stable/freqtrade/freqai/data_drawer.py

610 lines
24 KiB
Python
Raw Normal View History

import collections
import json
import logging
import re
import shutil
2022-06-06 23:07:30 +00:00
import threading
from pathlib import Path
from typing import Any, Dict, Tuple, TypedDict
import numpy as np
import pandas as pd
import rapidjson
2022-07-26 08:51:39 +00:00
from joblib import dump, load
from joblib.externals import cloudpickle
from numpy.typing import NDArray
from pandas import DataFrame
2022-07-26 08:51:39 +00:00
from freqtrade.configuration import TimeRange
from freqtrade.data.history import load_pair_history
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.strategy.interface import IStrategy
2022-07-26 08:51:39 +00:00
logger = logging.getLogger(__name__)
class pair_info(TypedDict):
model_filename: str
first: bool
trained_timestamp: int
priority: int
data_path: str
extras: dict
class FreqaiDataDrawer:
"""
Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving
/loading to/from disk.
2022-08-14 18:24:29 +00:00
This object remains persistent throughout live/dry.
Record of contribution:
FreqAI was developed by a group of individuals who all contributed specific skillsets to the
project.
Conception and software development:
Robert Caulk @robcaulk
Theoretical brainstorming:
Elin Törnquist @th0rntwig
Code review, software architecture brainstorming:
@xmatthias
Beta testing and bug reporting:
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
2022-08-14 18:24:29 +00:00
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
"""
2022-07-03 08:59:38 +00:00
def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
self.config = config
2022-07-03 08:59:38 +00:00
self.freqai_info = config.get("freqai", {})
# dictionary holding all pair metadata necessary to load in from disk
self.pair_dict: Dict[str, pair_info] = {}
# dictionary holding all actively inferenced models in memory given a model filename
self.model_dictionary: Dict[str, Any] = {}
self.model_return_values: Dict[str, DataFrame] = {}
self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
self.historic_predictions: Dict[str, DataFrame] = {}
self.follower_dict: Dict[str, pair_info] = {}
self.full_path = full_path
2022-07-26 17:41:49 +00:00
self.follower_name: str = self.config.get("bot_name", "follower1")
self.follower_dict_path = Path(
self.full_path / f"follower_dictionary-{self.follower_name}.json"
)
self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
self.follow_mode = follow_mode
if follow_mode:
self.create_follower_dict()
self.load_drawer_from_disk()
self.load_historic_predictions_from_disk()
self.training_queue: Dict[str, int] = {}
2022-06-06 23:07:30 +00:00
self.history_lock = threading.Lock()
2022-08-14 18:24:29 +00:00
self.save_lock = threading.Lock()
self.pair_dict_lock = threading.Lock()
self.old_DBSCAN_eps: Dict[str, float] = {}
self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0,
"priority": 1, "first": True, "data_path": "", "extras": {}}
def load_drawer_from_disk(self):
"""
Locate and load a previously saved data drawer full of all pair model metadata in
present model folder.
2022-07-27 05:27:24 +00:00
:return: bool - whether or not the drawer was located
"""
exists = self.pair_dictionary_path.is_file()
if exists:
with open(self.pair_dictionary_path, "r") as fp:
self.pair_dict = json.load(fp)
elif not self.follow_mode:
logger.info("Could not find existing datadrawer, starting from scratch")
else:
2022-07-03 08:59:38 +00:00
logger.warning(
f"Follower could not find pair_dictionary at {self.full_path} "
"sending null values back to strategy"
)
return exists
def load_historic_predictions_from_disk(self):
"""
Locate and load a previously saved historic predictions.
2022-07-27 05:27:24 +00:00
:return: bool - whether or not the drawer was located
"""
exists = self.historic_predictions_path.is_file()
if exists:
with open(self.historic_predictions_path, "rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.info(
f"Found existing historic predictions at {self.full_path}, but beware "
"that statistics may be inaccurate if the bot has been offline for "
"an extended period of time."
)
elif not self.follow_mode:
logger.info("Could not find existing historic_predictions, starting from scratch")
else:
logger.warning(
f"Follower could not find historic predictions at {self.full_path} "
"sending null values back to strategy"
)
return exists
2022-07-16 19:16:59 +00:00
def save_historic_predictions_to_disk(self):
"""
Save data drawer full of all pair model metadata in present model folder.
"""
with open(self.historic_predictions_path, "wb") as fp:
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
2022-07-16 19:16:59 +00:00
def save_drawer_to_disk(self):
"""
Save data drawer full of all pair model metadata in present model folder.
