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Ensure follower predictions are persistent and uniquely stored
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@ -26,16 +26,20 @@ class FreqaiDataDrawer:
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This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is
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reinstantiated for each coin.
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"""
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def __init__(self, full_path: Path, pair_whitelist, follow_mode: bool = False):
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def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
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self.config = config
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# dictionary holding all pair metadata necessary to load in from disk
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self.pair_dict: Dict[str, Any] = {}
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# dictionary holding all actively inferenced models in memory given a model filename
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self.model_dictionary: Dict[str, Any] = {}
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self.model_return_values: Dict[str, Any] = {}
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self.pair_data_dict: Dict[str, Any] = {}
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self.follower_dict: Dict[str, Any] = {}
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self.full_path = full_path
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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.training_queue: Dict[str, int] = {}
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# self.create_training_queue(pair_whitelist)
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@ -57,6 +61,29 @@ class FreqaiDataDrawer:
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with open(self.full_path / str('pair_dictionary.json'), "w") as fp:
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json.dump(self.pair_dict, fp, default=self.np_encoder)
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def save_follower_dict_to_dist(self):
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follower_name = self.config.get('bot_name', 'follower1')
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with open(self.full_path / str('follower_dictionary-' +
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follower_name + '.json'), "w") as fp:
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json.dump(self.follower_dict, fp, default=self.np_encoder)
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def create_follower_dict(self):
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follower_name = self.config.get('bot_name', 'follower1')
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whitelist_pairs = self.config.get('exchange', {}).get('pair_whitelist')
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exists = Path(self.full_path / str('follower_dictionary-' +
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follower_name + '.json')).resolve().exists()
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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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with open(self.full_path / str('follower_dictionary-' +
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follower_name + '.json'), "w") as fp:
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json.dump(self.follower_dict, fp, default=self.np_encoder)
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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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@ -56,7 +56,7 @@ class IFreqaiModel(ABC):
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self.set_full_path()
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self.follow_mode = self.freqai_info.get('follow_mode', False)
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self.data_drawer = FreqaiDataDrawer(Path(self.full_path),
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self.config['exchange']['pair_whitelist'],
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self.config,
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self.follow_mode)
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self.lock = threading.Lock()
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self.follow_mode = self.freqai_info.get('follow_mode', False)
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@ -242,22 +242,33 @@ class FreqaiExampleStrategy(IStrategy):
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if trade_candle.empty:
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return None
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trade_candle = trade_candle.squeeze()
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pair_dict = self.model.bridge.data_drawer.pair_dict
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follow_mode = self.config.get('freqai', {}).get('follow_mode', False)
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if not follow_mode:
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pair_dict = self.model.bridge.data_drawer.pair_dict
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else:
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pair_dict = self.model.bridge.data_drawer.follower_dict
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entry_tag = trade.enter_tag
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if 'prediction' + entry_tag not in pair_dict[pair]:
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with self.model.bridge.lock:
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self.model.bridge.data_drawer.pair_dict[pair][
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'prediction' + entry_tag] = abs(trade_candle['prediction'])
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self.model.bridge.data_drawer.save_drawer_to_disk()
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pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['prediction'])
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if not follow_mode:
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self.model.bridge.data_drawer.save_drawer_to_disk()
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else:
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self.model.bridge.data_drawer.save_follower_dict_to_dist()
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else:
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if pair_dict[pair]['prediction' + entry_tag] > 0:
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roi_price = abs(trade_candle['prediction'])
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else:
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with self.model.bridge.lock:
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self.model.bridge.data_drawer.pair_dict[pair][
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'prediction' + entry_tag] = abs(trade_candle['prediction'])
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self.model.bridge.data_drawer.save_drawer_to_disk()
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pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['prediction'])
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if not follow_mode:
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self.model.bridge.data_drawer.save_drawer_to_disk()
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else:
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self.model.bridge.data_drawer.save_follower_dict_to_dist()
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roi_price = abs(trade_candle['prediction'])
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roi_time = self.max_roi_time_long.value
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@ -269,17 +280,44 @@ class FreqaiExampleStrategy(IStrategy):
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else:
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roi_decay += self.linear_roi_offset.value
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if current_profit > roi_price:
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if current_profit > roi_decay:
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return 'roi_custom_win'
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if current_profit < -roi_decay:
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return 'roi_custom_loss'
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def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
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rate: float, time_in_force: str, exit_reason: str,
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current_time, **kwargs) -> bool:
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entry_tag = trade.enter_tag
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follow_mode = self.config.get('freqai', {}).get('follow_mode', False)
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if not follow_mode:
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pair_dict = self.model.bridge.data_drawer.pair_dict
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else:
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pair_dict = self.model.bridge.data_drawer.follower_dict
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with self.model.bridge.lock:
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self.model.bridge.data_drawer.pair_dict[pair]['prediction' + entry_tag] = 0
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self.model.bridge.data_drawer.save_drawer_to_disk()
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pair_dict[pair]['prediction' + entry_tag] = 0
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if not follow_mode:
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self.model.bridge.data_drawer.save_drawer_to_disk()
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else:
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self.model.bridge.data_drawer.save_follower_dict_to_dist()
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return True
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time, entry_tag,
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side: str, **kwargs) -> bool:
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = df.iloc[-1].squeeze()
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if side == 'long':
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if last_candle['close'] > (last_candle['close'] * (1 + 0.0025)):
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return False
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else:
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if last_candle['close'] < (last_candle['close'] * (1 - 0.0025)):
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return False
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return True
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