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add timestamps to each metric, use rapidjson

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This commit is contained in:
robcaulk 2022-10-15 13:23:01 +02:00
parent b236e362ba
commit d81eef0b70
2 changed files with 13 additions and 30 deletions
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37
freqtrade/freqai/data_drawer.py
View File

@ -1,9 +1,9 @@
import collections
import json
import logging
import re
import shutil
import threading
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, Tuple, TypedDict
@ -95,7 +95,7 @@ class FreqaiDataDrawer:
self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0,
"data_path": "", "extras": {}}
self.metric_tracker: Dict[str, Dict[str, list]] = {}
self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
"""
@ -106,9 +106,11 @@ class FreqaiDataDrawer:
if pair not in self.metric_tracker:
self.metric_tracker[pair] = {}
if metric not in self.metric_tracker[pair]:
self.metric_tracker[pair][metric] = []
self.metric_tracker[pair][metric] = {'timestamp': [], 'value': []}
self.metric_tracker[pair][metric].append(value)
timestamp = int(datetime.now(timezone.utc).timestamp())
self.metric_tracker[pair][metric]['value'].append(value)
self.metric_tracker[pair][metric]['timestamp'].append(timestamp)
def collect_metrics(self, time_spent: float, pair: str):
"""
@ -130,7 +132,7 @@ class FreqaiDataDrawer:
exists = self.pair_dictionary_path.is_file()
if exists:
with open(self.pair_dictionary_path, "r") as fp:
self.pair_dict = json.load(fp)
self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
elif not self.follow_mode:
logger.info("Could not find existing datadrawer, starting from scratch")
else:
@ -148,7 +150,7 @@ class FreqaiDataDrawer:
exists = self.metric_tracker_path.is_file()
if exists:
with open(self.metric_tracker_path, "r") as fp:
self.metric_tracker = json.load(fp)
self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
else:
logger.info("Could not find existing metric tracker, starting from scratch")
@ -515,7 +517,7 @@ class FreqaiDataDrawer:
presaved backtesting (prediction file loading).
"""
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
dk.data = json.load(fp)
dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
@ -542,7 +544,7 @@ class FreqaiDataDrawer:
)
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
dk.data = json.load(fp)
dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
@ -676,22 +678,3 @@ class FreqaiDataDrawer:
).reset_index(drop=True)
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)
# 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')
# return exists, dk

6
freqtrade/freqai/freqai_interface.py
View File

@ -7,7 +7,7 @@ from collections import deque
from datetime import datetime, timezone
from pathlib import Path
from threading import Lock
from typing import Any, Dict, List, Tuple
from typing import Any, Dict, List, Literal, Tuple
import numpy as np
import pandas as pd
@ -657,7 +657,7 @@ class IFreqaiModel(ABC):
return
def inference_timer(self, do: str = 'start', pair: str = ''):
def inference_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
"""
Timer designed to track the cumulative time spent in FreqAI for one pass through
the whitelist. This will check if the time spent is more than 1/4 the time
@ -682,7 +682,7 @@ class IFreqaiModel(ABC):
self.inference_time = 0
return
def train_timer(self, do: str = 'start', pair: str = ''):
def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
"""
Timer designed to track the cumulative time spent training the full pairlist in
FreqAI.