"""
2022-08-14 18:24:29 +00:00
with self.save_lock:
with open(self.pair_dictionary_path, 'w') as fp:
rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_follower_dict_to_disk(self):
"""
Save follower dictionary to disk (used by strategy for persistent prediction targets)
"""
with open(self.follower_dict_path, "w") as fp:
rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def create_follower_dict(self):
"""
Create or dictionary for each follower to maintain unique persistent prediction targets
"""
2022-07-03 08:59:38 +00:00
whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
exists = self.follower_dict_path.is_file()
if exists:
2022-07-03 08:59:38 +00:00
logger.info("Found an existing follower dictionary")
for pair in whitelist_pairs:
self.follower_dict[pair] = {}
2022-07-26 17:41:49 +00:00
self.save_follower_dict_to_disk()
def np_encoder(self, object):
if isinstance(object, np.generic):
return object.item()
def get_pair_dict_info(self, pair: str) -> Tuple[str, int, bool]:
"""
Locate and load existing model metadata from persistent storage. If not located,
create a new one and append the current pair to it and prepare it for its first
training
:param pair: str: pair to lookup
:return:
model_filename: str = unique filename used for loading persistent objects from disk
trained_timestamp: int = the last time the coin was trained
return_null_array: bool = Follower could not find pair metadata
"""
pair_dict = self.pair_dict.get(pair)
data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
return_null_array = False
if pair_dict:
model_filename = pair_dict["model_filename"]
trained_timestamp = pair_dict["trained_timestamp"]
elif not self.follow_mode:
self.pair_dict[pair] = self.empty_pair_dict.copy()
model_filename = ""
trained_timestamp = 0
self.pair_dict[pair]["priority"] = len(self.pair_dict)
if not data_path_set and self.follow_mode:
2022-07-03 08:59:38 +00:00
logger.warning(
f"Follower could not find current pair {pair} in "
f"pair_dictionary at path {self.full_path}, sending null values "
"back to strategy."
)
trained_timestamp = 0
model_filename = ''
return_null_array = True
return model_filename, trained_timestamp, return_null_array
def set_pair_dict_info(self, metadata: dict) -> None:
2022-07-03 08:59:38 +00:00
pair_in_dict = self.pair_dict.get(metadata["pair"])
if pair_in_dict:
return
else:
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
2022-07-03 08:59:38 +00:00
self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
return
def pair_to_end_of_training_queue(self, pair: str) -> None:
# march all pairs up in the queue
with self.pair_dict_lock:
for p in self.pair_dict:
self.pair_dict[p]["priority"] -= 1
# send pair to end of queue
self.pair_dict[pair]["priority"] = len(self.pair_dict)
2022-08-12 14:12:28 +00:00
def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
2022-07-02 16:09:38 +00:00
"""
2022-08-12 14:12:28 +00:00
Set the initial return values to the historical predictions dataframe. This avoids needing
to repredict on historical candles, and also stores historical predictions despite
retrainings (so stored predictions are true predictions, not just inferencing on trained
data)
2022-07-02 16:09:38 +00:00
"""
hist_df = self.historic_predictions
len_diff = len(hist_df[pair].index) - len(pred_df.index)
if len_diff < 0:
df_concat = pd.concat([pred_df.iloc[:abs(len_diff)], hist_df[pair]],
2022-08-12 14:12:28 +00:00
ignore_index=True, keys=hist_df[pair].keys())
else:
df_concat = hist_df[pair].tail(len(pred_df.index)).reset_index(drop=True)
df_concat = df_concat.fillna(0)
self.model_return_values[pair] = df_concat
def append_model_predictions(self, pair: str, predictions: DataFrame,
do_preds: NDArray[np.int_],
2022-07-28 05:07:40 +00:00
dk: FreqaiDataKitchen, len_df: int) -> None:
2022-08-12 14:12:28 +00:00
"""
Append model predictions to historic predictions dataframe, then set the
strategy return dataframe to the tail of the historic predictions. The length of
the tail is equivalent to the length of the dataframe that entered FreqAI from
the strategy originally. Doing this allows FreqUI to always display the correct
historic predictions.
