|
||||
market.get('darkpool', False) is False)
|
||||
|
||||
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
|
||||
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
|
||||
# Only fetch tickers for current stake currency
|
||||
# Otherwise the request for kraken becomes too large.
|
||||
symbols = list(self.get_markets(quote_currencies=[self._config['stake_currency']]))
|
||||
|
||||
16
freqtrade/exchange/types.py
Normal file
16
freqtrade/exchange/types.py
Normal file
@ -0,0 +1,16 @@
|
||||
from typing import Dict, Optional, TypedDict
|
||||
|
||||
|
||||
class Ticker(TypedDict):
|
||||
symbol: str
|
||||
ask: Optional[float]
|
||||
askVolume: Optional[float]
|
||||
bid: Optional[float]
|
||||
bidVolume: Optional[float]
|
||||
last: Optional[float]
|
||||
quoteVolume: Optional[float]
|
||||
baseVolume: Optional[float]
|
||||
# Several more - only listing required.
|
||||
|
||||
|
||||
Tickers = Dict[str, Ticker]
|
||||
@ -78,7 +78,7 @@ class BaseClassifierModel(IFreqaiModel):
|
||||
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_df: Full dataframe for the current backtest period.
|
||||
:param unfiltered_df: Full dataframe for the current backtest period.
|
||||
:return:
|
||||
:pred_df: dataframe containing the predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
|
||||
@ -77,7 +77,7 @@ class BaseRegressionModel(IFreqaiModel):
|
||||
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_df: Full dataframe for the current backtest period.
|
||||
:param unfiltered_df: Full dataframe for the current backtest period.
|
||||
:return:
|
||||
:pred_df: dataframe containing the predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
|
||||
@ -461,9 +461,8 @@ class FreqaiDataDrawer:
|
||||
def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Saves all data associated with a model for a single sub-train time range
|
||||
:params:
|
||||
:model: User trained model which can be reused for inferencing to generate
|
||||
predictions
|
||||
:param model: User trained model which can be reused for inferencing to generate
|
||||
predictions
|
||||
"""
|
||||
|
||||
if not dk.data_path.is_dir():
|
||||
@ -581,8 +580,7 @@ class FreqaiDataDrawer:
|
||||
Append new candles to our stores historic data (in memory) so that
|
||||
we do not need to load candle history from disk and we dont need to
|
||||
pinging exchange multiple times for the same candle.
|
||||
:params:
|
||||
dataframe: DataFrame = strategy provided dataframe
|
||||
:param dataframe: DataFrame = strategy provided dataframe
|
||||
"""
|
||||
feat_params = self.freqai_info["feature_parameters"]
|
||||
with self.history_lock:
|
||||
@ -628,9 +626,8 @@ class FreqaiDataDrawer:
|
||||
"""
|
||||
Load pair histories for all whitelist and corr_pairlist pairs.
|
||||
Only called once upon startup of bot.
|
||||
:params:
|
||||
timerange: TimeRange = full timerange required to populate all indicators
|
||||
for training according to user defined train_period_days
|
||||
:param timerange: TimeRange = full timerange required to populate all indicators
|
||||
for training according to user defined train_period_days
|
||||
"""
|
||||
history_data = self.historic_data
|
||||
|
||||
@ -653,10 +650,9 @@ class FreqaiDataDrawer:
|
||||
"""
|
||||
Searches through our historic_data in memory and returns the dataframes relevant
|
||||
to the present pair.
|
||||
:params:
|
||||
timerange: TimeRange = full timerange required to populate all indicators
|
||||
for training according to user defined train_period_days
|
||||
metadata: dict = strategy furnished pair metadata
|
||||
:param timerange: TimeRange = full timerange required to populate all indicators
|
||||
for training according to user defined train_period_days
|
||||
:param metadata: dict = strategy furnished pair metadata
|
||||
"""
|
||||
with self.history_lock:
|
||||
corr_dataframes: Dict[Any, Any] = {}
|
||||
|
||||
@ -107,9 +107,8 @@ class FreqaiDataKitchen:
|
||||
) -> None:
|
||||
"""
|
||||
Set the paths to the data for the present coin/botloop
|
||||
:params:
|
||||
metadata: dict = strategy furnished pair metadata
|
||||
trained_timestamp: int = timestamp of most recent training
|
||||
:param metadata: dict = strategy furnished pair metadata
|
||||
:param trained_timestamp: int = timestamp of most recent training
|
||||
"""
|
||||
self.full_path = Path(
|
||||
self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
|
||||
@ -129,8 +128,8 @@ class FreqaiDataKitchen:
|
||||
Given the dataframe for the full history for training, split the data into
|
||||
training and test data according to user specified parameters in configuration
|
||||
file.
|
||||
:filtered_dataframe: cleaned dataframe ready to be split.
|
||||
:labels: cleaned labels ready to be split.
|
||||
:param filtered_dataframe: cleaned dataframe ready to be split.
|
||||
:param labels: cleaned labels ready to be split.
|
||||
"""
|
||||
feat_dict = self.freqai_config["feature_parameters"]
|
||||
|
||||
@ -189,13 +188,14 @@ class FreqaiDataKitchen:
|
||||
remove all NaNs. Any row with a NaN is removed from training dataset or replaced with
|
||||
0s in the prediction dataset. However, prediction dataset do_predict will reflect any
|
||||
row that had a NaN and will shield user from that prediction.
|
||||
:params:
|
||||
:unfiltered_df: the full dataframe for the present training period
|
||||
:training_feature_list: list, the training feature list constructed by
|
||||
self.build_feature_list() according to user specified parameters in the configuration file.
|
||||
:labels: the labels for the dataset
|
||||
:training_filter: boolean which lets the function know if it is training data or
|
||||
prediction data to be filtered.
|
||||
|
||||
:param unfiltered_df: the full dataframe for the present training period
|
||||
:param training_feature_list: list, the training feature list constructed by
|
||||
self.build_feature_list() according to user specified
|
||||
parameters in the configuration file.
|
||||
:param labels: the labels for the dataset
|
||||
:param training_filter: boolean which lets the function know if it is training data or
|
||||
prediction data to be filtered.
|
||||
:returns:
|
||||
:filtered_df: dataframe cleaned of NaNs and only containing the user
|
||||
requested feature set.
|
||||
@ -241,6 +241,7 @@ class FreqaiDataKitchen:
|
||||
self.data["filter_drop_index_training"] = drop_index
|
||||
|
||||
else:
|
||||
filtered_df = self.check_pred_labels(filtered_df)
|
||||
# we are backtesting so we need to preserve row number to send back to strategy,
|
||||
# so now we use do_predict to avoid any prediction based on a NaN
|
||||
drop_index = pd.isnull(filtered_df).any(axis=1)
|
||||
@ -285,8 +286,8 @@ class FreqaiDataKitchen:
|
||||
def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
|
||||
"""
|
||||
Normalize all data in the data_dictionary according to the training dataset
|
||||
:params:
|
||||
:data_dictionary: dictionary containing the cleaned and split training/test data/labels
|
||||
:param data_dictionary: dictionary containing the cleaned and
|
||||
split training/test data/labels
|
||||
:returns:
|
||||
:data_dictionary: updated dictionary with standardized values.
|
||||
"""
|
||||
@ -460,6 +461,24 @@ class FreqaiDataKitchen:
|
||||
|
||||
return df
|
||||
|
||||
def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Check that prediction feature labels match training feature labels.
|
||||
:params:
|
||||
:df_predictions: incoming predictions
|
||||
"""
|
||||
train_labels = self.data_dictionary["train_features"].columns
|
||||
pred_labels = df_predictions.columns
|
||||
num_diffs = len(pred_labels.difference(train_labels))
|
||||
if num_diffs != 0:
|
||||
df_predictions = df_predictions[train_labels]
|
||||
logger.warning(
|
||||
f"Removed {num_diffs} features from prediction features, "
|
||||
f"these were likely considered constant values during most recent training."
|
||||
)
|
||||
|
||||
return df_predictions
|
||||
|
||||
def principal_component_analysis(self) -> None:
|
||||
"""
|
||||
Performs Principal Component Analysis on the data for dimensionality reduction
|
||||
@ -516,8 +535,7 @@ class FreqaiDataKitchen:
|
||||
def pca_transform(self, filtered_dataframe: DataFrame) -> None:
|
||||
"""
|
||||
Use an existing pca transform to transform data into components
|
||||
:params:
|
||||
filtered_dataframe: DataFrame = the cleaned dataframe
|
||||
:param filtered_dataframe: DataFrame = the cleaned dataframe
|
||||
"""
|
||||
pca_components = self.pca.transform(filtered_dataframe)
|
||||
self.data_dictionary["prediction_features"] = pd.DataFrame(
|
||||
@ -561,8 +579,7 @@ class FreqaiDataKitchen:
|
||||
"""
|
||||
Build/inference a Support Vector Machine to detect outliers
|
||||
in training data and prediction
|
||||
:params:
|
||||
predict: bool = If true, inference an existing SVM model, else construct one
|
||||
:param predict: bool = If true, inference an existing SVM model, else construct one
|
||||
"""
|
||||
|
||||
if self.keras:
|
||||
@ -647,11 +664,11 @@ class FreqaiDataKitchen:
|
||||
Use DBSCAN to cluster training data and remove "noisy" data (read outliers).
|
||||
User controls this via the config param `DBSCAN_outlier_pct` which indicates the
|
||||
pct of training data that they want to be considered outliers.
