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merge develop into feat/freqai-rl-dev

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robcaulk
2022-10-30 10:13:03 +01:00
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29
docs/advanced-hyperopt.md
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@@ -78,6 +78,8 @@ This function needs to return a floating point number (`float`). Smaller numbers
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
```python
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
# Define a custom stoploss space.
@@ -94,6 +96,33 @@ class MyAwesomeStrategy(IStrategy):
SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
]
def generate_roi_table(params: Dict) -> Dict[int, float]:
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
def trailing_space() -> List[Dimension]:
# All parameters here are mandatory, you can only modify their type or the range.
return [
# Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times.
Categorical([True], name='trailing_stop'),
SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
```
!!! Note

8
docs/backtesting.md
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@@ -522,13 +522,13 @@ Since backtesting lacks some detailed information about what happens within a ca
- ROI
- exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
- exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
- Forceexits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
- Force-exits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
- Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
- Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
- Low happens before high for stoploss, protecting capital first
- Trailing stoploss
- Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
- On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point
- On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point. This rule is NOT applicable to custom-stoploss scenarios, since there's no information about the stoploss logic available.
- High happens first - adjusting stoploss
- Low uses the adjusted stoploss (so exits with large high-low difference are backtested correctly)
- ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
@@ -546,8 +546,8 @@ In addition to the above assumptions, strategy authors should carefully read the
### Trading limits in backtesting
Exchanges have certain trading limits, like minimum base currency, or minimum stake (quote) currency.
These limits are usually listed in the exchange documentation as "trading rules" or similar.
Exchanges have certain trading limits, like minimum (and maximum) base currency, or minimum/maximum stake (quote) currency.
These limits are usually listed in the exchange documentation as "trading rules" or similar and can be quite different between different pairs.
Backtesting (as well as live and dry-run) does honor these limits, and will ensure that a stoploss can be placed below this value - so the value will be slightly higher than what the exchange specifies.
Freqtrade has however no information about historic limits.

16
docs/configuration.md
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@@ -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

14
docs/deprecated.md
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@@ -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`

6
docs/faq.md
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@@ -102,6 +102,12 @@ If this happens for all pairs in the pairlist, this might indicate a recent exch
Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
### I'm getting "Price jump between 2 candles detected"
This message is a warning that the candles had a price jump of > 30%.
This might be a sign that the pair stopped trading, and some token exchange took place (e.g. COCOS in 2021 - where price jumped from 0.0000154 to 0.01621).
This message is often accompanied by ["Missing data fillup"](#im-getting-missing-data-fillup-messages-in-the-log) - as trading on such pairs is often stopped for some time.
### I'm getting "Outdated history for pair xxx" in the log
The bot is trying to tell you that it got an outdated last candle (not the last complete candle).

