ensure spice_rack is backtestable. Ensure download-data knows about the spice_rack informative pair requirements
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@@ -5,7 +5,7 @@ This module defines the interface to apply for strategies
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import logging
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from abc import ABC, abstractmethod
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from datetime import datetime, timedelta, timezone
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from typing import Any, Dict, List, Optional, Tuple, Union
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from typing import Dict, List, Optional, Tuple, Union
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import arrow
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from pandas import DataFrame
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@@ -147,9 +147,9 @@ class IStrategy(ABC, HyperStrategyMixin):
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def load_freqAI_model(self) -> None:
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spice_rack = self.config.get('freqai_spice_rack', False)
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if self.config.get('freqai', {}).get('enabled', False) or spice_rack:
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spice_rack = self.config.get('freqai_spice_rack', False)
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if spice_rack:
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self.config = self.setup_freqai_spice_rack(self.config)
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from freqtrade.freqai.utils import setup_freqai_spice_rack
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self.config = setup_freqai_spice_rack(self.config, self.dp._exchange)
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# Import here to avoid importing this if freqAI is disabled
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from freqtrade.freqai.utils import download_all_data_for_training
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from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
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@@ -190,60 +190,6 @@ class IStrategy(ABC, HyperStrategyMixin):
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self.freqai = DummyClass() # type: ignore
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def setup_freqai_spice_rack(self, config: dict) -> Dict[str, Any]:
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import difflib
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import json
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from pathlib import Path
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auto_config = config.get('freqai_config', 'lightgbm_config.json')
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with open(Path('freqtrade') / 'freqai' / 'spice_rack'
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/ auto_config) as json_file:
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freqai_config = json.load(json_file)
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config['freqai'] = freqai_config['freqai']
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config['freqai']['identifier'] = config['freqai_identifier']
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corr_pairs = config['freqai']['feature_parameters']['include_corr_pairlist']
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timeframes = config['freqai']['feature_parameters']['include_timeframes']
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new_corr_pairs = []
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new_tfs = []
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if not self.dp:
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logger.warning('No dataprovider available.')
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config['freqai']['enabled'] = False
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return config
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# find the closest pairs to what the default config wants
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for pair in corr_pairs:
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closest_pair = difflib.get_close_matches(
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pair,
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self.dp._exchange.markets # type: ignore
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)
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if not closest_pair:
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logger.warning(f'Could not find {pair} in markets, removing from '
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f'corr_pairlist.')
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else:
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closest_pair = closest_pair[0]
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new_corr_pairs.append(closest_pair)
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logger.info(f'Spice rack will use {closest_pair} as informative in FreqAI model.')
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# find the closest matching timeframes to what the default config wants
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if timeframe_to_seconds(config['timeframe']) > timeframe_to_seconds('15m'):
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logger.warning('Default spice rack is designed for lower base timeframes (e.g. > '
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f'15m). But user passed {config["timeframe"]}.')
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new_tfs.append(config['timeframe'])
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list_tfs = [timeframe_to_seconds(tf) for tf
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in self.dp._exchange.timeframes] # type: ignore
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for tf in timeframes:
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tf_secs = timeframe_to_seconds(tf)
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closest_index = min(range(len(list_tfs)), key=lambda i: abs(list_tfs[i] - tf_secs))
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closest_tf = self.dp._exchange.timeframes[closest_index] # type: ignore
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logger.info(f'Spice rack will use {closest_tf} as informative tf in FreqAI model.')
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new_tfs.append(closest_tf)
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config['freqai']['feature_parameters'].update({'include_timeframes': new_tfs})
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config['freqai']['feature_parameters'].update({'include_corr_pairlist': new_corr_pairs})
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config.update({"freqaimodel": 'LightGBMRegressorMultiTarget'})
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return config
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def ft_bot_start(self, **kwargs) -> None:
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"""
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Strategy init - runs after dataprovider has been added.
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