mirror of
https://github.com/freqtrade/freqtrade.git
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324 lines
10 KiB
Python
324 lines
10 KiB
Python
"""
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Various tool function for Freqtrade and scripts
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"""
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import gzip
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import logging
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import re
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, Iterator, List, Mapping, Optional, Union
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from typing.io import IO
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from urllib.parse import urlparse
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import orjson
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import pandas as pd
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import rapidjson
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from freqtrade.constants import DECIMAL_PER_COIN_FALLBACK, DECIMALS_PER_COIN
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from freqtrade.enums import SignalTagType, SignalType
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logger = logging.getLogger(__name__)
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def decimals_per_coin(coin: str):
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"""
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Helper method getting decimal amount for this coin
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example usage: f".{decimals_per_coin('USD')}f"
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:param coin: Which coin are we printing the price / value for
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"""
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return DECIMALS_PER_COIN.get(coin, DECIMAL_PER_COIN_FALLBACK)
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def round_coin_value(
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value: float, coin: str, show_coin_name=True, keep_trailing_zeros=False) -> str:
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"""
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Get price value for this coin
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:param value: Value to be printed
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:param coin: Which coin are we printing the price / value for
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:param show_coin_name: Return string in format: "222.22 USDT" or "222.22"
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:param keep_trailing_zeros: Keep trailing zeros "222.200" vs. "222.2"
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:return: Formatted / rounded value (with or without coin name)
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"""
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val = f"{value:.{decimals_per_coin(coin)}f}"
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if not keep_trailing_zeros:
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val = val.rstrip('0').rstrip('.')
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if show_coin_name:
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val = f"{val} {coin}"
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return val
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def shorten_date(_date: str) -> str:
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"""
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Trim the date so it fits on small screens
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"""
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new_date = re.sub('seconds?', 'sec', _date)
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new_date = re.sub('minutes?', 'min', new_date)
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new_date = re.sub('hours?', 'h', new_date)
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new_date = re.sub('days?', 'd', new_date)
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new_date = re.sub('^an?', '1', new_date)
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return new_date
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def file_dump_json(filename: Path, data: Any, is_zip: bool = False, log: bool = True) -> None:
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"""
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Dump JSON data into a file
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:param filename: file to create
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:param is_zip: if file should be zip
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:param data: JSON Data to save
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:return:
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"""
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if is_zip:
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if filename.suffix != '.gz':
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filename = filename.with_suffix('.gz')
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if log:
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logger.info(f'dumping json to "{filename}"')
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with gzip.open(filename, 'w') as fpz:
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rapidjson.dump(data, fpz, default=str, number_mode=rapidjson.NM_NATIVE)
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else:
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if log:
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logger.info(f'dumping json to "{filename}"')
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with filename.open('w') as fp:
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rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
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logger.debug(f'done json to "{filename}"')
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def file_dump_joblib(filename: Path, data: Any, log: bool = True) -> None:
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"""
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Dump object data into a file
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:param filename: file to create
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:param data: Object data to save
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:return:
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"""
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import joblib
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if log:
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logger.info(f'dumping joblib to "{filename}"')
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with filename.open('wb') as fp:
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joblib.dump(data, fp)
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logger.debug(f'done joblib dump to "{filename}"')
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def json_load(datafile: IO) -> Any:
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"""
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load data with rapidjson
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Use this to have a consistent experience,
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set number_mode to "NM_NATIVE" for greatest speed
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"""
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return rapidjson.load(datafile, number_mode=rapidjson.NM_NATIVE)
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def file_load_json(file: Path):
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if file.suffix != ".gz":
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gzipfile = file.with_suffix(file.suffix + '.gz')
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else:
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gzipfile = file
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# Try gzip file first, otherwise regular json file.
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if gzipfile.is_file():
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logger.debug(f"Loading historical data from file {gzipfile}")
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with gzip.open(gzipfile) as datafile:
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pairdata = json_load(datafile)
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elif file.is_file():
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logger.debug(f"Loading historical data from file {file}")
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with file.open() as datafile:
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pairdata = json_load(datafile)
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else:
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return None
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return pairdata
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def pair_to_filename(pair: str) -> str:
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for ch in ['/', ' ', '.', '@', '$', '+', ':']:
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pair = pair.replace(ch, '_')
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return pair
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def format_ms_time(date: int) -> str:
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"""
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convert MS date to readable format.
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: epoch-string in ms
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"""
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return datetime.fromtimestamp(date / 1000.0).strftime('%Y-%m-%dT%H:%M:%S')
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def deep_merge_dicts(source, destination, allow_null_overrides: bool = True):
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"""
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Values from Source override destination, destination is returned (and modified!!)
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Sample:
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>>> a = { 'first' : { 'rows' : { 'pass' : 'dog', 'number' : '1' } } }
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>>> b = { 'first' : { 'rows' : { 'fail' : 'cat', 'number' : '5' } } }
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>>> merge(b, a) == { 'first' : { 'rows' : { 'pass' : 'dog', 'fail' : 'cat', 'number' : '5' } } }
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True
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"""
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for key, value in source.items():
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if isinstance(value, dict):
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# get node or create one
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node = destination.setdefault(key, {})
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deep_merge_dicts(value, node, allow_null_overrides)
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elif value is not None or allow_null_overrides:
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destination[key] = value
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return destination
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def round_dict(d, n):
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"""
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Rounds float values in the dict to n digits after the decimal point.