42
docs/freqai-configuration.md
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@@ -192,11 +192,11 @@ dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
```
To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_predictions_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
```json
"freqai": {
"fit_live_prediction_candles": 300,
"fit_live_predictions_candles": 300,
}
```
@@ -204,14 +204,44 @@ If this value is set, FreqAI will initially use the predictions from the trainin
## Using different prediction models
FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures.
FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `CatBoost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`.
### Setting classifier targets
Regression and classification models differ in what targets they predict - a regression model will predict a target of continuous values, for example what price BTC will be at tomorrow, whilst a classifier will predict a target of discrete values, for example if the price of BTC will go up tomorrow or not. This means that you have to specify your targets differently depending on which model type you are using (see details [below](#setting-model-targets)).
FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example:
All of the aforementioned model libraries implement gradient boosted decision tree algorithms. They all work on the principle of ensemble learning, where predictions from multiple simple learners are combined to get a final prediction that is more stable and generalized. The simple learners in this case are decision trees. Gradient boosting refers to the method of learning, where each simple learner is built in sequence - the subsequent learner is used to improve on the error from the previous learner. If you want to learn more about the different model libraries you can find the information in their respective docs:
* CatBoost: https://catboost.ai/en/docs/
* LightGBM: https://lightgbm.readthedocs.io/en/v3.3.2/#
* XGBoost: https://xgboost.readthedocs.io/en/stable/#
There are also numerous online articles describing and comparing the algorithms. Some relatively light-weight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.
Apart from the models already available in FreqAI, it is also possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to customize various aspects of the training procedures. You can place custom FreqAI models in `user_data/freqaimodels` - and freqtrade will pick them up from there based on the provided `--freqaimodel` name - which has to correspond to the class name of your custom model.
Make sure to use unique names to avoid overriding built-in models.
### Setting model targets
#### Regressors
If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the `CatboostRegressor`via the flag `--freqaimodel CatboostRegressor`. An example of how you could set a regression target for predicting the price 100 candles into the future would be
```python
df['&s-close_price'] = df['close'].shift(-100)
```
If you want to predict multiple targets, you need to define multiple labels using the same syntax as shown above.
#### Classifiers
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
```
Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column.
If you want to predict multiple targets you must specify all labels in the same label column. You could, for example, add the label `same` to define where the price was unchanged by setting
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down'])
```

3
docs/freqai-parameter-table.md
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@@ -37,12 +37,13 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
| | **Data split parameters**
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
| | **Model training parameters**
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. <br> **Datatype:** Dictionary.
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. A list of the currently available models can be found [here](freqai-configuration.md#using-different-prediction-models). <br> **Datatype:** Dictionary.
| `n_estimators` | The number of boosted trees to fit in the training of the model. <br> **Datatype:** Integer.
| `learning_rate` | Boosting learning rate during training of the model. <br> **Datatype:** Float.
| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.

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docs/freqai-running.md
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@@ -142,15 +142,32 @@ dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1
This specific hyperopt would help you understand the appropriate `DI_values` for your particular parameter space.
## Using Tensorboard
CatBoost models benefit from tracking training metrics via Tensorboard. You can take advantage of the FreqAI integration to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
```bash
cd freqtrade
tensorboard --logdir user_data/models/unique-id
```
where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell if you wish to view the output in your browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
![tensorboard](assets/tensorboard.jpg)
## Setting up a follower
You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining:
```json
"freqai": {
"enabled": true,
"follow_mode": true,
"identifier": "example"
"identifier": "example",
"feature_parameters": {
// leader bots feature_parameters inserted here
},
}
```
In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models.
In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models. The user will also need to duplicate the `feature_parameters` parameters from from the leaders freqai configuration file into the freqai section of the followers config.

6
docs/requirements-docs.txt
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@@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.4.0
mkdocs-material==8.5.6
mkdocs==1.4.1
mkdocs-material==8.5.7
mdx_truly_sane_lists==1.3
pymdown-extensions==9.6
pymdown-extensions==9.7
jinja2==3.1.2

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

1
docs/strategy-callbacks.md
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@@ -159,6 +159,7 @@ The stoploss price can only ever move upwards - if the stoploss value returned f
The method must return a stoploss value (float / number) as a percentage of the current price.
E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
During backtesting, `current_rate` (and `current_profit`) are provided against the candle's high (or low for short trades) - while the resulting stoploss is evaluated against the candle's low (or high for short trades).
The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.

14
docs/strategy-customization.md
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@@ -655,13 +655,13 @@ This is where calling `self.dp.current_whitelist()` comes in handy.
# fetch live / historical candle (OHLCV) data for the first informative pair
inf_pair, inf_timeframe = self.informative_pairs()[0]
informative = self.dp.get_pair_dataframe(pair=inf_pair,
timeframe=inf_timeframe)
timeframe=inf_timeframe)
```
!!! Warning "Warning about backtesting"
Be careful when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
for the backtesting runmode) provides the full time-range in one go,
so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode.
In backtesting, `dp.get_pair_dataframe()` behavior differs depending on where it's called.
Within `populate_*()` methods, `dp.get_pair_dataframe()` returns the full timerange. Please make sure to not "look into the future" to avoid surprises when running in dry/live mode.
Within [callbacks](strategy-callbacks.md), you'll get the full timerange up to the current (simulated) candle.
### *get_analyzed_dataframe(pair, timeframe)*
@@ -670,13 +670,13 @@ It can also be used in specific callbacks to get the signal that caused the acti
``` python
# fetch current dataframe
if self.dp.runmode.value in ('live', 'dry_run'):
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
timeframe=self.timeframe)
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
timeframe=self.timeframe)
```
!!! Note "No data available"
Returns an empty dataframe if the requested pair was not cached.
You can check for this with `if dataframe.empty:` and handle this case accordingly.
This should not happen when using whitelisted pairs.
### *orderbook(pair, maximum)*

22
docs/strategy_migration.md
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@@ -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
}
```

2
docs/telegram-usage.md
View File

@@ -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)

37
docs/utils.md
View File

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

70
docs/webhook-config.md
View File

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