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Merge branch 'develop' into logging-syslog

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This commit is contained in:
hroff-1902
2019-11-30 21:38:50 +03:00
committed by GitHub
parent c7c7a1c2aa e0e0bad7c1
commit 36b2ed172c
151 changed files with 5355 additions and 2299 deletions
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1
freqtrade/configuration/__init__.py
View File

@@ -1,4 +1,5 @@
from freqtrade.configuration.arguments import Arguments # noqa: F401
from freqtrade.configuration.check_exchange import check_exchange, remove_credentials # noqa: F401
from freqtrade.configuration.timerange import TimeRange # noqa: F401
from freqtrade.configuration.configuration import Configuration # noqa: F401
from freqtrade.configuration.config_validation import validate_config_consistency # noqa: F401

113
freqtrade/configuration/arguments.py
View File

@@ -13,7 +13,7 @@ ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_dat
ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_MAIN = ARGS_COMMON + ARGS_STRATEGY + ["db_url", "sd_notify"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
"max_open_trades", "stake_amount", "fee"]
@@ -37,13 +37,18 @@ ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one_column",
"print_csv", "base_currencies", "quote_currencies", "list_pairs_all"]
ARGS_CREATE_USERDIR = ["user_data_dir"]
ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
"timeframes", "erase"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit", "db_url",
"trade_source", "export", "exportfilename", "timerange", "ticker_interval"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
"db_url", "trade_source", "export", "exportfilename",
"timerange", "ticker_interval"]
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "ticker_interval"]
@@ -51,7 +56,7 @@ ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
"plot-dataframe", "plot-profit"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
class Arguments:
@@ -61,11 +66,6 @@ class Arguments:
def __init__(self, args: Optional[List[str]]) -> None:
self.args = args
self._parsed_arg: Optional[argparse.Namespace] = None
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
def _load_args(self) -> None:
self._build_args(optionlist=ARGS_MAIN)
self._build_subcommands()
def get_parsed_arg(self) -> Dict[str, Any]:
"""
@@ -73,7 +73,7 @@ class Arguments:
:return: List[str] List of arguments
"""
if self._parsed_arg is None:
self._load_args()
self._build_subcommands()
self._parsed_arg = self._parse_args()
return vars(self._parsed_arg)
@@ -84,22 +84,17 @@ class Arguments:
"""
parsed_arg = self.parser.parse_args(self.args)
# When no config is provided, but a config exists, use that configuration!
subparser = parsed_arg.subparser if 'subparser' in parsed_arg else None
# Workaround issue in argparse with action='append' and default value
# (see https://bugs.python.org/issue16399)
# Allow no-config for certain commands (like downloading / plotting)
if (parsed_arg.config is None
and subparser not in NO_CONF_ALLOWED
and ((Path.cwd() / constants.DEFAULT_CONFIG).is_file()
or (subparser not in NO_CONF_REQURIED))):
if ('config' in parsed_arg and parsed_arg.config is None and
((Path.cwd() / constants.DEFAULT_CONFIG).is_file() or
not ('command' in parsed_arg and parsed_arg.command in NO_CONF_REQURIED))):
parsed_arg.config = [constants.DEFAULT_CONFIG]
return parsed_arg
def _build_args(self, optionlist, parser=None):
parser = parser or self.parser
def _build_args(self, optionlist, parser):
for val in optionlist:
opt = AVAILABLE_CLI_OPTIONS[val]
@@ -110,38 +105,81 @@ class Arguments:
Builds and attaches all subcommands.
:return: None
"""
# Build shared arguments (as group Common Options)
_common_parser = argparse.ArgumentParser(add_help=False)
group = _common_parser.add_argument_group("Common arguments")
self._build_args(optionlist=ARGS_COMMON, parser=group)
_strategy_parser = argparse.ArgumentParser(add_help=False)
strategy_group = _strategy_parser.add_argument_group("Strategy arguments")
self._build_args(optionlist=ARGS_STRATEGY, parser=strategy_group)
# Build main command
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
from freqtrade.utils import (start_create_userdir, start_download_data,