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"""
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return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
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def safe_value_fallback(obj: dict, key1: str, key2: str, default_value=None):
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"""
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Search a value in obj, return this if it's not None.
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Then search key2 in obj - return that if it's not none - then use default_value.
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Else falls back to None.
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"""
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if key1 in obj and obj[key1] is not None:
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return obj[key1]
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else:
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if key2 in obj and obj[key2] is not None:
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return obj[key2]
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return default_value
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dictMap = Union[Dict[str, Any], Mapping[str, Any]]
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def safe_value_fallback2(dict1: dictMap, dict2: dictMap, key1: str, key2: str, default_value=None):
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"""
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Search a value in dict1, return this if it's not None.
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Fall back to dict2 - return key2 from dict2 if it's not None.
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Else falls back to None.
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"""
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if key1 in dict1 and dict1[key1] is not None:
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return dict1[key1]
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else:
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if key2 in dict2 and dict2[key2] is not None:
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return dict2[key2]
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return default_value
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def plural(num: float, singular: str, plural: Optional[str] = None) -> str:
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return singular if (num == 1 or num == -1) else plural or singular + 's'
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def render_template(templatefile: str, arguments: dict = {}) -> str:
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from jinja2 import Environment, PackageLoader, select_autoescape
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env = Environment(
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loader=PackageLoader('freqtrade', 'templates'),
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autoescape=select_autoescape(['html', 'xml'])
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)
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template = env.get_template(templatefile)
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return template.render(**arguments)
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def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
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arguments: dict = {}) -> str:
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"""
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Use templatefile if possible, otherwise fall back to templatefallbackfile
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"""
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from jinja2.exceptions import TemplateNotFound
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try:
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return render_template(templatefile, arguments)
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except TemplateNotFound:
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return render_template(templatefallbackfile, arguments)
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def chunks(lst: List[Any], n: int) -> Iterator[List[Any]]:
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"""
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Split lst into chunks of the size n.
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:param lst: list to split into chunks
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:param n: number of max elements per chunk
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:return: None
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"""
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for chunk in range(0, len(lst), n):
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yield (lst[chunk:chunk + n])
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def parse_db_uri_for_logging(uri: str):
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"""
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Helper method to parse the DB URI and return the same DB URI with the password censored
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if it contains it. Otherwise, return the DB URI unchanged
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:param uri: DB URI to parse for logging
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"""
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parsed_db_uri = urlparse(uri)
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if not parsed_db_uri.netloc: # No need for censoring as no password was provided
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return uri
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pwd = parsed_db_uri.netloc.split(':')[1].split('@')[0]
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return parsed_db_uri.geturl().replace(f':{pwd}@', ':*****@')
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def dataframe_to_json(dataframe: pd.DataFrame) -> str:
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"""
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Serialize a DataFrame for transmission over the wire using JSON
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:param dataframe: A pandas DataFrame
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:returns: A JSON string of the pandas DataFrame
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"""
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# https://github.com/pandas-dev/pandas/issues/24889
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# https://github.com/pandas-dev/pandas/issues/40443
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# We need to convert to a dict to avoid mem leak
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def default(z):
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if isinstance(z, pd.Timestamp):
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return z.timestamp() * 1e3
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if z is pd.NaT:
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return 'NaT'
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raise TypeError
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return str(orjson.dumps(dataframe.to_dict(orient='split'), default=default), 'utf-8')
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def json_to_dataframe(data: str) -> pd.DataFrame:
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"""
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Deserialize JSON into a DataFrame
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:param data: A JSON string
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:returns: A pandas DataFrame from the JSON string
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"""
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dataframe = pd.read_json(data, orient='split')
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if 'date' in dataframe.columns:
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dataframe['date'] = pd.to_datetime(dataframe['date'], unit='ms', utc=True)
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return dataframe
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def remove_entry_exit_signals(dataframe: pd.DataFrame):
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"""
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Remove Entry and Exit signals from a DataFrame
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:param dataframe: The DataFrame to remove signals from
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"""
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dataframe[SignalType.ENTER_LONG.value] = 0
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dataframe[SignalType.EXIT_LONG.value] = 0
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dataframe[SignalType.ENTER_SHORT.value] = 0
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dataframe[SignalType.EXIT_SHORT.value] = 0
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dataframe[SignalTagType.ENTER_TAG.value] = None
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dataframe[SignalTagType.EXIT_TAG.value] = None
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return dataframe
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def append_candles_to_dataframe(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
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"""
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Append the `right` dataframe to the `left` dataframe
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:param left: The full dataframe you want appended to
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:param right: The new dataframe containing the data you want appended
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:returns: The dataframe with the right data in it
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"""
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if left.iloc[-1]['date'] != right.iloc[-1]['date']:
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left = pd.concat([left, right])
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# Only keep the last 1500 candles in memory
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left = left[-1500:] if len(left) > 1500 else left
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left.reset_index(drop=True, inplace=True)
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return left
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