integrate hyperopt and implement subcommand
This commit is contained in:
gcarq
parent
7fa5846c6b
commit
b9c4eafd96
@ -128,18 +128,20 @@ def parse_args(args: List[str]):
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def build_subcommands(parser: argparse.ArgumentParser) -> None:
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""" Builds and attaches all subcommands """
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from freqtrade.optimize import backtesting
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from freqtrade.optimize import backtesting, hyperopt
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subparsers = parser.add_subparsers(dest='subparser')
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backtest = subparsers.add_parser('backtesting', help='backtesting module')
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backtest.set_defaults(func=backtesting.start)
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backtest.add_argument(
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# Add backtesting subcommand
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backtesting_cmd = subparsers.add_parser('backtesting', help='backtesting module')
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backtesting_cmd.set_defaults(func=backtesting.start)
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backtesting_cmd.add_argument(
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'-l', '--live',
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action='store_true',
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dest='live',
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help='using live data',
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)
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backtest.add_argument(
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backtesting_cmd.add_argument(
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'-i', '--ticker-interval',
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help='specify ticker interval in minutes (default: 5)',
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dest='ticker_interval',
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@ -147,13 +149,17 @@ def build_subcommands(parser: argparse.ArgumentParser) -> None:
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type=int,
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metavar='INT',
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)
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backtest.add_argument(
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backtesting_cmd.add_argument(
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'--realistic-simulation',
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help='uses max_open_trades from config to simulate real world limitations',
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action='store_true',
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dest='realistic_simulation',
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)
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# Add hyperopt subcommand
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hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
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hyperopt_cmd.set_defaults(func=hyperopt.start)
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# Required json-schema for user specified config
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CONF_SCHEMA = {
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@ -1 +1,41 @@
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from . import backtesting
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# pragma pylint: disable=missing-docstring
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import json
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import os
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from typing import Optional, List, Dict
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from pandas import DataFrame
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from freqtrade.analyze import populate_indicators, parse_ticker_dataframe
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def load_data(ticker_interval: int = 5, pairs: Optional[List[str]] = None) -> Dict[str, List]:
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"""
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Loads ticker history data for the given parameters
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:param ticker_interval: ticker interval in minutes
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:param pairs: list of pairs
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:return: dict
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"""
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path = os.path.abspath(os.path.dirname(__file__))
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result = {}
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_pairs = pairs or [
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'BTC_BCC', 'BTC_ETH', 'BTC_DASH', 'BTC_POWR', 'BTC_ETC',
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'BTC_VTC', 'BTC_WAVES', 'BTC_LSK', 'BTC_XLM', 'BTC_OK',
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]
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for pair in _pairs:
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with open('{abspath}/../tests/testdata/{pair}-{ticker_interval}.json'.format(
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abspath=path,
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pair=pair,
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ticker_interval=ticker_interval,
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)) as tickerdata:
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result[pair] = json.load(tickerdata)
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return result
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def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
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"""Creates a dataframe and populates indicators for given ticker data"""
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processed = {}
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for pair, pair_data in tickerdata.items():
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processed[pair] = populate_indicators(parse_ticker_dataframe(pair_data))
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return processed
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@ -9,34 +9,17 @@ from pandas import DataFrame
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from tabulate import tabulate
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from freqtrade import exchange
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from freqtrade.analyze import parse_ticker_dataframe, populate_indicators, \
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populate_buy_trend, populate_sell_trend
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from freqtrade.analyze import populate_buy_trend, populate_sell_trend
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from freqtrade.exchange import Bittrex
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from freqtrade.main import min_roi_reached
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from freqtrade.misc import load_config
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from freqtrade.optimize import load_data, preprocess
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from freqtrade.persistence import Trade
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from freqtrade.tests import load_backtesting_data
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logger = logging.getLogger(__name__)
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def format_results(results: DataFrame):
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return ('Made {:6d} buys. Average profit {: 5.2f}%. '
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'Total profit was {: 7.3f}. Average duration {:5.1f} mins.').format(
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len(results.index),
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results.profit.mean() * 100.0,
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results.profit.sum(),
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results.duration.mean() * 5,
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)
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def preprocess(backdata) -> Dict[str, DataFrame]:
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processed = {}
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for pair, pair_data in backdata.items():
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processed[pair] = populate_indicators(parse_ticker_dataframe(pair_data))
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return processed
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def get_timeframe(data: Dict[str, Dict]) -> Tuple[arrow.Arrow, arrow.Arrow]:
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"""
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Get the maximum timeframe for the given backtest data
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@ -151,7 +134,7 @@ def start(args):
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data[pair] = exchange.get_ticker_history(pair, args.ticker_interval)
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else:
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print('Using local backtesting data (ignoring whitelist in given config)...')