start_list_exchanges, start_list_timeframes,
start_list_markets)
start_list_exchanges, start_list_markets,
start_new_hyperopt, start_new_strategy,
start_list_timeframes, start_trading)
from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
subparsers = self.parser.add_subparsers(dest='subparser')
subparsers = self.parser.add_subparsers(dest='command',
# Use custom message when no subhandler is added
# shown from `main.py`
# required=True
)
# Add trade subcommand
trade_cmd = subparsers.add_parser('trade', help='Trade module.',
parents=[_common_parser, _strategy_parser])
trade_cmd.set_defaults(func=start_trading)
self._build_args(optionlist=ARGS_TRADE, parser=trade_cmd)
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.')
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.',
parents=[_common_parser, _strategy_parser])
backtesting_cmd.set_defaults(func=start_backtesting)
self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.')
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])
edge_cmd.set_defaults(func=start_edge)
self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.')
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.',
parents=[_common_parser, _strategy_parser],
)
hyperopt_cmd.set_defaults(func=start_hyperopt)
self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
# add create-userdir subcommand
create_userdir_cmd = subparsers.add_parser('create-userdir',
help="Create user-data directory.")
help="Create user-data directory.",
)
create_userdir_cmd.set_defaults(func=start_create_userdir)
self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
# add new-strategy subcommand
build_strategy_cmd = subparsers.add_parser('new-strategy',
help="Create new strategy")
build_strategy_cmd.set_defaults(func=start_new_strategy)
self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
# add new-hyperopt subcommand
build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
help="Create new hyperopt")
build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
# Add list-exchanges subcommand
list_exchanges_cmd = subparsers.add_parser(
'list-exchanges',
help='Print available exchanges.'
help='Print available exchanges.',
parents=[_common_parser],
)
list_exchanges_cmd.set_defaults(func=start_list_exchanges)
self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
@@ -149,7 +187,8 @@ class Arguments:
# Add list-timeframes subcommand
list_timeframes_cmd = subparsers.add_parser(
'list-timeframes',
help='Print available ticker intervals (timeframes) for the exchange.'
help='Print available ticker intervals (timeframes) for the exchange.',
parents=[_common_parser],
)
list_timeframes_cmd.set_defaults(func=start_list_timeframes)
self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
@@ -157,7 +196,8 @@ class Arguments:
# Add list-markets subcommand
list_markets_cmd = subparsers.add_parser(
'list-markets',
help='Print markets on exchange.'
help='Print markets on exchange.',
parents=[_common_parser],
)
list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
@@ -165,7 +205,8 @@ class Arguments:
# Add list-pairs subcommand
list_pairs_cmd = subparsers.add_parser(
'list-pairs',
help='Print pairs on exchange.'
help='Print pairs on exchange.',
parents=[_common_parser],
)
list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_pairs_cmd)
@@ -173,16 +214,17 @@ class Arguments:
# Add download-data subcommand
download_data_cmd = subparsers.add_parser(
'download-data',
help='Download backtesting data.'
help='Download backtesting data.',
parents=[_common_parser],
)
download_data_cmd.set_defaults(func=start_download_data)
self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
# Add Plotting subcommand
from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
plot_dataframe_cmd = subparsers.add_parser(
'plot-dataframe',
help='Plot candles with indicators.'
help='Plot candles with indicators.',
parents=[_common_parser, _strategy_parser],
)
plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
@@ -190,7 +232,8 @@ class Arguments:
# Plot profit
plot_profit_cmd = subparsers.add_parser(
'plot-profit',
help='Generate plot showing profits.'
help='Generate plot showing profits.',
parents=[_common_parser],
)
plot_profit_cmd.set_defaults(func=start_plot_profit)
self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)