|
||||
:params:
|
||||
predict: bool = If False (training), iterate to find the best hyper parameters to match
|
||||
user requested outlier percent target. If True (prediction), use the parameters
|
||||
determined from the previous training to estimate if the current prediction point
|
||||
is an outlier.
|
||||
:param predict: bool = If False (training), iterate to find the best hyper parameters
|
||||
to match user requested outlier percent target.
|
||||
If True (prediction), use the parameters determined from
|
||||
the previous training to estimate if the current prediction point
|
||||
is an outlier.
|
||||
"""
|
||||
|
||||
if predict:
|
||||
@ -1118,15 +1135,13 @@ class FreqaiDataKitchen:
|
||||
prediction_dataframe: DataFrame = pd.DataFrame(),
|
||||
) -> DataFrame:
|
||||
"""
|
||||
Use the user defined strategy for populating indicators during
|
||||
retrain
|
||||
:params:
|
||||
strategy: IStrategy = user defined strategy object
|
||||
corr_dataframes: dict = dict containing the informative pair dataframes
|
||||
(for user defined timeframes)
|
||||
base_dataframes: dict = dict containing the current pair dataframes
|
||||
(for user defined timeframes)
|
||||
metadata: dict = strategy furnished pair metadata
|
||||
Use the user defined strategy for populating indicators during retrain
|
||||
:param strategy: IStrategy = user defined strategy object
|
||||
:param corr_dataframes: dict = dict containing the informative pair dataframes
|
||||
(for user defined timeframes)
|
||||
:param base_dataframes: dict = dict containing the current pair dataframes
|
||||
(for user defined timeframes)
|
||||
:param metadata: dict = strategy furnished pair metadata
|
||||
:returns:
|
||||
dataframe: DataFrame = dataframe containing populated indicators
|
||||
"""
|
||||
|
||||
@ -196,16 +196,15 @@ class IFreqaiModel(ABC):
|
||||
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
|
||||
|
||||
dk = FreqaiDataKitchen(self.config, self.live, pair)
|
||||
dk.set_paths(pair, trained_timestamp)
|
||||
(
|
||||
retrain,
|
||||
new_trained_timerange,
|
||||
data_load_timerange,
|
||||
) = dk.check_if_new_training_required(trained_timestamp)
|
||||
dk.set_paths(pair, new_trained_timerange.stopts)
|
||||
|
||||
if retrain:
|
||||
self.train_timer('start')
|
||||
dk.set_paths(pair, new_trained_timerange.stopts)
|
||||
try:
|
||||
self.extract_data_and_train_model(
|
||||
new_trained_timerange, pair, strategy, dk, data_load_timerange
|
||||
@ -270,9 +269,7 @@ class IFreqaiModel(ABC):
|
||||
)
|
||||
|
||||
trained_timestamp_int = int(trained_timestamp.stopts)
|
||||
dk.data_path = Path(
|
||||
dk.full_path / f"sub-train-{pair.split('/')[0]}_{trained_timestamp_int}"
|
||||
)
|
||||
dk.set_paths(pair, trained_timestamp_int)
|
||||
|
||||
dk.set_new_model_names(pair, trained_timestamp)
|
||||
|
||||
@ -605,11 +602,11 @@ class IFreqaiModel(ABC):
|
||||
If the user reuses an identifier on a subsequent instance,
|
||||
this function will not be called. In that case, "real" predictions
|
||||
will be appended to the loaded set of historic predictions.
|
||||
:param: df: DataFrame = the dataframe containing the training feature data
|
||||
:param: model: Any = A model which was `fit` using a common library such as
|
||||
catboost or lightgbm
|
||||
:param: dk: FreqaiDataKitchen = object containing methods for data analysis
|
||||
:param: pair: str = current pair
|
||||
:param df: DataFrame = the dataframe containing the training feature data
|
||||
:param model: Any = A model which was `fit` using a common library such as
|
||||
catboost or lightgbm
|
||||
:param dk: FreqaiDataKitchen = object containing methods for data analysis
|
||||
:param pair: str = current pair
|
||||
"""
|
||||
|
||||
self.dd.historic_predictions[pair] = pred_df
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
from catboost import CatBoostClassifier, Pool
|
||||
@ -20,9 +21,8 @@ class CatboostClassifier(BaseClassifierModel):
|
||||
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:params:
|
||||
:data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
train_data = Pool(
|
||||
@ -32,8 +32,9 @@ class CatboostClassifier(BaseClassifierModel):
|
||||
)
|
||||
|
||||
cbr = CatBoostClassifier(
|
||||
allow_writing_files=False,
|
||||
allow_writing_files=True,
|
||||
loss_function='MultiClass',
|
||||
train_dir=Path(dk.data_path),
|
||||
**self.model_training_parameters,
|
||||
)
|
||||
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
from catboost import CatBoostRegressor, Pool
|
||||
@ -41,7 +42,8 @@ class CatboostRegressor(BaseRegressionModel):
|
||||
init_model = self.get_init_model(dk.pair)
|
||||
|
||||
model = CatBoostRegressor(
|
||||
allow_writing_files=False,
|
||||
allow_writing_files=True,
|
||||
train_dir=Path(dk.data_path),
|
||||
**self.model_training_parameters,
|
||||
)
|
||||
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
from catboost import CatBoostRegressor, Pool
|
||||
@ -26,7 +27,8 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
|
||||
"""
|
||||
|
||||
cbr = CatBoostRegressor(
|
||||
allow_writing_files=False,
|
||||
allow_writing_files=True,
|
||||
train_dir=Path(dk.data_path),
|
||||
**self.model_training_parameters,
|
||||
)
|
||||
|
||||
|
||||
@ -20,9 +20,8 @@ class LightGBMClassifier(BaseClassifierModel):
|
||||
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:params:
|
||||
:data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
|
||||
|
||||
@ -26,9 +26,8 @@ class XGBoostClassifier(BaseClassifierModel):
|
||||
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:params:
|
||||
:data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
X = data_dictionary["train_features"].to_numpy()
|
||||
@ -65,7 +64,7 @@ class XGBoostClassifier(BaseClassifierModel):
|
||||
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_df: Full dataframe for the current backtest period.
|
||||
:param unfiltered_df: Full dataframe for the current backtest period.
|
||||
:return:
|
||||
:pred_df: dataframe containing the predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
|
||||
@ -6,7 +6,7 @@ import logging
|
||||
import re
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Any, Iterator, List
|
||||
from typing import Any, Dict, Iterator, List, Mapping, Union
|
||||
from typing.io import IO
|
||||
from urllib.parse import urlparse
|
||||
|
||||
@ -186,7 +186,10 @@ def safe_value_fallback(obj: dict, key1: str, key2: str, default_value=None):
|
||||
return default_value
|
||||
|
||||
|
||||
def safe_value_fallback2(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
|
||||
dictMap = Union[Dict[str, Any], Mapping[str, Any]]
|
||||
|
||||
|
||||
def safe_value_fallback2(dict1: dictMap, dict2: dictMap, key1: str, key2: str, default_value=None):
|
||||
"""
|
||||
Search a value in dict1, return this if it's not None.
|
||||
Fall back to dict2 - return key2 from dict2 if it's not None.
|
||||
|
||||
@ -617,13 +617,16 @@ class Backtesting:
|
||||
exit_reason = row[EXIT_TAG_IDX]
|
||||
# Custom exit pricing only for exit-signals
|
||||
if order_type == 'limit':
|
||||
close_rate = strategy_safe_wrapper(self.strategy.custom_exit_price,
|
||||
default_retval=close_rate)(
|
||||
rate = strategy_safe_wrapper(self.strategy.custom_exit_price,
|
||||
default_retval=close_rate)(
|
||||
pair=trade.pair,
|
||||
trade=trade, # type: ignore[arg-type]
|
||||
current_time=exit_candle_time,
|
||||
proposed_rate=close_rate, current_profit=current_profit,
|
||||
exit_tag=exit_reason)
|
||||
if rate != close_rate:
|
||||
close_rate = price_to_precision(rate, trade.price_precision,
|
||||
self.precision_mode)
|
||||
# We can't place orders lower than current low.
|
||||
# freqtrade does not support this in live, and the order would fill immediately
|
||||
if trade.is_short:
|
||||
@ -660,7 +663,6 @@ class Backtesting:
|
||||
# amount = amount or trade.amount
|
||||
amount = amount_to_contract_precision(amount or trade.amount, trade.amount_precision,
|
||||
self.precision_mode, trade.contract_size)
|
||||
rate = price_to_precision(close_rate, trade.price_precision, self.precision_mode)
|
||||
order = Order(
|
||||
id=self.order_id_counter,
|
||||
ft_trade_id=trade.id,
|
||||
@ -674,12 +676,12 @@ class Backtesting:
|
||||
side=trade.exit_side,
|
||||
order_type=order_type,
|
||||
status="open",
|
||||
price=rate,
|
||||
average=rate,
|
||||
price=close_rate,
|
||||
average=close_rate,
|
||||
amount=amount,
|
||||
filled=0,
|
||||
remaining=amount,
|
||||
cost=amount * rate,
|
||||
cost=amount * close_rate,
|
||||
)
|
||||
trade.orders.append(order)
|
||||
return trade
|
||||
@ -726,18 +728,21 @@ class Backtesting:
|
||||
def get_valid_price_and_stake(
|
||||
self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
|
||||
direction: LongShort, current_time: datetime, entry_tag: Optional[str],
|
||||
trade: Optional[LocalTrade], order_type: str
|
||||
trade: Optional[LocalTrade], order_type: str, price_precision: Optional[float]
|
||||
) -> Tuple[float, float, float, float]:
|
||||
|
||||
if order_type == 'limit':