"""
index = self.historic_predictions[pair].index[-1:]
columns = self.historic_predictions[pair].columns
nan_df = pd.DataFrame(np.nan, index=index, columns=columns)
self.historic_predictions[pair] = pd.concat(
[self.historic_predictions[pair], nan_df], ignore_index=True, axis=0)
df = self.historic_predictions[pair]
2022-08-12 14:12:28 +00:00
# model outputs and associated statistics
for label in predictions.columns:
2022-07-02 16:09:38 +00:00
df[label].iloc[-1] = predictions[label].iloc[-1]
if df[label].dtype == object:
continue
2022-07-03 08:59:38 +00:00
df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
2022-08-12 14:12:28 +00:00
# outlier indicators
2022-07-03 08:59:38 +00:00
df["do_predict"].iloc[-1] = do_preds[-1]
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
2022-07-03 08:59:38 +00:00
df["DI_values"].iloc[-1] = dk.DI_values[-1]
2022-08-12 14:12:28 +00:00
# extra values the user added within custom prediction model
if dk.data['extra_returns_per_train']:
rets = dk.data['extra_returns_per_train']
for return_str in rets:
df[return_str].iloc[-1] = rets[return_str]
self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True)
def attach_return_values_to_return_dataframe(
self, pair: str, dataframe: DataFrame) -> DataFrame:
2022-07-02 16:09:38 +00:00
"""
Attach the return values to the strat dataframe
:param dataframe: DataFrame = strategy dataframe
:return: DataFrame = strat dataframe with return values attached
2022-07-02 16:09:38 +00:00
"""
df = self.model_return_values[pair]
2022-07-03 08:59:38 +00:00
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
2022-07-02 16:09:38 +00:00
dataframe = pd.concat([dataframe[to_keep], df], axis=1)
return dataframe
2022-07-28 05:07:40 +00:00
def return_null_values_to_strategy(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> None:
2022-07-02 16:09:38 +00:00
"""
Build 0 filled dataframe to return to strategy
"""
2022-07-02 16:09:38 +00:00
dk.find_features(dataframe)
2022-08-10 13:16:50 +00:00
full_labels = dk.label_list + dk.unique_class_list
for label in full_labels:
2022-07-02 16:09:38 +00:00
dataframe[label] = 0
dataframe[f"{label}_mean"] = 0
dataframe[f"{label}_std"] = 0
2022-07-03 08:59:38 +00:00
dataframe["do_predict"] = 0
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
dataframe["DI_values"] = 0
if dk.data['extra_returns_per_train']:
rets = dk.data['extra_returns_per_train']
for return_str in rets:
dataframe[return_str] = 0
2022-07-02 16:09:38 +00:00
dk.return_dataframe = dataframe
def purge_old_models(self) -> None:
model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
pattern = re.compile(r"sub-train-(\w+)_(\d{10})")
delete_dict: Dict[str, Any] = {}
for dir in model_folders:
result = pattern.match(str(dir.name))
if result is None:
break
coin = result.group(1)
timestamp = result.group(2)
if coin not in delete_dict:
delete_dict[coin] = {}
2022-07-03 08:59:38 +00:00
delete_dict[coin]["num_folders"] = 1
delete_dict[coin]["timestamps"] = {int(timestamp): dir}
else:
2022-07-03 08:59:38 +00:00
delete_dict[coin]["num_folders"] += 1
delete_dict[coin]["timestamps"][int(timestamp)] = dir
for coin in delete_dict:
2022-07-03 08:59:38 +00:00
if delete_dict[coin]["num_folders"] > 2:
sorted_dict = collections.OrderedDict(
2022-07-03 08:59:38 +00:00
sorted(delete_dict[coin]["timestamps"].items())
)
num_delete = len(sorted_dict) - 2
deleted = 0
for k, v in sorted_dict.items():
if deleted >= num_delete:
break
2022-07-03 08:59:38 +00:00
logger.info(f"Freqai purging old model file {v}")
shutil.rmtree(v)
deleted += 1
def update_follower_metadata(self):
# follower needs to load from disk to get any changes made by leader to pair_dict
self.load_drawer_from_disk()
2022-07-03 08:59:38 +00:00
if self.config.get("freqai", {}).get("purge_old_models", False):
self.purge_old_models()
# Functions pulled back from FreqaiDataKitchen because they relied on DataDrawer
def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
"""
Saves all data associated with a model for a single sub-train time range
:params:
:model: User trained model which can be reused for inferencing to generate
predictions
"""
if not dk.data_path.is_dir():
dk.data_path.mkdir(parents=True, exist_ok=True)
save_path = Path(dk.data_path)
# Save the trained model
if not dk.keras:
dump(model, save_path / f"{dk.model_filename}_model.joblib")
else:
model.save(save_path / f"{dk.model_filename}_model.h5")
if dk.svm_model is not None:
dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
dk.data["data_path"] = str(dk.data_path)
dk.data["model_filename"] = str(dk.model_filename)
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
dk.data["label_list"] = dk.label_list
# store the metadata
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
# save the train data to file so we can check preds for area of applicability later
dk.data_dictionary["train_features"].to_pickle(
save_path / f"{dk.model_filename}_trained_df.pkl"
)
dk.data_dictionary["train_dates"].to_pickle(
save_path / f"{dk.model_filename}_trained_dates_df.pkl"
)
if self.freqai_info["feature_parameters"].get("principal_component_analysis"):
cloudpickle.dump(
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
)
# if self.live:
2022-08-22 11:30:30 +00:00
self.model_dictionary[coin] = model
self.pair_dict[coin]["model_filename"] = dk.model_filename