16
freqtrade/configuration/check_exchange.py
View File

@@ -10,6 +10,19 @@ from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def remove_credentials(config: Dict[str, Any]):
"""
Removes exchange keys from the configuration and specifies dry-run
Used for backtesting / hyperopt / edge and utils.
Modifies the input dict!
"""
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
config['exchange']['password'] = ''
config['exchange']['uid'] = ''
config['dry_run'] = True
def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
"""
Check if the exchange name in the config file is supported by Freqtrade
@@ -21,7 +34,8 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
and thus is not known for the Freqtrade at all.
"""
if config['runmode'] in [RunMode.PLOT] and not config.get('exchange', {}).get('name'):
if (config['runmode'] in [RunMode.PLOT, RunMode.UTIL_NO_EXCHANGE, RunMode.OTHER]
and not config.get('exchange', {}).get('name')):
# Skip checking exchange in plot mode, since it requires no exchange
return True
logger.info("Checking exchange...")

32
freqtrade/configuration/cli_options.py
View File

@@ -63,12 +63,16 @@ AVAILABLE_CLI_OPTIONS = {
help='Path to userdata directory.',
metavar='PATH',
),
"reset": Arg(
'--reset',
help='Reset sample files to their original state.',
action='store_true',
),
# Main options
"strategy": Arg(
'-s', '--strategy',
help='Specify strategy class name (default: `%(default)s`).',
help='Specify strategy class name which will be used by the bot.',
metavar='NAME',
default='DefaultStrategy',
),
"strategy_path": Arg(
'--strategy-path',
@@ -87,6 +91,11 @@ AVAILABLE_CLI_OPTIONS = {
help='Notify systemd service manager.',
action='store_true',
),
"dry_run": Arg(
'--dry-run',
help='Enforce dry-run for trading (removes Exchange secrets and simulates trades).',
action='store_true',
),
# Optimize common
"ticker_interval": Arg(
'-i', '--ticker-interval',
@@ -137,7 +146,7 @@ AVAILABLE_CLI_OPTIONS = {
),
"exportfilename": Arg(
'--export-filename',
help='Save backtest results to the file with this filename (default: `%(default)s`). '
help='Save backtest results to the file with this filename. '
'Requires `--export` to be set as well. '
'Example: `--export-filename=user_data/backtest_results/backtest_today.json`',
metavar='PATH',
@@ -157,14 +166,13 @@ AVAILABLE_CLI_OPTIONS = {
),
# Hyperopt
"hyperopt": Arg(
'--customhyperopt',
help='Specify hyperopt class name (default: `%(default)s`).',
'--hyperopt',
help='Specify hyperopt class name which will be used by the bot.',
metavar='NAME',
default=constants.DEFAULT_HYPEROPT,
),
"hyperopt_path": Arg(
'--hyperopt-path',
help='Specify additional lookup path for Hyperopts and Hyperopt Loss functions.',
help='Specify additional lookup path for Hyperopt and Hyperopt Loss functions.',
metavar='PATH',
),
"epochs": Arg(
@@ -175,7 +183,7 @@ AVAILABLE_CLI_OPTIONS = {
default=constants.HYPEROPT_EPOCH,
),
"spaces": Arg(
'-s', '--spaces',
'--spaces',
help='Specify which parameters to hyperopt. Space-separated list. '
'Default: `%(default)s`.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss'],
@@ -332,6 +340,14 @@ AVAILABLE_CLI_OPTIONS = {
help='Clean all existing data for the selected exchange/pairs/timeframes.',
action='store_true',
),
# Templating options
"template": Arg(
'--template',
help='Use a template which is either `minimal` or '
'`full` (containing multiple sample indicators). Default: `%(default)s`.',
choices=['full', 'minimal'],
default='full',
),
# Plot dataframe
"indicators1": Arg(
'--indicators1',

22
freqtrade/configuration/config_validation.py
View File

@@ -5,7 +5,7 @@ from jsonschema import Draft4Validator, validators
from jsonschema.exceptions import ValidationError, best_match
from freqtrade import constants, OperationalException
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@@ -61,9 +61,15 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
:param conf: Config in JSON format
:return: Returns None if everything is ok, otherwise throw an OperationalException
"""
# validating trailing stoploss
_validate_trailing_stoploss(conf)
_validate_edge(conf)
_validate_whitelist(conf)
# validate configuration before returning
logger.info('Validating configuration ...')
validate_config_schema(conf)
def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
@@ -111,3 +117,17 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
"Edge and VolumePairList are incompatible, "
"Edge will override whatever pairs VolumePairlist selects."
)
def _validate_whitelist(conf: Dict[str, Any]) -> None:
"""
Dynamic whitelist does not require pair_whitelist to be set - however StaticWhitelist does.
"""
if conf.get('runmode', RunMode.OTHER) in [RunMode.OTHER, RunMode.PLOT,
RunMode.UTIL_NO_EXCHANGE, RunMode.UTIL_EXCHANGE]:
return
for pl in conf.get('pairlists', [{'method': 'StaticPairList'}]):
if (pl.get('method') == 'StaticPairList'
and not conf.get('exchange', {}).get('pair_whitelist')):
raise OperationalException("StaticPairList requires pair_whitelist to be set.")