|
||||
propose_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
|
||||
default_retval=propose_rate)(
|
||||
new_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
|
||||
default_retval=propose_rate)(
|
||||
pair=pair, current_time=current_time,
|
||||
proposed_rate=propose_rate, entry_tag=entry_tag,
|
||||
side=direction,
|
||||
) # default value is the open rate
|
||||
# We can't place orders higher than current high (otherwise it'd be a stop limit entry)
|
||||
# which freqtrade does not support in live.
|
||||
if new_rate != propose_rate:
|
||||
propose_rate = price_to_precision(new_rate, price_precision,
|
||||
self.precision_mode)
|
||||
if direction == "short":
|
||||
propose_rate = max(propose_rate, row[LOW_IDX])
|
||||
else:
|
||||
@ -799,9 +804,11 @@ class Backtesting:
|
||||
pos_adjust = trade is not None and requested_rate is None
|
||||
|
||||
stake_amount_ = stake_amount or (trade.stake_amount if trade else 0.0)
|
||||
precision_price = self.exchange.get_precision_price(pair)
|
||||
|
||||
propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake(
|
||||
pair, row, row[OPEN_IDX], stake_amount_, direction, current_time, entry_tag, trade,
|
||||
order_type
|
||||
order_type, precision_price,
|
||||
)
|
||||
|
||||
# replace proposed rate if another rate was requested
|
||||
@ -817,8 +824,6 @@ class Backtesting:
|
||||
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
|
||||
self.order_id_counter += 1
|
||||
base_currency = self.exchange.get_pair_base_currency(pair)
|
||||
precision_price = self.exchange.get_precision_price(pair)
|
||||
propose_rate = price_to_precision(propose_rate, precision_price, self.precision_mode)
|
||||
amount_p = (stake_amount / propose_rate) * leverage
|
||||
|
||||
contract_size = self.exchange.get_contract_size(pair)
|
||||
|
||||
@ -12,7 +12,7 @@ import tabulate
|
||||
from colorama import Fore, Style
|
||||
from pandas import isna, json_normalize
|
||||
|
||||
from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES, Config
|
||||
from freqtrade.constants import FTHYPT_FILEVERSION, Config
|
||||
from freqtrade.enums import HyperoptState
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
|
||||
@ -50,9 +50,8 @@ class HyperoptTools():
|
||||
Get Strategy-location (filename) from strategy_name
|
||||
"""
|
||||
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
||||
directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
|
||||
strategy_objs = StrategyResolver.search_all_objects(
|
||||
directory, False, config.get('recursive_strategy_search', False))
|
||||
config, False, config.get('recursive_strategy_search', False))
|
||||
strategies = [s for s in strategy_objs if s['name'] == strategy_name]
|
||||
if strategies:
|
||||
strategy = strategies[0]
|
||||
|
||||
@ -10,6 +10,7 @@ from pandas import DataFrame
|
||||
|
||||
from freqtrade.constants import Config, ListPairsWithTimeframes
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.misc import plural
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
from freqtrade.util import PeriodicCache
|
||||
@ -67,10 +68,10 @@ class AgeFilter(IPairList):
|
||||
f"{self._max_days_listed} {plural(self._max_days_listed, 'day')}"
|
||||
) if self._max_days_listed else '')
|
||||
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new allowlist
|
||||
"""
|
||||
needed_pairs: ListPairsWithTimeframes = [
|
||||
|
||||
@ -4,11 +4,12 @@ PairList Handler base class
|
||||
import logging
|
||||
from abc import ABC, abstractmethod, abstractproperty
|
||||
from copy import deepcopy
|
||||
from typing import Any, Dict, List
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange import Exchange, market_is_active
|
||||
from freqtrade.exchange.types import Ticker, Tickers
|
||||
from freqtrade.mixins import LoggingMixin
|
||||
|
||||
|
||||
@ -61,7 +62,7 @@ class IPairList(LoggingMixin, ABC):
|
||||
-> Please overwrite in subclasses
|
||||
"""
|
||||
|
||||
def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
|
||||
def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
|
||||
"""
|
||||
Check one pair against Pairlist Handler's specific conditions.
|
||||
|
||||
@ -69,12 +70,12 @@ class IPairList(LoggingMixin, ABC):
|
||||
filter_pairlist() method.
|
||||
|
||||
:param pair: Pair that's currently validated
|
||||
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
|
||||
:param ticker: ticker dict as returned from ccxt.fetch_ticker
|
||||
:return: True if the pair can stay, false if it should be removed
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
def gen_pairlist(self, tickers: Dict) -> List[str]:
|
||||
def gen_pairlist(self, tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Generate the pairlist.
|
||||
|
||||
@ -85,13 +86,13 @@ class IPairList(LoggingMixin, ABC):
|
||||
it will raise the exception if a Pairlist Handler is used at the first
|
||||
position in the chain.
|
||||
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: List of pairs
|
||||
"""
|
||||
raise OperationalException("This Pairlist Handler should not be used "
|
||||
"at the first position in the list of Pairlist Handlers.")
|
||||
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Filters and sorts pairlist and returns the whitelist again.
|
||||
|
||||
@ -103,14 +104,14 @@ class IPairList(LoggingMixin, ABC):
|
||||
own filtration.
|
||||
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new whitelist
|
||||
"""
|
||||
if self._enabled:
|
||||
# Copy list since we're modifying this list
|
||||
for p in deepcopy(pairlist):
|
||||
# Filter out assets
|
||||
if not self._validate_pair(p, tickers[p] if p in tickers else {}):
|
||||
if not self._validate_pair(p, tickers[p] if p in tickers else None):
|
||||
pairlist.remove(p)
|
||||
|
||||
return pairlist
|
||||
|
||||
@ -6,6 +6,7 @@ from typing import Any, Dict, List
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
|
||||
@ -42,12 +43,12 @@ class OffsetFilter(IPairList):
|
||||
return f"{self.name} - Taking {self._number_pairs} Pairs, starting from {self._offset}."
|
||||
return f"{self.name} - Offsetting pairs by {self._offset}."
|
||||
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Filters and sorts pairlist and returns the whitelist again.
|
||||
Called on each bot iteration - please use internal caching if necessary
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new whitelist
|
||||
"""
|
||||
if self._offset > len(pairlist):
|
||||
|
||||
@ -7,6 +7,7 @@ from typing import Any, Dict, List
|
||||
import pandas as pd
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
@ -39,12 +40,12 @@ class PerformanceFilter(IPairList):
|
||||
"""
|
||||
return f"{self.name} - Sorting pairs by performance."
|
||||
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Filters and sorts pairlist and returns the allowlist again.
|
||||
Called on each bot iteration - please use internal caching if necessary
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new allowlist
|
||||
"""
|
||||
# Get the trading performance for pairs from database
|
||||
|
||||
@ -2,10 +2,11 @@
|
||||
Precision pair list filter
|
||||
"""
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Ticker
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
|
||||
@ -44,15 +45,15 @@ class PrecisionFilter(IPairList):
|
||||
"""
|
||||
return f"{self.name} - Filtering untradable pairs."
|
||||
|
||||
def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
|
||||
def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
|
||||
"""
|
||||
Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
|
||||
low value pairs.
|
||||
:param pair: Pair that's currently validated
|
||||
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
|
||||
:param ticker: ticker dict as returned from ccxt.fetch_ticker
|
||||
:return: True if the pair can stay, false if it should be removed
|
||||
"""
|
||||
if ticker.get('last', None) is None:
|
||||
if not ticker or ticker.get('last', None) is None:
|
||||
self.log_once(f"Removed {pair} from whitelist, because "
|
||||
"ticker['last'] is empty (Usually no trade in the last 24h).",
|
||||
logger.info)
|
||||
|
||||
@ -2,10 +2,11 @@
|
||||
Price pair list filter
|
||||
"""
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Ticker
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
|
||||
@ -64,14 +65,16 @@ class PriceFilter(IPairList):
|
||||
|
||||
return f"{self.name} - No price filters configured."
|
||||
|
||||
def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
|
||||
def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
|
||||
"""
|
||||
Check if if one price-step (pip) is > than a certain barrier.