self.pair_dict[coin]["data_path"] = str(dk.data_path)
self.save_drawer_to_disk()
return
def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
"""
loads all data required to make a prediction on a sub-train time range
:returns:
:model: User trained model which can be inferenced for new predictions
"""
if not self.pair_dict[coin]["model_filename"]:
return None
if dk.live:
dk.model_filename = self.pair_dict[coin]["model_filename"]
dk.data_path = Path(self.pair_dict[coin]["data_path"])
if self.freqai_info.get("follow_mode", False):
2022-07-27 05:27:24 +00:00
# follower can be on a different system which is rsynced from the leader:
dk.data_path = Path(
self.config["user_data_dir"]
/ "models"
/ dk.data_path.parts[-2]
/ dk.data_path.parts[-1]
)
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
dk.data = json.load(fp)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
dk.data_dictionary["train_features"] = pd.read_pickle(
dk.data_path / f"{dk.model_filename}_trained_df.pkl"
)
# try to access model in memory instead of loading object from disk to save time
2022-08-22 11:30:30 +00:00
if dk.live and coin in self.model_dictionary:
model = self.model_dictionary[coin]
elif not dk.keras:
model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
else:
from tensorflow import keras
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
if not model:
raise OperationalException(
f"Unable to load model, ensure model exists at " f"{dk.data_path} "
)
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
dk.pca = cloudpickle.load(
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
)
return model
def update_historic_data(self, strategy: IStrategy, dk: FreqaiDataKitchen) -> None:
"""
Append new candles to our stores historic data (in memory) so that
we do not need to load candle history from disk and we dont need to
pinging exchange multiple times for the same candle.
:params:
dataframe: DataFrame = strategy provided dataframe
"""
feat_params = self.freqai_info["feature_parameters"]
with self.history_lock:
history_data = self.historic_data
for pair in dk.all_pairs:
for tf in feat_params.get("include_timeframes"):
# check if newest candle is already appended
df_dp = strategy.dp.get_pair_dataframe(pair, tf)
if len(df_dp.index) == 0:
continue
if str(history_data[pair][tf].iloc[-1]["date"]) == str(
df_dp.iloc[-1:]["date"].iloc[-1]
):
continue
try:
index = (
df_dp.loc[
df_dp["date"] == history_data[pair][tf].iloc[-1]["date"]
].index[0]
+ 1
)
except IndexError:
logger.warning(
f"Unable to update pair history for {pair}. "
"If this does not resolve itself after 1 additional candle, "
"please report the error to #freqai discord channel"
)
return
history_data[pair][tf] = pd.concat(
[
history_data[pair][tf],
df_dp.iloc[index:],
],
ignore_index=True,
axis=0,
)
def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
"""
Load pair histories for all whitelist and corr_pairlist pairs.
Only called once upon startup of bot.
:params:
timerange: TimeRange = full timerange required to populate all indicators
for training according to user defined train_period_days
"""
history_data = self.historic_data
for pair in dk.all_pairs:
if pair not in history_data:
history_data[pair] = {}
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
history_data[pair][tf] = load_pair_history(
datadir=self.config["datadir"],
timeframe=tf,
pair=pair,
timerange=timerange,
data_format=self.config.get("dataformat_ohlcv", "json"),
candle_type=self.config.get("trading_mode", "spot"),
)
def get_base_and_corr_dataframes(
self, timerange: TimeRange, pair: str, dk: FreqaiDataKitchen
) -> Tuple[Dict[Any, Any], Dict[Any, Any]]:
"""
Searches through our historic_data in memory and returns the dataframes relevant
to the present pair.
:params:
timerange: TimeRange = full timerange required to populate all indicators
for training according to user defined train_period_days
metadata: dict = strategy furnished pair metadata
"""
with self.history_lock:
corr_dataframes: Dict[Any, Any] = {}
base_dataframes: Dict[Any, Any] = {}
historic_data = self.historic_data
pairs = self.freqai_info["feature_parameters"].get(
"include_corr_pairlist", []
)
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf])
if pairs:
for p in pairs:
if pair in p:
continue # dont repeat anything from whitelist
if p not in corr_dataframes:
corr_dataframes[p] = {}
corr_dataframes[p][tf] = dk.slice_dataframe(
timerange, historic_data[p][tf]
)
return corr_dataframes, base_dataframes
# to be used if we want to send predictions directly to the follower instead of forcing
# follower to load models and inference
# def save_model_return_values_to_disk(self) -> None:
# with open(self.full_path / str('model_return_values.json'), "w") as fp:
# json.dump(self.model_return_values, fp, default=self.np_encoder)
2022-07-02 16:09:38 +00:00
# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
# if exists:
# with open(self.full_path / str('model_return_values.json'), "r") as fp:
# self.model_return_values = json.load(fp)
# elif not self.follow_mode:
# logger.info("Could not find existing datadrawer, starting from scratch")
# else:
# logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
# 'sending null values back to strategy')
2022-07-02 16:09:38 +00:00
# return exists, dk