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Merge remote-tracking branch 'origin/develop' into feat/add-pytorch-model-support

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This commit is contained in:
robcaulk
2023-04-08 13:22:25 +02:00
parent 48d3c8e62e f8d89c46e5
commit 69b9b35a08
123 changed files with 7298 additions and 3422 deletions
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6
freqtrade/freqai/RL/Base3ActionRLEnv.py
View File

@@ -47,7 +47,7 @@ class Base3ActionRLEnv(BaseEnvironment):
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_log(self.actions._member_names_[action])
self.tensorboard_log(self.actions._member_names_[action], category="actions")
trade_type = None
if self.is_tradesignal(action):
@@ -66,7 +66,7 @@ class Base3ActionRLEnv(BaseEnvironment):
elif action == Actions.Sell.value and not self.can_short:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
trade_type = "exit"
self._last_trade_tick = None
else:
print("case not defined")
@@ -74,7 +74,7 @@ class Base3ActionRLEnv(BaseEnvironment):
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
'type': trade_type, 'profit': self.get_unrealized_profit()})
if (self._total_profit < self.max_drawdown or
self._total_unrealized_profit < self.max_drawdown):

20
freqtrade/freqai/RL/Base4ActionRLEnv.py
View File

@@ -48,20 +48,10 @@ class Base4ActionRLEnv(BaseEnvironment):
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_log(self.actions._member_names_[action])
self.tensorboard_log(self.actions._member_names_[action], category="actions")
trade_type = None
if self.is_tradesignal(action):
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
Action: Long, position: Neutral -> Open Long
Action: Long, position: Short -> Close Short and Open Long
Action: Short, position: Neutral -> Open Short
Action: Short, position: Long -> Close Long and Open Short
"""
if action == Actions.Neutral.value:
self._position = Positions.Neutral
@@ -69,16 +59,16 @@ class Base4ActionRLEnv(BaseEnvironment):
self._last_trade_tick = None
elif action == Actions.Long_enter.value:
self._position = Positions.Long
trade_type = "long"
trade_type = "enter_long"
self._last_trade_tick = self._current_tick
elif action == Actions.Short_enter.value:
self._position = Positions.Short
trade_type = "short"
trade_type = "enter_short"
self._last_trade_tick = self._current_tick
elif action == Actions.Exit.value:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
trade_type = "exit"
self._last_trade_tick = None
else:
print("case not defined")
@@ -86,7 +76,7 @@ class Base4ActionRLEnv(BaseEnvironment):
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
'type': trade_type, 'profit': self.get_unrealized_profit()})
if (self._total_profit < self.max_drawdown or
self._total_unrealized_profit < self.max_drawdown):

22
freqtrade/freqai/RL/Base5ActionRLEnv.py
View File

@@ -49,20 +49,10 @@ class Base5ActionRLEnv(BaseEnvironment):
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_log(self.actions._member_names_[action])
self.tensorboard_log(self.actions._member_names_[action], category="actions")
trade_type = None
if self.is_tradesignal(action):
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
Action: Long, position: Neutral -> Open Long
Action: Long, position: Short -> Close Short and Open Long
Action: Short, position: Neutral -> Open Short
Action: Short, position: Long -> Close Long and Open Short
"""
if action == Actions.Neutral.value:
self._position = Positions.Neutral
@@ -70,21 +60,21 @@ class Base5ActionRLEnv(BaseEnvironment):
self._last_trade_tick = None
elif action == Actions.Long_enter.value:
self._position = Positions.Long
trade_type = "long"
trade_type = "enter_long"
self._last_trade_tick = self._current_tick
elif action == Actions.Short_enter.value:
self._position = Positions.Short
trade_type = "short"
trade_type = "enter_short"
self._last_trade_tick = self._current_tick
elif action == Actions.Long_exit.value:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
trade_type = "exit_long"
self._last_trade_tick = None
elif action == Actions.Short_exit.value:
self._update_total_profit()
self._position = Positions.Neutral
trade_type = "neutral"
trade_type = "exit_short"
self._last_trade_tick = None
else:
print("case not defined")
@@ -92,7 +82,7 @@ class Base5ActionRLEnv(BaseEnvironment):
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
'type': trade_type, 'profit': self.get_unrealized_profit()})
if (self._total_profit < self.max_drawdown or
self._total_unrealized_profit < self.max_drawdown):