|
||||
:param pair: Pair that's currently validated
|
||||
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
|
||||
:param ticker: ticker dict as returned from ccxt.fetch_ticker
|
||||
:return: True if the pair can stay, false if it should be removed
|
||||
"""
|
||||
if ticker.get('last', None) is None or ticker.get('last') == 0:
|
||||
if ticker and 'last' in ticker and ticker['last'] is not None and ticker.get('last') != 0:
|
||||
price: float = ticker['last']
|
||||
else:
|
||||
self.log_once(f"Removed {pair} from whitelist, because "
|
||||
"ticker['last'] is empty (Usually no trade in the last 24h).",
|
||||
logger.info)
|
||||
@ -79,8 +82,8 @@ class PriceFilter(IPairList):
|
||||
|
||||
# Perform low_price_ratio check.
|
||||
if self._low_price_ratio != 0:
|
||||
compare = self._exchange.price_get_one_pip(pair, ticker['last'])
|
||||
changeperc = compare / ticker['last']
|
||||
compare = self._exchange.price_get_one_pip(pair, price)
|
||||
changeperc = compare / price
|
||||
if changeperc > self._low_price_ratio:
|
||||
self.log_once(f"Removed {pair} from whitelist, "
|
||||
f"because 1 unit is {changeperc:.3%}", logger.info)
|
||||
@ -88,7 +91,6 @@ class PriceFilter(IPairList):
|
||||
|
||||
# Perform low_amount check
|
||||
if self._max_value != 0:
|
||||
price = ticker['last']
|
||||
market = self._exchange.markets[pair]
|
||||
limits = market['limits']
|
||||
if (limits['amount']['min'] is not None):
|
||||
@ -113,14 +115,14 @@ class PriceFilter(IPairList):
|
||||
|
||||
# Perform min_price check.
|
||||
if self._min_price != 0:
|
||||
if ticker['last'] < self._min_price:
|
||||
if price < self._min_price:
|
||||
self.log_once(f"Removed {pair} from whitelist, "
|
||||
f"because last price < {self._min_price:.8f}", logger.info)
|
||||
return False
|
||||
|
||||
# Perform max_price check.
|
||||
if self._max_price != 0:
|
||||
if ticker['last'] > self._max_price:
|
||||
if price > self._max_price:
|
||||
self.log_once(f"Removed {pair} from whitelist, "
|
||||
f"because last price > {self._max_price:.8f}", logger.info)
|
||||
return False
|
||||
|
||||
@ -7,6 +7,7 @@ import logging
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
|
||||
@ -68,10 +69,10 @@ class ProducerPairList(IPairList):
|
||||
|
||||
return pairs
|
||||
|
||||
def gen_pairlist(self, tickers: Dict) -> List[str]:
|
||||
def gen_pairlist(self, tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Generate the pairlist
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: List of pairs
|
||||
"""
|
||||
pairs = self._filter_pairlist(None)
|
||||
@ -79,12 +80,12 @@ class ProducerPairList(IPairList):
|
||||
pairs = self._whitelist_for_active_markets(self.verify_whitelist(pairs, logger.info))
|
||||
return pairs
|
||||
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Filters and sorts pairlist and returns the whitelist again.
|
||||
Called on each bot iteration - please use internal caching if necessary
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new whitelist
|
||||
"""
|
||||
return self._filter_pairlist(pairlist)
|
||||
|
||||
@ -7,6 +7,7 @@ from typing import Any, Dict, List
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.enums import RunMode
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
|
||||
@ -47,12 +48,12 @@ class ShuffleFilter(IPairList):
|
||||
return (f"{self.name} - Shuffling pairs" +
|
||||
(f", seed = {self._seed}." if self._seed is not None else "."))
|
||||
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Filters and sorts pairlist and returns the whitelist again.
|
||||
Called on each bot iteration - please use internal caching if necessary
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new whitelist
|
||||
"""
|
||||
# Shuffle is done inplace
|
||||
|
||||
@ -2,10 +2,10 @@
|
||||
Spread pair list filter
|
||||
"""
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Ticker
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
|
||||
@ -22,12 +22,6 @@ class SpreadFilter(IPairList):
|
||||
self._max_spread_ratio = pairlistconfig.get('max_spread_ratio', 0.005)
|
||||
self._enabled = self._max_spread_ratio != 0
|
||||
|
||||
if not self._exchange.exchange_has('fetchTickers'):
|
||||
raise OperationalException(
|
||||
'Exchange does not support fetchTickers, therefore SpreadFilter cannot be used.'
|
||||
'Please edit your config and restart the bot.'
|
||||
)
|
||||
|
||||
@property
|
||||
def needstickers(self) -> bool:
|
||||
"""
|
||||
@ -44,14 +38,14 @@ class SpreadFilter(IPairList):
|
||||
return (f"{self.name} - Filtering pairs with ask/bid diff above "
|
||||
f"{self._max_spread_ratio:.2%}.")
|
||||
|
||||
def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
|
||||
def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
|
||||
"""
|
||||
Validate spread for the ticker
|
||||
:param pair: Pair that's currently validated
|
||||
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
|
||||
:param ticker: ticker dict as returned from ccxt.fetch_ticker
|
||||
:return: True if the pair can stay, false if it should be removed
|
||||
"""
|
||||
if 'bid' in ticker and 'ask' in ticker and ticker['ask'] and ticker['bid']:
|
||||
if ticker and 'bid' in ticker and 'ask' in ticker and ticker['ask'] and ticker['bid']:
|
||||
spread = 1 - ticker['bid'] / ticker['ask']
|
||||
if spread > self._max_spread_ratio:
|
||||
self.log_once(f"Removed {pair} from whitelist, because spread "
|
||||
|
||||
@ -8,6 +8,7 @@ from copy import deepcopy
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
|
||||
@ -39,10 +40,10 @@ class StaticPairList(IPairList):
|
||||
"""
|
||||
return f"{self.name}"
|
||||
|
||||
def gen_pairlist(self, tickers: Dict) -> List[str]:
|
||||
def gen_pairlist(self, tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Generate the pairlist
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: List of pairs
|
||||
"""
|
||||
if self._allow_inactive:
|
||||
@ -53,12 +54,12 @@ class StaticPairList(IPairList):
|
||||
return self._whitelist_for_active_markets(
|
||||
self.verify_whitelist(self._config['exchange']['pair_whitelist'], logger.info))
|
||||
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Filters and sorts pairlist and returns the whitelist again.
|
||||
Called on each bot iteration - please use internal caching if necessary
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new whitelist
|
||||
"""
|
||||
pairlist_ = deepcopy(pairlist)
|
||||
|
||||
@ -13,6 +13,7 @@ from pandas import DataFrame
|
||||
|
||||
from freqtrade.constants import Config, ListPairsWithTimeframes
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.misc import plural
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
@ -62,11 +63,11 @@ class VolatilityFilter(IPairList):
|
||||
f"{self._min_volatility}-{self._max_volatility} "
|
||||
f" the last {self._days} {plural(self._days, 'day')}.")
|
||||
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Validate trading range
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new allowlist
|
||||
"""
|
||||
needed_pairs: ListPairsWithTimeframes = [
|
||||
|
||||
@ -5,13 +5,14 @@ Provides dynamic pair list based on trade volumes
|
||||
"""
|
||||
import logging
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from typing import Any, Dict, List
|
||||
from typing import Any, Dict, List, Literal
|
||||
|
||||
from cachetools import TTLCache
|
||||
|
||||
from freqtrade.constants import Config, ListPairsWithTimeframes
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.misc import format_ms_time
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
@ -36,7 +37,7 @@ class VolumePairList(IPairList):
|
||||
|
||||
self._stake_currency = config['stake_currency']
|
||||
self._number_pairs = self._pairlistconfig['number_assets']
|
||||
self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
|
||||
self._sort_key: Literal['quoteVolume'] = self._pairlistconfig.get('sort_key', 'quoteVolume')
|
||||
self._min_value = self._pairlistconfig.get('min_value', 0)
|
||||
self._refresh_period = self._pairlistconfig.get('refresh_period', 1800)
|
||||
self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
|
||||
@ -110,10 +111,10 @@ class VolumePairList(IPairList):
|
||||
"""
|
||||
return f"{self.name} - top {self._pairlistconfig['number_assets']} volume pairs."
|
||||
|
||||
def gen_pairlist(self, tickers: Dict) -> List[str]:
|
||||
def gen_pairlist(self, tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Generate the pairlist
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: List of pairs
|
||||
"""
|
||||
# Generate dynamic whitelist
|
||||
@ -150,7 +151,7 @@ class VolumePairList(IPairList):
|
||||
Filters and sorts pairlist and returns the whitelist again.
|
||||
Called on each bot iteration - please use internal caching if necessary
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new whitelist
|
||||
"""
|
||||
if self._use_range:
|
||||
|
||||
@ -12,7 +12,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
|
||||
:param wildcardpl: List of Pairlists, which may contain regex
|
||||
:param available_pairs: List of all available pairs (`exchange.get_markets().keys()`)
|
||||
:param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes
|
||||
:return expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
|
||||
:return: expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
|
||||
:raises: ValueError if a wildcard is invalid (like '*/BTC' - which should be `.*/BTC`)
|
||||
"""
|
||||
result = []
|
||||
|
||||
@ -11,6 +11,7 @@ from pandas import DataFrame
|
||||
|
||||
from freqtrade.constants import Config, ListPairsWithTimeframes
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.misc import plural
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
|
||||
@ -60,11 +61,11 @@ class RangeStabilityFilter(IPairList):
|
||||
f"{self._min_rate_of_change}{max_rate_desc} over the "
|
||||
f"last {plural(self._days, 'day')}.")
|
||||
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
|
||||
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
|
||||
"""
|
||||
Validate trading range
|
||||
:param pairlist: pairlist to filter or sort
|
||||
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
|
||||
:param tickers: Tickers (from exchange.get_tickers). May be cached.
|
||||
:return: new allowlist
|
||||
"""
|
||||
needed_pairs: ListPairsWithTimeframes = [
|
||||
|
||||
@ -11,6 +11,7 @@ from freqtrade.constants import Config, ListPairsWithTimeframes
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.enums import CandleType
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Tickers
|
||||
from freqtrade.mixins import LoggingMixin
|
||||
from freqtrade.plugins.pairlist.IPairList import IPairList
|
||||
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
|
||||
@ -45,6 +46,15 @@ class PairListManager(LoggingMixin):
|
||||
if not self._pairlist_handlers:
|
||||
raise OperationalException("No Pairlist Handlers defined")
|
||||
|
||||
if self._tickers_needed and not self._exchange.exchange_has('fetchTickers'):
|
||||
invalid = ". ".join([p.name for p in self._pairlist_handlers if p.needstickers])
|
||||
|
||||
raise OperationalException(
|
||||
"Exchange does not support fetchTickers, therefore the following pairlists "
|
||||
"cannot be used. Please edit your config and restart the bot.\n"
|
||||
f"{invalid}."