70
freqtrade/configuration/configuration.py
View File

@@ -9,15 +9,13 @@ from typing import Any, Callable, Dict, List, Optional
from freqtrade import OperationalException, constants
from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.config_validation import (validate_config_consistency,
validate_config_schema)
from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
from freqtrade.configuration.directory_operations import (create_datadir,
create_userdata_dir)
from freqtrade.configuration.load_config import load_config_file
from freqtrade.loggers import setup_logging
from freqtrade.misc import deep_merge_dicts, json_load
from freqtrade.state import RunMode
from freqtrade.state import RunMode, TRADING_MODES, NON_UTIL_MODES
logger = logging.getLogger(__name__)
@@ -81,9 +79,8 @@ class Configuration:
if 'ask_strategy' not in config:
config['ask_strategy'] = {}
# validate configuration before returning
logger.info('Validating configuration ...')
validate_config_schema(config)
if 'pairlists' not in config:
config['pairlists'] = []
return config
@@ -93,19 +90,21 @@ class Configuration:
:return: Configuration dictionary
"""
# Load all configs
config: Dict[str, Any] = self.load_from_files(self.args["config"])
config: Dict[str, Any] = self.load_from_files(self.args.get("config", []))
# Keep a copy of the original configuration file
config['original_config'] = deepcopy(config)
self._process_runmode(config)
self._process_common_options(config)
self._process_trading_options(config)
self._process_optimize_options(config)
self._process_plot_options(config)
self._process_runmode(config)
# Check if the exchange set by the user is supported
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
@@ -113,8 +112,6 @@ class Configuration:
process_temporary_deprecated_settings(config)
validate_config_consistency(config)
return config
def _process_logging_options(self, config: Dict[str, Any]) -> None:
@@ -130,21 +127,9 @@ class Configuration:
setup_logging(config)
def _process_common_options(self, config: Dict[str, Any]) -> None:
self._process_logging_options(config)
# Set strategy if not specified in config and or if it's non default
if self.args.get("strategy") != constants.DEFAULT_STRATEGY or not config.get('strategy'):
config.update({'strategy': self.args.get("strategy")})
self._args_to_config(config, argname='strategy_path',
logstring='Using additional Strategy lookup path: {}')
if ('db_url' in self.args and self.args["db_url"] and
self.args["db_url"] != constants.DEFAULT_DB_PROD_URL):
config.update({'db_url': self.args["db_url"]})
logger.info('Parameter --db-url detected ...')
def _process_trading_options(self, config: Dict[str, Any]) -> None:
if config['runmode'] not in TRADING_MODES:
return
if config.get('dry_run', False):
logger.info('Dry run is enabled')
@@ -158,17 +143,33 @@ class Configuration:
logger.info(f'Using DB: "{config["db_url"]}"')
def _process_common_options(self, config: Dict[str, Any]) -> None:
self._process_logging_options(config)
# Set strategy if not specified in config and or if it's non default
if self.args.get("strategy") or not config.get('strategy'):
config.update({'strategy': self.args.get("strategy")})
self._args_to_config(config, argname='strategy_path',
logstring='Using additional Strategy lookup path: {}')
if ('db_url' in self.args and self.args["db_url"] and
self.args["db_url"] != constants.DEFAULT_DB_PROD_URL):
config.update({'db_url': self.args["db_url"]})
logger.info('Parameter --db-url detected ...')
if config.get('forcebuy_enable', False):
logger.warning('`forcebuy` RPC message enabled.')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
# Support for sd_notify
if 'sd_notify' in self.args and self.args["sd_notify"]:
config['internals'].update({'sd_notify': True})
self._args_to_config(config, argname='dry_run',
logstring='Parameter --dry-run detected, '
'overriding dry_run to: {} ...')
def _process_datadir_options(self, config: Dict[str, Any]) -> None:
"""
Extract information for sys.argv and load directory configurations
@@ -179,6 +180,9 @@ class Configuration:
config['exchange']['name'] = self.args["exchange"]
logger.info(f"Using exchange {config['exchange']['name']}")
if 'pair_whitelist' not in config['exchange']:
config['exchange']['pair_whitelist'] = []
if 'user_data_dir' in self.args and self.args["user_data_dir"]:
config.update({'user_data_dir': self.args["user_data_dir"]})
elif 'user_data_dir' not in config:
@@ -209,6 +213,10 @@ class Configuration:
self._args_to_config(config, argname='position_stacking',
logstring='Parameter --enable-position-stacking detected ...')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
if 'use_max_market_positions' in self.args and not self.args["use_max_market_positions"]:
config.update({'use_max_market_positions': False})
logger.info('Parameter --disable-max-market-positions detected ...')
@@ -217,7 +225,7 @@ class Configuration:
config.update({'max_open_trades': self.args["max_open_trades"]})
logger.info('Parameter --max_open_trades detected, '
'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
else:
elif config['runmode'] in NON_UTIL_MODES:
logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
self._args_to_config(config, argname='stake_amount',