22
freqtrade/freqai/RL/BaseEnvironment.py
View File

@@ -137,7 +137,8 @@ class BaseEnvironment(gym.Env):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def tensorboard_log(self, metric: str, value: Union[int, float] = 1, inc: bool = True):
def tensorboard_log(self, metric: str, value: Optional[Union[int, float]] = None,
inc: Optional[bool] = None, category: str = "custom"):
"""
Function builds the tensorboard_metrics dictionary
to be parsed by the TensorboardCallback. This
@@ -149,17 +150,24 @@ class BaseEnvironment(gym.Env):
def calculate_reward(self, action: int) -> float:
if not self._is_valid(action):
self.tensorboard_log("is_valid")
self.tensorboard_log("invalid")
return -2
:param metric: metric to be tracked and incremented
:param value: value to increment `metric` by
:param inc: sets whether the `value` is incremented or not
:param value: `metric` value
:param inc: (deprecated) sets whether the `value` is incremented or not
:param category: `metric` category
"""
if not inc or metric not in self.tensorboard_metrics:
self.tensorboard_metrics[metric] = value
increment = True if value is None else False
value = 1 if increment else value
if category not in self.tensorboard_metrics:
self.tensorboard_metrics[category] = {}
if not increment or metric not in self.tensorboard_metrics[category]:
self.tensorboard_metrics[category][metric] = value
else:
self.tensorboard_metrics[metric] += value
self.tensorboard_metrics[category][metric] += value
def reset_tensorboard_log(self):
self.tensorboard_metrics = {}

29
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@@ -114,6 +114,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
# normalize all data based on train_dataset only
prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
data_dictionary = dk.normalize_data(data_dictionary)
# data cleaning/analysis
@@ -148,12 +149,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
env_info = self.pack_env_dict(dk.pair)
self.train_env = self.MyRLEnv(df=train_df,
prices=prices_train,
**env_info)
self.eval_env = Monitor(self.MyRLEnv(df=test_df,
prices=prices_test,
**env_info))
self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, **env_info)
self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test, **env_info))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),
best_model_save_path=str(dk.data_path))
@@ -238,6 +235,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
filtered_dataframe, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = self.drop_ohlc_from_df(filtered_dataframe, dk)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
@@ -285,7 +285,6 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# %-raw_volume_gen_shift-2_ETH/USDT_1h
# price data for model training and evaluation
tf = self.config['timeframe']
rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
@@ -318,8 +317,24 @@ class BaseReinforcementLearningModel(IFreqaiModel):
prices_test.rename(columns=rename_dict, inplace=True)
prices_test.reset_index(drop=True)
train_df = self.drop_ohlc_from_df(train_df, dk)
test_df = self.drop_ohlc_from_df(test_df, dk)
return prices_train, prices_test
def drop_ohlc_from_df(self, df: DataFrame, dk: FreqaiDataKitchen):
"""
Given a dataframe, drop the ohlc data
"""
drop_list = ['%-raw_open', '%-raw_low', '%-raw_high', '%-raw_close']
if self.rl_config["drop_ohlc_from_features"]:
df.drop(drop_list, axis=1, inplace=True)
feature_list = dk.training_features_list
dk.training_features_list = [e for e in feature_list if e not in drop_list]
return df
def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
"""
Can be used by user if they are trying to limit_ram_usage *and*