|
||||
)
|
||||
|
||||
refresh_period = config.get('pairlist_refresh_period', 3600)
|
||||
LoggingMixin.__init__(self, logger, refresh_period)
|
||||
|
||||
@ -76,7 +86,7 @@ class PairListManager(LoggingMixin):
|
||||
return [{p.name: p.short_desc()} for p in self._pairlist_handlers]
|
||||
|
||||
@cached(TTLCache(maxsize=1, ttl=1800))
|
||||
def _get_cached_tickers(self):
|
||||
def _get_cached_tickers(self) -> Tickers:
|
||||
return self._exchange.get_tickers()
|
||||
|
||||
def refresh_pairlist(self) -> None:
|
||||
|
||||
@ -183,9 +183,35 @@ class IResolver:
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def search_all_objects(cls, directory: Path, enum_failed: bool,
|
||||
def search_all_objects(cls, config: Config, enum_failed: bool,
|
||||
recursive: bool = False) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Searches for valid objects
|
||||
:param config: Config object
|
||||
:param enum_failed: If True, will return None for modules which fail.
|
||||
Otherwise, failing modules are skipped.
|
||||
:param recursive: Recursively walk directory tree searching for strategies
|
||||
:return: List of dicts containing 'name', 'class' and 'location' entries
|
||||
"""
|
||||
result = []
|
||||
|
||||
abs_paths = cls.build_search_paths(config, user_subdir=cls.user_subdir)
|
||||
for path in abs_paths:
|
||||
result.extend(cls._search_all_objects(path, enum_failed, recursive))
|
||||
return result
|
||||
|
||||
@classmethod
|
||||
def _build_rel_location(cls, directory: Path, entry: Path) -> str:
|
||||
|
||||
builtin = cls.initial_search_path == directory
|
||||
return f"<builtin>/{entry.relative_to(directory)}" if builtin else str(
|
||||
entry.relative_to(directory))
|
||||
|
||||
@classmethod
|
||||
def _search_all_objects(
|
||||
cls, directory: Path, enum_failed: bool, recursive: bool = False,
|
||||
basedir: Optional[Path] = None) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Searches a directory for valid objects
|
||||
:param directory: Path to search
|
||||
:param enum_failed: If True, will return None for modules which fail.
|
||||
@ -204,7 +230,8 @@ class IResolver:
|
||||
and not entry.name.startswith('__')
|
||||
and not entry.name.startswith('.')
|
||||
):
|
||||
objects.extend(cls.search_all_objects(entry, enum_failed, recursive=recursive))
|
||||
objects.extend(cls._search_all_objects(
|
||||
entry, enum_failed, recursive, basedir or directory))
|
||||
# Only consider python files
|
||||
if entry.suffix != '.py':
|
||||
logger.debug('Ignoring %s', entry)
|
||||
@ -217,5 +244,6 @@ class IResolver:
|
||||
{'name': obj[0].__name__ if obj is not None else '',
|
||||
'class': obj[0] if obj is not None else None,
|
||||
'location': entry,
|
||||
'location_rel': cls._build_rel_location(basedir or directory, entry),
|
||||
})
|
||||
return objects
|
||||
|
||||
@ -268,6 +268,14 @@ class StrategyResolver(IResolver):
|
||||
"or contains Python code errors."
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def build_search_paths(cls, config: Config, user_subdir: Optional[str] = None,
|
||||
extra_dirs: List[str] = []) -> List[Path]:
|
||||
|
||||
if 'strategy_path' in config and config['strategy_path'] not in extra_dirs:
|
||||
extra_dirs = [config['strategy_path']] + extra_dirs
|
||||
return super().build_search_paths(config, user_subdir, extra_dirs)
|
||||
|
||||
|
||||
def warn_deprecated_setting(strategy: IStrategy, old: str, new: str, error=False):
|
||||
if hasattr(strategy, old):
|
||||
|
||||
@ -1,13 +1,11 @@
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
from typing import List, Optional
|
||||
|
||||
from fastapi import APIRouter, Depends, Query
|
||||
from fastapi.exceptions import HTTPException
|
||||
|
||||
from freqtrade import __version__
|
||||
from freqtrade.constants import USERPATH_STRATEGIES
|
||||
from freqtrade.data.history import get_datahandler
|
||||
from freqtrade.enums import CandleType, TradingMode
|
||||
from freqtrade.exceptions import OperationalException
|
||||
@ -253,11 +251,9 @@ def plot_config(rpc: RPC = Depends(get_rpc)):
|
||||
|
||||
@router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])
|
||||
def list_strategies(config=Depends(get_config)):
|
||||
directory = Path(config.get(
|
||||
'strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
|
||||
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
||||
strategies = StrategyResolver.search_all_objects(
|
||||
directory, False, config.get('recursive_strategy_search', False))
|
||||
config, False, config.get('recursive_strategy_search', False))
|
||||
strategies = sorted(strategies, key=lambda x: x['name'])
|
||||
|
||||
return {'strategies': [x['name'] for x in strategies]}
|
||||
|
||||
@ -4,6 +4,7 @@ from typing import Any, Dict
|
||||
from fastapi import APIRouter, Depends, WebSocketDisconnect
|
||||
from fastapi.websockets import WebSocket, WebSocketState
|
||||
from pydantic import ValidationError
|
||||
from websockets.exceptions import WebSocketException
|
||||
|
||||
from freqtrade.enums import RPCMessageType, RPCRequestType
|
||||
from freqtrade.rpc.api_server.api_auth import validate_ws_token
|
||||
@ -102,7 +103,6 @@ async def message_endpoint(
|
||||
"""
|
||||
try:
|
||||
channel = await channel_manager.on_connect(ws)
|
||||
|
||||
if await is_websocket_alive(ws):
|
||||
|
||||
logger.info(f"Consumer connected - {channel}")
|
||||
@ -115,26 +115,31 @@ async def message_endpoint(
|
||||
# Process the request here
|
||||
await _process_consumer_request(request, channel, rpc)
|
||||
|
||||
except WebSocketDisconnect:
|
||||
except (WebSocketDisconnect, WebSocketException):
|
||||
# Handle client disconnects
|
||||
logger.info(f"Consumer disconnected - {channel}")
|
||||
await channel_manager.on_disconnect(ws)
|
||||
except Exception as e:
|
||||
logger.info(f"Consumer connection failed - {channel}")
|
||||
logger.exception(e)
|
||||
except RuntimeError:
|
||||
# Handle cases like -
|
||||
# RuntimeError('Cannot call "send" once a closed message has been sent')
|
||||
pass
|
||||
except Exception as e:
|
||||
logger.info(f"Consumer connection failed - {channel}: {e}")
|
||||
logger.debug(e, exc_info=e)
|
||||
finally:
|
||||
await channel_manager.on_disconnect(ws)
|
||||
|
||||
else:
|
||||
if channel:
|
||||
await channel_manager.on_disconnect(ws)
|
||||
await ws.close()
|
||||
|
||||
except RuntimeError:
|
||||
# WebSocket was closed
|
||||
await channel_manager.on_disconnect(ws)
|
||||
|
||||
# Do nothing
|
||||
pass
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to serve - {ws.client}")
|
||||
# Log tracebacks to keep track of what errors are happening
|
||||
logger.exception(e)
|
||||
finally:
|
||||
await channel_manager.on_disconnect(ws)
|
||||
|
||||
@ -198,10 +198,6 @@ class ApiServer(RPCHandler):
|
||||
logger.debug(f"Found message of type: {message.get('type')}")
|
||||
# Broadcast it
|
||||
await self._ws_channel_manager.broadcast(message)
|
||||
# Limit messages per sec.
|
||||
# Could cause problems with queue size if too low, and
|
||||
# problems with network traffik if too high.
|
||||
await asyncio.sleep(0.001)
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
@ -245,6 +241,7 @@ class ApiServer(RPCHandler):
|
||||
use_colors=False,
|
||||
log_config=None,
|
||||
access_log=True if verbosity != 'error' else False,
|
||||
ws_ping_interval=None # We do this explicitly ourselves
|
||||
)
|
||||
try:
|
||||
self._server = UvicornServer(uvconfig)
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
import asyncio
|
||||
import logging
|
||||
from threading import RLock
|
||||
from typing import List, Optional, Type
|
||||
from typing import Any, Dict, List, Optional, Type
|
||||
from uuid import uuid4
|
||||
|
||||
from fastapi import WebSocket as FastAPIWebSocket
|
||||
@ -34,6 +35,8 @@ class WebSocketChannel:
|
||||
self._serializer_cls = serializer_cls
|
||||
|
||||
self._subscriptions: List[str] = []
|
||||
self.queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue(maxsize=32)
|
||||
self._relay_task = asyncio.create_task(self.relay())
|
||||
|
||||
# Internal event to signify a closed websocket
|
||||
self._closed = False
|
||||
@ -48,12 +51,18 @@ class WebSocketChannel:
|
||||
def remote_addr(self):
|
||||
return self._websocket.remote_addr
|
||||
|
||||
async def send(self, data):
|
||||
async def _send(self, data):
|
||||
"""
|
||||
Send data on the wrapped websocket
|
||||
"""
|
||||
await self._wrapped_ws.send(data)
|
||||
|
||||
async def send(self, data):
|
||||
"""
|
||||
Add the data to the queue to be sent
|
||||
"""
|
||||
self.queue.put_nowait(data)
|
||||
|
||||
async def recv(self):
|
||||
"""
|
||||
Receive data on the wrapped websocket
|
||||
@ -72,6 +81,7 @@ class WebSocketChannel:
|
||||
"""
|
||||
|
||||
self._closed = True
|
||||
self._relay_task.cancel()
|
||||
|
||||
def is_closed(self) -> bool:
|
||||
"""
|
||||
@ -95,6 +105,26 @@ class WebSocketChannel:
|
||||
"""
|
||||
return message_type in self._subscriptions
|
||||
|
||||
async def relay(self):
|
||||
"""
|
||||
Relay messages from the channel's queue and send them out. This is started
|
||||
as a task.