23
freqtrade/configuration/deprecated_settings.py
View File

@@ -57,3 +57,26 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
'experimental', 'sell_profit_only')
process_deprecated_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
'experimental', 'ignore_roi_if_buy_signal')
if not config.get('pairlists') and not config.get('pairlists'):
config['pairlists'] = [{'method': 'StaticPairList'}]
logger.warning(
"DEPRECATED: "
"Pairlists must be defined explicitly in the future."
"Defaulting to StaticPairList for now.")
if config.get('pairlist', {}).get("method") == 'VolumePairList':
logger.warning(
"DEPRECATED: "
f"Using VolumePairList in pairlist is deprecated and must be moved to pairlists. "
"Please refer to the docs on configuration details")
pl = {'method': 'VolumePairList'}
pl.update(config.get('pairlist', {}).get('config'))
config['pairlists'].append(pl)
if config.get('pairlist', {}).get('config', {}).get('precision_filter'):
logger.warning(
"DEPRECATED: "
f"Using precision_filter setting is deprecated and has been replaced by"
"PrecisionFilter. Please refer to the docs on configuration details")
config['pairlists'].append({'method': 'PrecisionFilter'})

30
freqtrade/configuration/directory_operations.py
View File

@@ -1,8 +1,10 @@
import logging
from typing import Any, Dict, Optional
import shutil
from pathlib import Path
from typing import Any, Dict, Optional
from freqtrade import OperationalException
from freqtrade.constants import USER_DATA_FILES
logger = logging.getLogger(__name__)
@@ -31,7 +33,8 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
:param create_dir: Create directory if it does not exist.
:return: Path object containing the directory
"""
sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "plot", "strategies", ]
sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
"plot", "strategies", ]
folder = Path(directory)
if not folder.is_dir():
if create_dir:
@@ -48,3 +51,26 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
if not subfolder.is_dir():
subfolder.mkdir(parents=False)
return folder
def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
"""
Copy files from templates to User data directory.
:param directory: Directory to copy data to
:param overwrite: Overwrite existing sample files
"""
if not directory.is_dir():
raise OperationalException(f"Directory `{directory}` does not exist.")
sourcedir = Path(__file__).parents[1] / "templates"
for source, target in USER_DATA_FILES.items():
targetdir = directory / target
if not targetdir.is_dir():
raise OperationalException(f"Directory `{targetdir}` does not exist.")
targetfile = targetdir / source
if targetfile.exists():
if not overwrite:
logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
continue
else:
logger.warning(f"File `{targetfile}` exists already, overwriting.")
shutil.copy(str(sourcedir / source), str(targetfile))

31
freqtrade/configuration/timerange.py
View File

@@ -1,11 +1,14 @@
"""
This module contains the argument manager class
"""
import logging
import re
from typing import Optional
import arrow
logger = logging.getLogger(__name__)
class TimeRange:
"""
@@ -27,6 +30,34 @@ class TimeRange:
return (self.starttype == other.starttype and self.stoptype == other.stoptype
and self.startts == other.startts and self.stopts == other.stopts)
def subtract_start(self, seconds) -> None:
"""
Subtracts <seconds> from startts if startts is set.
:param seconds: Seconds to subtract from starttime
:return: None (Modifies the object in place)
"""
if self.startts:
self.startts = self.startts - seconds
def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
min_date: arrow.Arrow) -> None:
"""
Adjust startts by <startup_candles> candles.
Applies only if no startup-candles have been available.
:param timeframe_secs: Ticker timeframe in seconds e.g. `timeframe_to_seconds('5m')`
:param startup_candles: Number of candles to move start-date forward
:param min_date: Minimum data date loaded. Key kriterium to decide if start-time
has to be moved
:return: None (Modifies the object in place)
"""
if (not self.starttype or (startup_candles
and min_date.timestamp >= self.startts)):
# If no startts was defined, or backtest-data starts at the defined backtest-date
logger.warning("Moving start-date by %s candles to account for startup time.",
startup_candles)
self.startts = (min_date.timestamp + timeframe_secs * startup_candles)
self.starttype = 'date'
@staticmethod
def parse_timerange(text: Optional[str]):
"""