16
freqtrade/freqai/RL/TensorboardCallback.py
View File

@@ -13,7 +13,7 @@ class TensorboardCallback(BaseCallback):
episodic summary reports.
"""
def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
super(TensorboardCallback, self).__init__(verbose)
super().__init__(verbose)
self.model: Any = None
self.logger = None # type: Any
self.training_env: BaseEnvironment = None # type: ignore
@@ -46,14 +46,12 @@ class TensorboardCallback(BaseCallback):
local_info = self.locals["infos"][0]
tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
for info in local_info:
if info not in ["episode", "terminal_observation"]:
self.logger.record(f"_info/{info}", local_info[info])
for metric in local_info:
if metric not in ["episode", "terminal_observation"]:
self.logger.record(f"info/{metric}", local_info[metric])
for info in tensorboard_metrics:
if info in [action.name for action in self.actions]:
self.logger.record(f"_actions/{info}", tensorboard_metrics[info])
else:
self.logger.record(f"_custom/{info}", tensorboard_metrics[info])
for category in tensorboard_metrics:
for metric in tensorboard_metrics[category]:
self.logger.record(f"{category}/{metric}", tensorboard_metrics[category][metric])
return True

6
freqtrade/freqai/data_kitchen.py
View File

@@ -251,7 +251,7 @@ class FreqaiDataKitchen:
(drop_index == 0) & (drop_index_labels == 0)
]
logger.info(
f"dropped {len(unfiltered_df) - len(filtered_df)} training points"
f"{self.pair}: dropped {len(unfiltered_df) - len(filtered_df)} training points"
f" due to NaNs in populated dataset {len(unfiltered_df)}."
)
if (1 - len(filtered_df) / len(unfiltered_df)) > 0.1 and self.live:
@@ -675,7 +675,7 @@ class FreqaiDataKitchen:
]
logger.info(
f"SVM tossed {len(y_pred) - kept_points.sum()}"
f"{self.pair}: SVM tossed {len(y_pred) - kept_points.sum()}"
f" test points from {len(y_pred)} total points."
)
@@ -949,7 +949,7 @@ class FreqaiDataKitchen:
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(
f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
f"{self.pair}: DI tossed {len(do_predict) - do_predict.sum()} predictions for "
"being too far from training data."
)

14
freqtrade/freqai/freqai_interface.py
View File

@@ -105,6 +105,10 @@ class IFreqaiModel(ABC):
self.data_provider: Optional[DataProvider] = None
self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
self.can_short = True # overridden in start() with strategy.can_short
self.model: Any = None
if self.ft_params.get('principal_component_analysis', False) and self.continual_learning:
self.ft_params.update({'principal_component_analysis': False})
logger.warning('User tried to use PCA with continual learning. Deactivating PCA.')
record_params(config, self.full_path)
@@ -154,8 +158,7 @@ class IFreqaiModel(ABC):
dk = self.start_backtesting(dataframe, metadata, self.dk, strategy)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
else:
logger.info(
"Backtesting using historic predictions (live models)")
logger.info("Backtesting using historic predictions (live models)")
dk = self.start_backtesting_from_historic_predictions(
dataframe, metadata, self.dk)
dataframe = dk.return_dataframe
@@ -339,13 +342,14 @@ class IFreqaiModel(ABC):
except Exception as msg:
logger.warning(
f"Training {pair} raised exception {msg.__class__.__name__}. "
f"Message: {msg}, skipping.")
f"Message: {msg}, skipping.", exc_info=True)
self.model = None
self.dd.pair_dict[pair]["trained_timestamp"] = int(
tr_train.stopts)
if self.plot_features:
if self.plot_features and self.model is not None:
plot_feature_importance(self.model, pair, dk, self.plot_features)
if self.save_backtest_models:
if self.save_backtest_models and self.model is not None:
logger.info('Saving backtest model to disk.')
self.dd.save_data(self.model, pair, dk)
else:

2
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@@ -100,7 +100,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
"""
# first, penalize if the action is not valid
if not self._is_valid(action):
self.tensorboard_log("is_valid")
self.tensorboard_log("invalid", category="actions")
return -2
pnl = self.get_unrealized_profit()