|
||||
"""
|
||||
while True:
|
||||
message = await self.queue.get()
|
||||
try:
|
||||
await self._send(message)
|
||||
self.queue.task_done()
|
||||
|
||||
# Limit messages per sec.
|
||||
# Could cause problems with queue size if too low, and
|
||||
# problems with network traffik if too high.
|
||||
# 0.001 = 1000/s
|
||||
await asyncio.sleep(0.001)
|
||||
except RuntimeError:
|
||||
# The connection was closed, just exit the task
|
||||
return
|
||||
|
||||
|
||||
class ChannelManager:
|
||||
def __init__(self):
|
||||
@ -155,12 +185,12 @@ class ChannelManager:
|
||||
with self._lock:
|
||||
message_type = data.get('type')
|
||||
for websocket, channel in self.channels.copy().items():
|
||||
try:
|
||||
if channel.subscribed_to(message_type):
|
||||
if channel.subscribed_to(message_type):
|
||||
if not channel.queue.full():
|
||||
await channel.send(data)
|
||||
except RuntimeError:
|
||||
# Handle cannot send after close cases
|
||||
await self.on_disconnect(websocket)
|
||||
else:
|
||||
logger.info(f"Channel {channel} is too far behind, disconnecting")
|
||||
await self.on_disconnect(websocket)
|
||||
|
||||
async def send_direct(self, channel, data):
|
||||
"""
|
||||
|
||||
@ -11,13 +11,12 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
class Discord(Webhook):
|
||||
def __init__(self, rpc: 'RPC', config: Config):
|
||||
# super().__init__(rpc, config)
|
||||
self._config = config
|
||||
self.rpc = rpc
|
||||
self.config = config
|
||||
self.strategy = config.get('strategy', '')
|
||||
self.timeframe = config.get('timeframe', '')
|
||||
|
||||
self._url = self.config['discord']['webhook_url']
|
||||
self._url = config['discord']['webhook_url']
|
||||
self._format = 'json'
|
||||
self._retries = 1
|
||||
self._retry_delay = 0.1
|
||||
@ -31,19 +30,21 @@ class Discord(Webhook):
|
||||
|
||||
def send_msg(self, msg) -> None:
|
||||
|
||||
if msg['type'].value in self.config['discord']:
|
||||
if msg['type'].value in self._config['discord']:
|
||||
logger.info(f"Sending discord message: {msg}")
|
||||
|
||||
msg['strategy'] = self.strategy
|
||||
msg['timeframe'] = self.timeframe
|
||||
fields = self.config['discord'].get(msg['type'].value)
|
||||
fields = self._config['discord'].get(msg['type'].value)
|
||||
color = 0x0000FF
|
||||
if msg['type'] in (RPCMessageType.EXIT, RPCMessageType.EXIT_FILL):
|
||||
profit_ratio = msg.get('profit_ratio')
|
||||
color = (0x00FF00 if profit_ratio > 0 else 0xFF0000)
|
||||
|
||||
title = msg['type'].value
|
||||
if 'pair' in msg:
|
||||
title = f"Trade: {msg['pair']} {msg['type'].value}"
|
||||
embeds = [{
|
||||
'title': f"Trade: {msg['pair']} {msg['type'].value}",
|
||||
'title': title,
|
||||
'color': color,
|
||||
'fields': [],
|
||||
|
||||
@ -51,7 +52,7 @@ class Discord(Webhook):
|
||||
for f in fields:
|
||||
for k, v in f.items():
|
||||
v = v.format(**msg)
|
||||
embeds[0]['fields'].append( # type: ignore
|
||||
embeds[0]['fields'].append(
|
||||
{'name': k, 'value': v, 'inline': True})
|
||||
|
||||
# Send the message to discord channel
|
||||
|
||||
@ -62,7 +62,7 @@ class ExternalMessageConsumer:
|
||||
self.enabled = self._emc_config.get('enabled', False)
|
||||
self.producers: List[Producer] = self._emc_config.get('producers', [])
|
||||
|
||||
self.wait_timeout = self._emc_config.get('wait_timeout', 300) # in seconds
|
||||
self.wait_timeout = self._emc_config.get('wait_timeout', 30) # in seconds
|
||||
self.ping_timeout = self._emc_config.get('ping_timeout', 10) # in seconds
|
||||
self.sleep_time = self._emc_config.get('sleep_time', 10) # in seconds
|
||||
|
||||
@ -174,6 +174,7 @@ class ExternalMessageConsumer:
|
||||
:param producer: Dictionary containing producer info
|
||||
:param lock: An asyncio Lock
|
||||
"""
|
||||
channel = None
|
||||
while self._running:
|
||||
try:
|
||||
host, port = producer['host'], producer['port']
|
||||
@ -182,7 +183,11 @@ class ExternalMessageConsumer:
|
||||
ws_url = f"ws://{host}:{port}/api/v1/message/ws?token={token}"
|
||||
|
||||
# This will raise InvalidURI if the url is bad
|
||||
async with websockets.connect(ws_url, max_size=self.message_size_limit) as ws:
|
||||
async with websockets.connect(
|
||||
ws_url,
|
||||
max_size=self.message_size_limit,
|
||||
ping_interval=None
|
||||
) as ws:
|
||||
channel = WebSocketChannel(ws, channel_id=name)
|
||||
|
||||
logger.info(f"Producer connection success - {channel}")
|
||||
@ -224,6 +229,10 @@ class ExternalMessageConsumer:
|
||||
logger.exception(e)
|
||||
continue
|
||||
|
||||
finally:
|
||||
if channel:
|
||||
await channel.close()
|
||||
|
||||
async def _receive_messages(
|
||||
self,
|
||||
channel: WebSocketChannel,
|
||||
|
||||
@ -88,10 +88,13 @@ class RPCManager:
|
||||
"""
|
||||
while queue:
|
||||
msg = queue.popleft()
|
||||
self.send_msg({
|
||||
'type': RPCMessageType.STRATEGY_MSG,
|
||||
'msg': msg,
|
||||
})
|
||||
logger.info('Sending rpc strategy_msg: %s', msg)
|
||||
for mod in self.registered_modules:
|
||||
if mod._config.get(mod.name, {}).get('allow_custom_messages', False):
|
||||
mod.send_msg({
|
||||
'type': RPCMessageType.STRATEGY_MSG,
|
||||
'msg': msg,
|
||||
})
|
||||
|
||||
def startup_messages(self, config: Config, pairlist, protections) -> None:
|
||||
if config['dry_run']:
|
||||
|
||||
@ -3,7 +3,7 @@ This module manages webhook communication
|
||||
"""
|
||||
import logging
|
||||
import time
|
||||
from typing import Any, Dict
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from requests import RequestException, post
|
||||
|
||||
@ -41,36 +41,44 @@ class Webhook(RPCHandler):
|
||||
"""
|
||||
pass
|
||||
|
||||
def _get_value_dict(self, msg: Dict[str, Any]) -> Optional[Dict[str, Any]]:
|
||||
whconfig = self._config['webhook']
|
||||
# Deprecated 2022.10 - only keep generic method.
|
||||
if msg['type'] in [RPCMessageType.ENTRY]:
|
||||
valuedict = whconfig.get('webhookentry')
|
||||
elif msg['type'] in [RPCMessageType.ENTRY_CANCEL]:
|
||||
valuedict = whconfig.get('webhookentrycancel')
|
||||
elif msg['type'] in [RPCMessageType.ENTRY_FILL]:
|
||||
valuedict = whconfig.get('webhookentryfill')
|
||||
elif msg['type'] == RPCMessageType.EXIT:
|
||||
valuedict = whconfig.get('webhookexit')
|
||||
elif msg['type'] == RPCMessageType.EXIT_FILL:
|
||||
valuedict = whconfig.get('webhookexitfill')
|
||||
elif msg['type'] == RPCMessageType.EXIT_CANCEL:
|
||||
valuedict = whconfig.get('webhookexitcancel')
|
||||
elif msg['type'] in (RPCMessageType.STATUS,
|
||||
RPCMessageType.STARTUP,
|
||||
RPCMessageType.WARNING):
|
||||
valuedict = whconfig.get('webhookstatus')
|
||||
elif msg['type'].value in whconfig:
|
||||
# Allow all types ...
|
||||
valuedict = whconfig.get(msg['type'].value)
|
||||
elif msg['type'] in (
|
||||
RPCMessageType.PROTECTION_TRIGGER,
|
||||
RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
|
||||
RPCMessageType.WHITELIST,
|
||||
RPCMessageType.ANALYZED_DF,
|
||||
RPCMessageType.STRATEGY_MSG):
|
||||
# Don't fail for non-implemented types
|
||||
return None
|
||||
return valuedict
|
||||
|
||||
def send_msg(self, msg: Dict[str, Any]) -> None:
|
||||
""" Send a message to telegram channel """
|
||||
try:
|
||||
whconfig = self._config['webhook']
|
||||
if msg['type'] in [RPCMessageType.ENTRY]:
|
||||
valuedict = whconfig.get('webhookentry')
|
||||
elif msg['type'] in [RPCMessageType.ENTRY_CANCEL]:
|
||||
valuedict = whconfig.get('webhookentrycancel')
|
||||
elif msg['type'] in [RPCMessageType.ENTRY_FILL]:
|
||||
valuedict = whconfig.get('webhookentryfill')
|
||||
elif msg['type'] == RPCMessageType.EXIT:
|
||||
valuedict = whconfig.get('webhookexit')
|
||||
elif msg['type'] == RPCMessageType.EXIT_FILL:
|
||||
valuedict = whconfig.get('webhookexitfill')
|
||||
elif msg['type'] == RPCMessageType.EXIT_CANCEL:
|
||||
valuedict = whconfig.get('webhookexitcancel')
|
||||
elif msg['type'] in (RPCMessageType.STATUS,
|
||||
RPCMessageType.STARTUP,
|
||||
RPCMessageType.WARNING):
|
||||
valuedict = whconfig.get('webhookstatus')
|
||||
elif msg['type'] in (
|
||||
RPCMessageType.PROTECTION_TRIGGER,
|
||||
RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
|
||||
RPCMessageType.WHITELIST,
|
||||
RPCMessageType.ANALYZED_DF,
|
||||
RPCMessageType.STRATEGY_MSG):
|
||||
# Don't fail for non-implemented types
|
||||
return
|
||||
else:
|
||||
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
|
||||
|
||||
valuedict = self._get_value_dict(msg)
|
||||
|
||||
if not valuedict:
|
||||
logger.info("Message type '%s' not configured for webhooks", msg['type'])
|
||||
return
|
||||
|
||||
@ -49,7 +49,7 @@ class IStrategy(ABC, HyperStrategyMixin):
|
||||
|
||||
_ft_params_from_file: Dict
|
||||
# associated minimal roi
|
||||
minimal_roi: Dict = {}
|
||||
minimal_roi: Dict = {"0": 10.0}
|
||||
|
||||
# associated stoploss
|
||||
stoploss: float
|
||||
|
||||
@ -8,23 +8,23 @@
|
||||
coveralls==3.3.1
|
||||
flake8==5.0.4
|
||||
flake8-tidy-imports==4.8.0
|
||||
mypy==0.981
|
||||
mypy==0.982
|
||||
pre-commit==2.20.0
|
||||
pytest==7.1.3
|
||||
pytest-asyncio==0.19.0
|
||||
pytest-cov==4.0.0
|
||||
pytest-mock==3.9.0
|
||||
pytest-mock==3.10.0
|
||||
pytest-random-order==1.0.4
|
||||
isort==5.10.1
|
||||
# For datetime mocking
|
||||
time-machine==2.8.2
|
||||
|
||||
# Convert jupyter notebooks to markdown documents
|
||||
nbconvert==7.0.0
|
||||
nbconvert==7.2.1
|
||||
|
||||
# mypy types
|
||||
types-cachetools==5.2.1
|
||||
types-filelock==3.2.7
|
||||
types-requests==2.28.11
|
||||
types-tabulate==0.8.11
|
||||
types-requests==2.28.11.2
|
||||
types-tabulate==0.9.0.0
|
||||
types-python-dateutil==2.8.19
|
||||
|
||||
@ -7,3 +7,4 @@ joblib==1.2.0
|
||||
catboost==1.1; platform_machine != 'aarch64'
|
||||
lightgbm==3.3.2
|
||||
xgboost==1.6.2
|
||||
tensorboard==2.10.1
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
-r requirements.txt
|
||||
|
||||
# Required for hyperopt
|
||||
scipy==1.9.1
|
||||
scipy==1.9.2
|
||||
scikit-learn==1.1.2
|
||||
scikit-optimize==0.9.0
|
||||
filelock==3.8.0
|
||||
|
||||
@ -4,7 +4,7 @@ pandas==1.5.0; platform_machine != 'armv7l'
|
||||
pandas==1.4.3; platform_machine == 'armv7l'
|
||||
pandas-ta==0.3.14b
|
||||
|
||||
ccxt==1.95.2
|
||||
ccxt==1.95.30
|
||||
# Pin cryptography for now due to rust build errors with piwheels
|
||||
cryptography==38.0.1
|
||||
aiohttp==3.8.3
|
||||
@ -17,7 +17,7 @@ urllib3==1.26.12
|
||||
jsonschema==4.16.0
|
||||
TA-Lib==0.4.25
|
||||
technical==1.3.0
|
||||
tabulate==0.8.10
|
||||
tabulate==0.9.0
|
||||
pycoingecko==3.0.0
|
||||
jinja2==3.1.2
|
||||
tables==3.7.0
|
||||
|
||||
@ -82,7 +82,7 @@ def readable_timedelta(delta):
|
||||
"""
|
||||
attrs = ['years', 'months', 'days', 'hours', 'minutes', 'seconds', 'microseconds']
|
||||
return ", ".join([
|
||||
'%d %s' % (getattr(delta, attr), attr if getattr(delta, attr) > 1 else attr[:-1])
|
||||
'%d %s' % (getattr(delta, attr), attr if getattr(delta, attr) > 0 else attr[:-1])
|
||||
for attr in attrs if getattr(delta, attr)
|
||||
])
|
||||
|
||||
@ -170,7 +170,7 @@ class ClientProtocol:
|
||||
|
||||
def _calculate_time_difference(self):
|
||||
old_last_received_at = self._LAST_RECEIVED_AT
|
||||
self._LAST_RECEIVED_AT = time.time() * 1000
|
||||
self._LAST_RECEIVED_AT = time.time() * 1e6
|
||||
time_delta = relativedelta(microseconds=(self._LAST_RECEIVED_AT - old_last_received_at))
|
||||
|
||||
return readable_timedelta(time_delta)
|
||||
@ -238,7 +238,7 @@ async def create_client(
|
||||
|
||||
except (
|
||||
asyncio.TimeoutError,
|
||||
websockets.exceptions.ConnectionClosed
|
||||
websockets.exceptions.WebSocketException
|
||||
):
|
||||
# Try pinging
|
||||
try:
|
||||
@ -298,7 +298,7 @@ async def _main(args):
|
||||
producers = emc_config.get('producers', [])
|
||||
producer = producers[0]
|
||||
|
||||
wait_timeout = emc_config.get('wait_timeout', 300)
|
||||
wait_timeout = emc_config.get('wait_timeout', 30)
|
||||
ping_timeout = emc_config.get('ping_timeout', 10)
|
||||
sleep_time = emc_config.get('sleep_time', 10)
|
||||
message_size_limit = (emc_config.get('message_size_limit', 8) << 20)
|
||||
@ -311,7 +311,8 @@ async def _main(args):
|
||||
sleep_time=sleep_time,
|
||||
ping_timeout=ping_timeout,
|
||||
wait_timeout=wait_timeout,
|
||||
max_size=message_size_limit
|
||||
max_size=message_size_limit,
|
||||
ping_interval=None
|
||||
)
|
||||
|
||||
|
||||
|
||||
@ -56,7 +56,7 @@ EXCHANGES = {
|
||||
'leverage_in_spot_market': True,
|
||||
},
|
||||
'kucoin': {
|
||||
'pair': 'BTC/USDT',
|
||||
'pair': 'XRP/USDT',
|
||||
'stake_currency': 'USDT',
|
||||
'hasQuoteVolume': True,
|
||||
'timeframe': '5m',
|
||||
|
||||
@ -1834,6 +1834,7 @@ def test_get_tickers(default_conf, mocker, exchange_name):
|
||||
'last': 41,
|
||||
}
|
||||
}
|
||||
mocker.patch('freqtrade.exchange.exchange.Exchange.exchange_has', return_value=True)
|
||||
api_mock.fetch_tickers = MagicMock(return_value=tick)
|
||||
api_mock.fetch_bids_asks = MagicMock(return_value={})
|
||||
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
|
||||
@ -1883,6 +1884,11 @@ def test_get_tickers(default_conf, mocker, exchange_name):
|
||||
assert api_mock.fetch_tickers.call_count == 1
|
||||
assert api_mock.fetch_bids_asks.call_count == (1 if exchange_name == 'binance' else 0)
|
||||
|
||||
api_mock.fetch_tickers.reset_mock()
|
||||
api_mock.fetch_bids_asks.reset_mock()
|
||||
mocker.patch('freqtrade.exchange.exchange.Exchange.exchange_has', return_value=False)
|
||||
assert exchange.get_tickers() == {}
|
||||
|
||||
|
||||
@pytest.mark.parametrize("exchange_name", EXCHANGES)
|
||||
def test_fetch_ticker(default_conf, mocker, exchange_name):
|
||||
|
||||
@ -107,6 +107,8 @@ def make_unfiltered_dataframe(mocker, freqai_conf):
|
||||
unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
|
||||
strategy, corr_dataframes, base_dataframes, freqai.dk.pair
|
||||
)
|
||||
for i in range(5):
|
||||
unfiltered_dataframe[f'constant_{i}'] = i
|
||||
|
||||
unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
|
||||
|
||||
|
||||
@ -163,7 +163,7 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
|
||||
("CatboostClassifier", 6, "freqai_test_classifier")
|
||||
],
|
||||
)
|
||||
def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
|
||||
def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
|
||||
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
|
||||
freqai_conf['runmode'] = RunMode.BACKTEST
|
||||
Trade.use_db = False
|
||||
@ -187,12 +187,23 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
for i in range(5):
|
||||
df[f'%-constant_{i}'] = i
|
||||
# df.loc[:, f'%-constant_{i}'] = i
|
||||
|
||||
metadata = {"pair": "LTC/BTC"}