96
freqtrade/constants.py
View File

@@ -6,11 +6,8 @@ bot constants
DEFAULT_CONFIG = 'config.json'
DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec
DEFAULT_TICKER_INTERVAL = 5 # min
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_STRATEGY = 'DefaultStrategy'
DEFAULT_HYPEROPT = 'DefaultHyperOpt'
DEFAULT_HYPEROPT_LOSS = 'DefaultHyperOptLoss'
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite://'
@@ -20,11 +17,23 @@ REQUIRED_ORDERTIF = ['buy', 'sell']
REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'PrecisionFilter', 'PriceFilter']
DRY_RUN_WALLET = 999.9
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
TICKER_INTERVALS = [
USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGY = 'strategies'
# Soure files with destination directories within user-directory
USER_DATA_FILES = {
'sample_strategy.py': USERPATH_STRATEGY,
'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
'sample_hyperopt.py': USERPATH_HYPEROPTS,
'strategy_analysis_example.ipynb': 'notebooks',
}
TIMEFRAMES = [
'1m', '3m', '5m', '15m', '30m',
'1h', '2h', '4h', '6h', '8h', '12h',
'1d', '3d', '1w',
@@ -56,13 +65,13 @@ MINIMAL_CONFIG = {
CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': 'integer', 'minimum': -1},
'ticker_interval': {'type': 'string', 'enum': TICKER_INTERVALS},
'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
'ticker_interval': {'type': 'string', 'enum': TIMEFRAMES},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
'stake_amount': {
"type": ["number", "string"],
"minimum": 0.0005,
"pattern": UNLIMITED_STAKE_AMOUNT
'type': ['number', 'string'],
'minimum': 0.0001,
'pattern': UNLIMITED_STAKE_AMOUNT
},
'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
'dry_run': {'type': 'boolean'},
@@ -84,8 +93,8 @@ CONF_SCHEMA = {
'unfilledtimeout': {
'type': 'object',
'properties': {
'buy': {'type': 'number', 'minimum': 3},
'sell': {'type': 'number', 'minimum': 10}
'buy': {'type': 'number', 'minimum': 1},
'sell': {'type': 'number', 'minimum': 1}
}
},
'bid_strategy': {
@@ -97,7 +106,7 @@ CONF_SCHEMA = {
'maximum': 1,
'exclusiveMaximum': False,
'use_order_book': {'type': 'boolean'},
'order_book_top': {'type': 'number', 'maximum': 20, 'minimum': 1},
'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
'check_depth_of_market': {
'type': 'object',
'properties': {
@@ -113,8 +122,8 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'use_order_book': {'type': 'boolean'},
'order_book_min': {'type': 'number', 'minimum': 1},
'order_book_max': {'type': 'number', 'minimum': 1, 'maximum': 50},
'order_book_min': {'type': 'integer', 'minimum': 1},
'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
'use_sell_signal': {'type': 'boolean'},
'sell_profit_only': {'type': 'boolean'},
'ignore_roi_if_buy_signal': {'type': 'boolean'}
@@ -151,13 +160,16 @@ CONF_SCHEMA = {
'block_bad_exchanges': {'type': 'boolean'}
}
},
'pairlist': {
'type': 'object',
'properties': {
'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS},
'config': {'type': 'object'}
},
'required': ['method']
'pairlists': {
'type': 'array',
'items': {
'type': 'object',
'properties': {
'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS},
'config': {'type': 'object'}
},
'required': ['method'],
}
},
'telegram': {
'type': 'object',
@@ -184,8 +196,8 @@ CONF_SCHEMA = {
'listen_ip_address': {'format': 'ipv4'},
'listen_port': {
'type': 'integer',
"minimum": 1024,
"maximum": 65535
'minimum': 1024,
'maximum': 65535
},
'username': {'type': 'string'},
'password': {'type': 'string'},
@@ -198,7 +210,7 @@ CONF_SCHEMA = {
'internals': {
'type': 'object',
'properties': {
'process_throttle_secs': {'type': 'number'},
'process_throttle_secs': {'type': 'integer'},
'interval': {'type': 'integer'},
'sd_notify': {'type': 'boolean'},
}
@@ -235,37 +247,37 @@ CONF_SCHEMA = {
'ccxt_config': {'type': 'object'},
'ccxt_async_config': {'type': 'object'}
},
'required': ['name', 'pair_whitelist']
'required': ['name']
},
'edge': {
'type': 'object',
'properties': {
"enabled": {'type': 'boolean'},
"process_throttle_secs": {'type': 'integer', 'minimum': 600},
"calculate_since_number_of_days": {'type': 'integer'},
"allowed_risk": {'type': 'number'},
"capital_available_percentage": {'type': 'number'},
"stoploss_range_min": {'type': 'number'},
"stoploss_range_max": {'type': 'number'},
"stoploss_range_step": {'type': 'number'},
"minimum_winrate": {'type': 'number'},
"minimum_expectancy": {'type': 'number'},
"min_trade_number": {'type': 'number'},
"max_trade_duration_minute": {'type': 'integer'},
"remove_pumps": {'type': 'boolean'}
'enabled': {'type': 'boolean'},
'process_throttle_secs': {'type': 'integer', 'minimum': 600},
'calculate_since_number_of_days': {'type': 'integer'},
'allowed_risk': {'type': 'number'},
'capital_available_percentage': {'type': 'number'},
'stoploss_range_min': {'type': 'number'},
'stoploss_range_max': {'type': 'number'},
'stoploss_range_step': {'type': 'number'},
'minimum_winrate': {'type': 'number'},
'minimum_expectancy': {'type': 'number'},
'min_trade_number': {'type': 'number'},
'max_trade_duration_minute': {'type': 'integer'},
'remove_pumps': {'type': 'boolean'}
},
'required': ['process_throttle_secs', 'allowed_risk', 'capital_available_percentage']
}
},
'anyOf': [
{'required': ['exchange']}
],
'required': [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run',
'bid_strategy',
'unfilledtimeout',
'stoploss',
'minimal_roi',
]
}

