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Merge branch 'develop' into backtest_live_models

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Wagner Costa Santos 2022-10-13 15:52:19 -03:00
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37
docs/freqai-configuration.md
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@ -204,17 +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/`.
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.
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)).
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 classifier targets
### 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:
#### 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'])
```

2
docs/freqai-parameter-table.md
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@ -42,7 +42,7 @@ 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.

5
docs/freqai-running.md
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@ -156,18 +156,17 @@ This specific hyperopt would help you understand the appropriate `DI_values` for
## 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:
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 the user wishes to view the output in their browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
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:

29
freqtrade/optimize/backtesting.py
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@ -621,13 +621,16 @@ 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:
@ -664,7 +667,6 @@ 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,
@ -678,12 +680,12 @@ 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
@ -730,18 +732,21 @@ 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:
@ -803,9 +808,11 @@ 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
@ -821,8 +828,6 @@ 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)

3
tests/optimize/test_hyperopt.py
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@ -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)