stable/freqtrade/optimize/__init__.py
kryofly 60ed4b9d1e --datadir <path> argument
This argument enables usage of different backtesting directories.
Useful if one wants compare backtesting performance over time.
2018-01-06 23:24:35 +01:00

134 lines
4.5 KiB
Python

# pragma pylint: disable=missing-docstring
import logging
import json
import os
from typing import Optional, List, Dict
from pandas import DataFrame
from freqtrade.exchange import get_ticker_history
from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
from freqtrade.analyze import populate_indicators, parse_ticker_dataframe
logger = logging.getLogger(__name__)
def load_tickerdata_file(datadir, pair, ticker_interval):
"""
Load a pair from file,
:return dict OR empty if unsuccesful
"""
path = make_testdata_path(datadir)
file = '{abspath}/{pair}-{ticker_interval}.json'.format(
abspath=path,
pair=pair,
ticker_interval=ticker_interval,
)
# The file does not exist we download it
if not os.path.isfile(file):
return None
# Read the file, load the json
with open(file) as tickerdata:
pairdata = json.load(tickerdata)
return pairdata
def load_data(datadir: str, ticker_interval: int = 5, pairs: Optional[List[str]] = None,
refresh_pairs: Optional[bool] = False) -> Dict[str, List]:
"""
Loads ticker history data for the given parameters
:param ticker_interval: ticker interval in minutes
:param pairs: list of pairs
:return: dict
"""
result = {}
_pairs = pairs or hyperopt_optimize_conf()['exchange']['pair_whitelist']
# If the user force the refresh of pairs
if refresh_pairs:
logger.info('Download data for all pairs and store them in %s', datadir)
download_pairs(datadir, _pairs)
for pair in _pairs:
pairdata = load_tickerdata_file(datadir, pair, ticker_interval)
if not pairdata:
# download the tickerdata from exchange
download_backtesting_testdata(datadir, pair=pair, interval=ticker_interval)
# and retry reading the pair
pairdata = load_tickerdata_file(datadir, pair, ticker_interval)
result[pair] = pairdata
return result
def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""Creates a dataframe and populates indicators for given ticker data"""
return {pair: populate_indicators(parse_ticker_dataframe(pair_data))
for pair, pair_data in tickerdata.items()}
def make_testdata_path(datadir: str) -> str:
"""Return the path where testdata files are stored"""
return datadir or os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', 'tests', 'testdata'))
def download_pairs(datadir, pairs: List[str]) -> bool:
"""For each pairs passed in parameters, download 1 and 5 ticker intervals"""
for pair in pairs:
try:
for interval in [1, 5]:
download_backtesting_testdata(datadir, pair=pair, interval=interval)
except BaseException:
logger.info('Failed to download the pair: "{pair}", Interval: {interval} min'.format(
pair=pair,
interval=interval,
))
return False
return True
def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) -> bool:
"""
Download the latest 1 and 5 ticker intervals from Bittrex for the pairs passed in parameters
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pairs: list of pairs to download
:return: bool
"""
path = make_testdata_path(datadir)
logger.info('Download the pair: "{pair}", Interval: {interval} min'.format(
pair=pair,
interval=interval,
))
filepair = pair.replace("-", "_")
filename = os.path.join(path, '{pair}-{interval}.json'.format(
pair=filepair,
interval=interval,
))
filename = filename.replace('USDT_BTC', 'BTC_FAKEBULL')
if os.path.isfile(filename):
with open(filename, "rt") as fp:
data = json.load(fp)
logger.debug("Current Start: {}".format(data[1]['T']))
logger.debug("Current End: {}".format(data[-1:][0]['T']))
else:
data = []
logger.debug("Current Start: None")
logger.debug("Current End: None")
new_data = get_ticker_history(pair=pair, tick_interval=int(interval))
for row in new_data:
if row not in data:
data.append(row)
logger.debug("New Start: {}".format(data[1]['T']))
logger.debug("New End: {}".format(data[-1:][0]['T']))
data = sorted(data, key=lambda data: data['T'])
with open(filename, "wt") as fp:
json.dump(data, fp)
return True