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data = load_backtesting_data(args.ticker_interval)
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data = load_data(args.ticker_interval)
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print('Using stake_currency: {} ...\nUsing stake_amount: {} ...'.format(
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config['stake_currency'], config['stake_amount']
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@ -1,29 +1,124 @@
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# pragma pylint: disable=missing-docstring,W0212
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import logging
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import os
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from functools import reduce
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from math import exp
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from operator import itemgetter
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import pytest
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from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
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from pandas import DataFrame
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from freqtrade import exchange
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from freqtrade import exchange, optimize
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from freqtrade.exchange import Bittrex
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from freqtrade.optimize.backtesting import backtest, format_results
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from freqtrade.optimize.backtesting import preprocess
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from freqtrade.tests import load_backtesting_data
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from freqtrade.optimize.backtesting import backtest
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from freqtrade.vendor.qtpylib.indicators import crossed_above
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logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
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# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
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TARGET_TRADES = 1100
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TOTAL_TRIES = 4
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# pylint: disable=C0103
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current_tries = 0
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# Configuration and data used by hyperopt
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PROCESSED = optimize.preprocess(optimize.load_data())
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OPTIMIZE_CONFIG = {
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'max_open_trades': 3,
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'stake_currency': 'BTC',
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'stake_amount': 0.01,
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'minimal_roi': {
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'40': 0.0,
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'30': 0.01,
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'20': 0.02,
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'0': 0.04,
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},
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'stoploss': -0.10,
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}
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SPACE = {
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'mfi': hp.choice('mfi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
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]),
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'fastd': hp.choice('fastd', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
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]),
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'adx': hp.choice('adx', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
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]),
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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'uptrend_long_ema': hp.choice('uptrend_long_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_short_ema': hp.choice('uptrend_short_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'over_sar': hp.choice('over_sar', [
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{'enabled': False},
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{'enabled': True}
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]),
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'green_candle': hp.choice('green_candle', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_sma': hp.choice('uptrend_sma', [
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{'enabled': False},
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{'enabled': True}
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]),
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'trigger': hp.choice('trigger', [
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{'type': 'lower_bb'},
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{'type': 'faststoch10'},
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{'type': 'ao_cross_zero'},
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{'type': 'ema5_cross_ema10'},
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{'type': 'macd_cross_signal'},
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{'type': 'sar_reversal'},
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{'type': 'stochf_cross'},
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{'type': 'ht_sine'},
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]),
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}
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def optimizer(params):
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from freqtrade.optimize import backtesting
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backtesting.populate_buy_trend = buy_strategy_generator(params)
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results = backtest(OPTIMIZE_CONFIG, PROCESSED)
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result = format_results(results)
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total_profit = results.profit.sum() * 1000
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trade_count = len(results.index)
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trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
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profit_loss = max(0, 1 - total_profit / 10000) # max profit 10000
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# pylint: disable=W0603
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global current_tries
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current_tries += 1
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print('{:5d}/{}: {}'.format(current_tries, TOTAL_TRIES, result))
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return {
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'loss': trade_loss + profit_loss,
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'status': STATUS_OK,
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'result': result
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}
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def format_results(results: DataFrame):
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return ('Made {:6d} buys. Average profit {: 5.2f}%. '
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'Total profit was {: 7.3f}. Average duration {:5.1f} mins.').format(
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len(results.index),
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results.profit.mean() * 100.0,
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results.profit.sum(),
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results.duration.mean() * 5,
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)
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def buy_strategy_generator(params):
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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@ -70,94 +165,14 @@ def buy_strategy_generator(params):
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return populate_buy_trend
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@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
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def test_hyperopt(backtest_conf, mocker):
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mocked_buy_trend = mocker.patch('freqtrade.tests.test_backtesting.populate_buy_trend')
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def start(args):
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# TODO: parse args
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backdata = load_backtesting_data()
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processed = preprocess(backdata)
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exchange._API = Bittrex({'key': '', 'secret': ''})
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def optimizer(params):
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mocked_buy_trend.side_effect = buy_strategy_generator(params)
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results = backtest(backtest_conf, processed, mocker)
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result = format_results(results)
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total_profit = results.profit.sum() * 1000
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trade_count = len(results.index)
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trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
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profit_loss = max(0, 1 - total_profit / 10000) # max profit 10000
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# pylint: disable=W0603
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global current_tries
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current_tries += 1
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print('{:5d}/{}: {}'.format(current_tries, TOTAL_TRIES, result))
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return {
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'loss': trade_loss + profit_loss,
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'status': STATUS_OK,
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'result': result
