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Merge pull request #8095 from freqtrade/remove-follow-mode
remove follow mode in favor of producer consumer
This commit is contained in:
Matthias
GitHub
commit
3c4ff2e037
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@ -18,7 +18,6 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
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| `purge_old_models` | Delete all unused models during live runs (not relevant to backtesting). If set to false (not default), dry/live runs will accumulate all unused models to disk. If <br> **Datatype:** Boolean. <br> Default: `True`.
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| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
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| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
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| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
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| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
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| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
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| `data_kitchen_thread_count` | <br> Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.). This has no impact on the number of threads used for training. If user does not set it (default), FreqAI will use max number of threads - 2 (leaving 1 physical core available for Freqtrade bot and FreqUI) <br> **Datatype:** Positive integer.
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@ -165,20 +165,3 @@ tensorboard --logdir user_data/models/unique-id
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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).
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## Setting up a follower
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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:
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```json
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"freqai": {
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"enabled": true,
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"follow_mode": true,
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"identifier": "example",
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"feature_parameters": {
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// leader bots feature_parameters inserted here
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},
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}
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```
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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.
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@ -59,7 +59,7 @@ class FreqaiDataDrawer:
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Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
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"""
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def __init__(self, full_path: Path, config: Config, follow_mode: bool = False):
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def __init__(self, full_path: Path, config: Config):
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self.config = config
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self.freqai_info = config.get("freqai", {})
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@ -84,9 +84,6 @@ class FreqaiDataDrawer:
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self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
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self.global_metadata_path = Path(self.full_path / "global_metadata.json")
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self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
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self.follow_mode = follow_mode
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if follow_mode:
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self.create_follower_dict()
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self.load_drawer_from_disk()
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self.load_historic_predictions_from_disk()
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self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
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@ -149,13 +146,8 @@ class FreqaiDataDrawer:
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if exists:
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with open(self.pair_dictionary_path, "r") as fp:
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self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
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elif not self.follow_mode:
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logger.info("Could not find existing datadrawer, starting from scratch")
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else:
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logger.warning(
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f"Follower could not find pair_dictionary at {self.full_path} "
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"sending null values back to strategy"
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)
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logger.info("Could not find existing datadrawer, starting from scratch")
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def load_metric_tracker_from_disk(self):
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"""
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@ -193,13 +185,8 @@ class FreqaiDataDrawer:
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self.historic_predictions = cloudpickle.load(fp)
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logger.warning('FreqAI successfully loaded the backup historical predictions file.')
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elif not self.follow_mode:
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logger.info("Could not find existing historic_predictions, starting from scratch")
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else:
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logger.warning(
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f"Follower could not find historic predictions at {self.full_path} "
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"sending null values back to strategy"
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)
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logger.info("Could not find existing historic_predictions, starting from scratch")
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return exists
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@ -248,23 +235,6 @@ class FreqaiDataDrawer:
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rapidjson.dump(metadata, fp, default=self.np_encoder,
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number_mode=rapidjson.NM_NATIVE)
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def create_follower_dict(self):
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"""
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Create or dictionary for each follower to maintain unique persistent prediction targets
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"""
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whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
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exists = self.follower_dict_path.is_file()
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if exists:
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logger.info("Found an existing follower dictionary")
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for pair in whitelist_pairs:
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self.follower_dict[pair] = {}
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self.save_follower_dict_to_disk()
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def np_encoder(self, object):
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if isinstance(object, np.generic):
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return object.item()
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@ -282,27 +252,17 @@ class FreqaiDataDrawer:
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"""
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pair_dict = self.pair_dict.get(pair)
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data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
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# data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
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return_null_array = False
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if pair_dict:
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model_filename = pair_dict["model_filename"]
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trained_timestamp = pair_dict["trained_timestamp"]
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elif not self.follow_mode:
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else:
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self.pair_dict[pair] = self.empty_pair_dict.copy()
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model_filename = ""
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trained_timestamp = 0
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if not data_path_set and self.follow_mode:
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logger.warning(
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f"Follower could not find current pair {pair} in "
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f"pair_dictionary at path {self.full_path}, sending null values "
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"back to strategy."
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)
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trained_timestamp = 0
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model_filename = ''
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return_null_array = True
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return model_filename, trained_timestamp, return_null_array
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def set_pair_dict_info(self, metadata: dict) -> None:
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@ -311,7 +271,6 @@ class FreqaiDataDrawer:
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return
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else:
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self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
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return
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def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
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@ -66,12 +66,11 @@ class IFreqaiModel(ABC):
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self.retrain = False
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self.first = True
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self.set_full_path()
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self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
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self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
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if self.save_backtest_models:
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logger.info('Backtesting module configured to save all models.')
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self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
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self.dd = FreqaiDataDrawer(Path(self.full_path), self.config)
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# set current candle to arbitrary historical date
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self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
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self.dd.current_candle = self.current_candle
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@ -153,7 +152,7 @@ class IFreqaiModel(ABC):
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# (backtest window, i.e. window immediately following the training window).
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# FreqAI slides the window and sequentially builds the backtesting results before returning
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# the concatenated results for the full backtesting period back to the strategy.
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elif not self.follow_mode:
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else:
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self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
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if not self.config.get("freqai_backtest_live_models", False):
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logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
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@ -379,27 +378,16 @@ class IFreqaiModel(ABC):
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:returns:
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dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
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"""
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# update follower
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if self.follow_mode:
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self.dd.update_follower_metadata()
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# get the model metadata associated with the current pair
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(_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
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# if the metadata doesn't exist, the follower returns null arrays to strategy
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if self.follow_mode and return_null_array:
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logger.info("Returning null array from follower to strategy")
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self.dd.return_null_values_to_strategy(dataframe, dk)
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return dk
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# append the historic data once per round
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if self.dd.historic_data:
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self.dd.update_historic_data(strategy, dk)
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logger.debug(f'Updating historic data on pair {metadata["pair"]}')
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self.track_current_candle()
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if not self.follow_mode:
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(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
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trained_timestamp
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)
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@ -413,13 +401,6 @@ class IFreqaiModel(ABC):
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self.scanning = True
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self.start_scanning(strategy)
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elif self.follow_mode:
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dk.set_paths(metadata["pair"], trained_timestamp)
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logger.info(
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"FreqAI instance set to follow_mode, finding existing pair "
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f"using { self.identifier }"
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)
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# load the model and associated data into the data kitchen
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self.model = self.dd.load_data(metadata["pair"], dk)
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@ -376,57 +376,6 @@ def test_backtesting_fit_live_predictions(mocker, freqai_conf, caplog):
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_follow_mode(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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metadata = {"pair": "ADA/BTC"}
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freqai.dd.set_pair_dict_info(metadata)
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.extract_data_and_train_model(
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new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
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# start the follower and ask it to predict on existing files
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freqai_conf.get("freqai", {}).update({"follow_mode": "true"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.live)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
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freqai.dk.pair = "ADA/BTC"
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freqai.start_live(df, metadata, strategy, freqai.dk)
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assert len(freqai.dk.return_dataframe.index) == 5702
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_principal_component_analysis(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
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