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
|
||||
assert len(model_folders) == num_files
|
||||
assert log_has_re(
|
||||
"Removed features ",
|
||||
caplog,
|
||||
)
|
||||
assert log_has_re(
|
||||
"Removed 5 features from prediction features, ",
|
||||
caplog,
|
||||
)
|
||||
Backtesting.cleanup()
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
@ -262,6 +273,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
|
||||
assert log_has_re(
|
||||
@ -318,6 +330,7 @@ def test_follow_mode(mocker, freqai_conf):
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
|
||||
|
||||
freqai.start_live(df, metadata, strategy, freqai.dk)
|
||||
|
||||
assert len(freqai.dk.return_dataframe.index) == 5702
|
||||
|
||||
@ -910,8 +910,9 @@ def test_in_strategy_auto_hyperopt_with_parallel(mocker, hyperopt_conf, tmpdir,
|
||||
})
|
||||
hyperopt = Hyperopt(hyperopt_conf)
|
||||
hyperopt.backtesting.exchange.get_max_leverage = lambda *x, **xx: 1.0
|
||||
hyperopt.backtesting.exchange.get_min_pair_stake_amount = lambda *x, **xx: 1.0
|
||||
hyperopt.backtesting.exchange.get_min_pair_stake_amount = lambda *x, **xx: 0.00001
|
||||
hyperopt.backtesting.exchange.get_max_pair_stake_amount = lambda *x, **xx: 100.0
|
||||
hyperopt.backtesting.exchange._markets = get_markets()
|
||||
|
||||
assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto)
|
||||
assert isinstance(hyperopt.backtesting.strategy.buy_rsi, IntParameter)
|
||||
|
||||
@ -1443,8 +1443,9 @@ def test_api_plot_config(botclient):
|
||||
assert isinstance(rc.json()['subplots'], dict)
|
||||
|
||||
|
||||
def test_api_strategies(botclient):
|
||||
def test_api_strategies(botclient, tmpdir):
|
||||
ftbot, client = botclient
|
||||
ftbot.config['user_data_dir'] = Path(tmpdir)
|
||||
|
||||
rc = client_get(client, f"{BASE_URI}/strategies")
|
||||
|
||||
|
||||
@ -99,6 +99,7 @@ def test_send_msg_telegram_error(mocker, default_conf, caplog) -> None:
|
||||
|
||||
def test_process_msg_queue(mocker, default_conf, caplog) -> None:
|
||||
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg')
|
||||
default_conf['telegram']['allow_custom_messages'] = True
|
||||
mocker.patch('freqtrade.rpc.telegram.Telegram._init')
|
||||
|
||||
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
|
||||
@ -108,8 +109,8 @@ def test_process_msg_queue(mocker, default_conf, caplog) -> None:
|
||||
queue.append('Test message 2')
|
||||
rpc_manager.process_msg_queue(queue)
|
||||
|
||||
assert log_has("Sending rpc message: {'type': strategy_msg, 'msg': 'Test message'}", caplog)
|
||||
assert log_has("Sending rpc message: {'type': strategy_msg, 'msg': 'Test message 2'}", caplog)
|
||||
assert log_has("Sending rpc strategy_msg: Test message", caplog)
|
||||
assert log_has("Sending rpc strategy_msg: Test message 2", caplog)
|
||||
assert telegram_mock.call_count == 2
|
||||
|
||||
|
||||
|
||||
@ -3,7 +3,6 @@
|
||||
from datetime import datetime, timedelta
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
from requests import RequestException
|
||||
|
||||
from freqtrade.enums import ExitType, RPCMessageType
|
||||
@ -337,34 +336,18 @@ def test_exception_send_msg(default_conf, mocker, caplog):
|
||||
caplog)
|
||||
|
||||
default_conf["webhook"] = get_webhook_dict()
|
||||
default_conf["webhook"]["webhookentry"]["value1"] = "{DEADBEEF:8f}"
|
||||
default_conf["webhook"]["strategy_msg"] = {"value1": "{DEADBEEF:8f}"}
|
||||
msg_mock = MagicMock()
|
||||
mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
|
||||
webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
|
||||
msg = {
|
||||
'type': RPCMessageType.ENTRY,
|
||||
'exchange': 'Binance',
|
||||
'pair': 'ETH/BTC',
|
||||
'limit': 0.005,
|
||||
'order_type': 'limit',
|
||||
'stake_amount': 0.8,
|
||||
'stake_amount_fiat': 500,
|
||||
'stake_currency': 'BTC',
|
||||
'fiat_currency': 'EUR'
|
||||
'type': RPCMessageType.STRATEGY_MSG,
|
||||
'msg': 'hello world',
|
||||
}
|
||||
webhook.send_msg(msg)
|
||||
assert log_has("Problem calling Webhook. Please check your webhook configuration. "
|
||||
"Exception: 'DEADBEEF'", caplog)
|
||||
|
||||
msg_mock = MagicMock()
|
||||
mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
|
||||
msg = {
|
||||
'type': 'DEADBEEF',
|
||||
'status': 'whatever'
|
||||
}
|
||||
with pytest.raises(NotImplementedError):
|
||||
webhook.send_msg(msg)
|
||||
|
||||
# Test no failure for not implemented but known messagetypes
|
||||
for e in RPCMessageType:
|
||||
msg = {
|
||||
|
||||
@ -32,7 +32,7 @@ def test_search_strategy():
|
||||
|
||||
def test_search_all_strategies_no_failed():
|
||||
directory = Path(__file__).parent / "strats"
|
||||
strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
|
||||
strategies = StrategyResolver._search_all_objects(directory, enum_failed=False)
|
||||
assert isinstance(strategies, list)
|
||||
assert len(strategies) == 9
|
||||
assert isinstance(strategies[0], dict)
|
||||
@ -40,7 +40,7 @@ def test_search_all_strategies_no_failed():
|
||||
|
||||
def test_search_all_strategies_with_failed():
|
||||
directory = Path(__file__).parent / "strats"
|
||||
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
|
||||
strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
|
||||
assert isinstance(strategies, list)
|
||||
assert len(strategies) == 10
|
||||
# with enum_failed=True search_all_objects() shall find 2 good strategies
|
||||
@ -49,7 +49,7 @@ def test_search_all_strategies_with_failed():
|
||||
assert len([x for x in strategies if x['class'] is None]) == 1
|
||||
|
||||
directory = Path(__file__).parent / "strats_nonexistingdir"
|
||||
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
|
||||
strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
|
||||
assert len(strategies) == 0
|
||||
|
||||
|
||||
@ -77,10 +77,9 @@ def test_load_strategy_base64(dataframe_1m, caplog, default_conf):
|
||||
|
||||
|
||||
def test_load_strategy_invalid_directory(caplog, default_conf):
|
||||
default_conf['strategy'] = 'StrategyTestV3'
|
||||
extra_dir = Path.cwd() / 'some/path'
|
||||
with pytest.raises(OperationalException):
|
||||
StrategyResolver._load_strategy(CURRENT_TEST_STRATEGY, config=default_conf,
|
||||
with pytest.raises(OperationalException, match=r"Impossible to load Strategy.*"):
|
||||
StrategyResolver._load_strategy('StrategyTestV333', config=default_conf,
|
||||
extra_dir=extra_dir)
|
||||
|
||||
assert log_has_re(r'Path .*' + r'some.*path.*' + r'.* does not exist', caplog)
|
||||
@ -102,8 +101,8 @@ def test_load_strategy_noname(default_conf):
|
||||
StrategyResolver.load_strategy(default_conf)
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings("ignore:deprecated")
|
||||
@pytest.mark.parametrize('strategy_name', ['StrategyTestV2'])
|
||||
@ pytest.mark.filterwarnings("ignore:deprecated")
|
||||
@ pytest.mark.parametrize('strategy_name', ['StrategyTestV2'])
|
||||
def test_strategy_pre_v3(dataframe_1m, default_conf, strategy_name):
|
||||
default_conf.update({'strategy': strategy_name})
|
||||
|
||||
@ -349,7 +348,7 @@ def test_strategy_override_use_exit_profit_only(caplog, default_conf):
|
||||
assert log_has("Override strategy 'exit_profit_only' with value in config file: True.", caplog)
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings("ignore:deprecated")
|
||||
@ pytest.mark.filterwarnings("ignore:deprecated")
|
||||
def test_missing_implements(default_conf, caplog):
|
||||
|
||||
default_location = Path(__file__).parent / "strats"
|
||||
|
||||
@ -25,7 +25,7 @@ def test_create_userdata_dir(mocker, default_conf, caplog) -> None:
|
||||
md = mocker.patch.object(Path, 'mkdir', MagicMock())
|
||||
|
||||
x = create_userdata_dir('/tmp/bar', create_dir=True)
|
||||
assert md.call_count == 9
|
||||
assert md.call_count == 10
|
||||
assert md.call_args[1]['parents'] is False
|
||||
assert log_has(f'Created user-data directory: {Path("/tmp/bar")}', caplog)
|
||||
assert isinstance(x, Path)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user