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}
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space = {
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'mfi': hp.choice('mfi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
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]),
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'fastd': hp.choice('fastd', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
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]),
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'adx': hp.choice('adx', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
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]),
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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'uptrend_long_ema': hp.choice('uptrend_long_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_short_ema': hp.choice('uptrend_short_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'over_sar': hp.choice('over_sar', [
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{'enabled': False},
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{'enabled': True}
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]),
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'green_candle': hp.choice('green_candle', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_sma': hp.choice('uptrend_sma', [
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{'enabled': False},
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{'enabled': True}
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]),
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'trigger': hp.choice('trigger', [
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{'type': 'lower_bb'},
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{'type': 'faststoch10'},
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{'type': 'ao_cross_zero'},
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{'type': 'ema5_cross_ema10'},
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{'type': 'macd_cross_signal'},
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{'type': 'sar_reversal'},
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{'type': 'stochf_cross'},
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{'type': 'ht_sine'},
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]),
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}
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trials = Trials()
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best = fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=TOTAL_TRIES, trials=trials)
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best = fmin(fn=optimizer, space=SPACE, algo=tpe.suggest, max_evals=TOTAL_TRIES, trials=trials)
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print('\n\n\n\n==================== HYPEROPT BACKTESTING REPORT ==============================')
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print('Best parameters {}'.format(best))
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newlist = sorted(trials.results, key=itemgetter('loss'))
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print('Result: {}'.format(newlist[0]['result']))
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if __name__ == '__main__':
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# for profiling with cProfile and line_profiler
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pytest.main([__file__, '-s'])
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@ -1,21 +0,0 @@
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# pragma pylint: disable=missing-docstring
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import json
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import os
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from typing import Optional, List
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def load_backtesting_data(ticker_interval: int = 5, pairs: Optional[List[str]] = None):
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path = os.path.abspath(os.path.dirname(__file__))
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result = {}
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_pairs = pairs or [
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'BTC_BCC', 'BTC_ETH', 'BTC_DASH', 'BTC_POWR', 'BTC_ETC',
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'BTC_VTC', 'BTC_WAVES', 'BTC_LSK', 'BTC_XLM', 'BTC_OK',
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]
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for pair in _pairs:
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with open('{abspath}/testdata/{pair}-{ticker_interval}.json'.format(
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abspath=path,
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pair=pair,
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ticker_interval=ticker_interval,
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)) as tickerdata:
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result[pair] = json.load(tickerdata)
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return result
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@ -51,22 +51,6 @@ def default_conf():
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return configuration
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@pytest.fixture(scope="module")
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def backtest_conf():
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return {
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"max_open_trades": 3,
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"stake_currency": "BTC",
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"stake_amount": 0.01,
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"minimal_roi": {
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"40": 0.0,
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"30": 0.01,
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"20": 0.02,
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"0": 0.04
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},
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"stoploss": -0.10
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}
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@pytest.fixture
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def update():
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_update = Update(0)
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@ -78,10 +78,10 @@ def test_parse_args_backtesting(mocker):
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def test_parse_args_backtesting_invalid():
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with pytest.raises(SystemExit, match=r'2'):
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parse_args(['--ticker-interval'])
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parse_args(['backtesting --ticker-interval'])
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with pytest.raises(SystemExit, match=r'2'):
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parse_args(['--ticker-interval', 'abc'])
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parse_args(['backtesting --ticker-interval', 'abc'])
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def test_parse_args_backtesting_custom(mocker):
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@ -99,6 +99,19 @@ def test_parse_args_backtesting_custom(mocker):
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assert call_args.ticker_interval == 1
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def test_parse_args_hyperopt(mocker):
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hyperopt_mock = mocker.patch('freqtrade.optimize.hyperopt.start', MagicMock())
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args = parse_args(['hyperopt'])
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assert args is None
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assert hyperopt_mock.call_count == 1
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call_args = hyperopt_mock.call_args[0][0]
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assert call_args.config == 'config.json'
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assert call_args.loglevel == 20
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assert call_args.subparser == 'hyperopt'
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assert call_args.func is not None
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def test_load_config(default_conf, mocker):
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file_mock = mocker.patch('freqtrade.misc.open', mocker.mock_open(
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read_data=json.dumps(default_conf)
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@ -1,18 +1,16 @@
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# pragma pylint: disable=missing-docstring,W0212
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from freqtrade import exchange
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from freqtrade import exchange, optimize
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from freqtrade.exchange import Bittrex
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from freqtrade.optimize.backtesting import backtest, preprocess
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from freqtrade.tests import load_backtesting_data
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from freqtrade.optimize.backtesting import backtest
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def test_backtest(backtest_conf, mocker):
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mocker.patch.dict('freqtrade.main._CONF', backtest_conf)
|
||||
def test_backtest(default_conf, mocker):
|
||||
mocker.patch.dict('freqtrade.main._CONF', default_conf)
|
||||
exchange._API = Bittrex({'key': '', 'secret': ''})
|
||||
|
||||
data = load_backtesting_data(ticker_interval=5, pairs=['BTC_ETH'])
|
||||
results = backtest(backtest_conf, preprocess(data), 10, True)
|
||||
data = optimize.load_data(ticker_interval=5, pairs=['BTC_ETH'])
|
||||
results = backtest(default_conf, optimize.preprocess(data), 10, True)
|
||||
num_resutls = len(results)
|
||||
assert num_resutls > 0
|
||||
|
||||
|
||||
6
freqtrade/tests/test_optimize_hyperopt.py
Normal file
6
freqtrade/tests/test_optimize_hyperopt.py
Normal file
@ -0,0 +1,6 @@
|
||||
# pragma pylint: disable=missing-docstring,W0212
|
||||
|
||||
|
||||
def test_optimizer(default_conf, mocker):
|
||||
# TODO: implement test
|
||||
pass
|
||||
Loading…
Reference in New Issue
Block a user