53
freqtrade/data/btanalysis.py
View File

@@ -7,7 +7,7 @@ from typing import Dict
import numpy as np
import pandas as pd
import pytz
from datetime import timezone
from freqtrade import persistence
from freqtrade.misc import json_load
@@ -52,16 +52,18 @@ def load_backtest_data(filename) -> pd.DataFrame:
return df
def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int) -> pd.DataFrame:
def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps
and then counting overlaps.
:param results: Results Dataframe - can be loaded
:param freq: Frequency used for the backtest
:param max_open_trades: parameter max_open_trades used during backtest run
:return: dataframe with open-counts per time-period in freq
:param timeframe: Timeframe used for backtest
:return: dataframe with open-counts per time-period in timeframe
"""
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
from freqtrade.exchange import timeframe_to_minutes
timeframe_min = timeframe_to_minutes(timeframe)
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time,
freq=f"{timeframe_min}min"))
for row in results[['open_time', 'close_time']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
@@ -69,8 +71,23 @@ def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
df_final = df2.resample(freq)[['pair']].count()
return df_final[df_final['pair'] > max_open_trades]
df_final = df2.resample(f"{timeframe_min}min")[['pair']].count()
df_final = df_final.rename({'pair': 'open_trades'}, axis=1)
return df_final
def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
max_open_trades: int) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps
:param results: Results Dataframe - can be loaded
:param timeframe: Frequency used for the backtest
:param max_open_trades: parameter max_open_trades used during backtest run
:return: dataframe with open-counts per time-period in freq
"""
df_final = analyze_trade_parallelism(results, timeframe)
return df_final[df_final['open_trades'] > max_open_trades]
def load_trades_from_db(db_url: str) -> pd.DataFrame:
@@ -89,8 +106,8 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
"stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=pytz.UTC),
t.close_date.replace(tzinfo=pytz.UTC) if t.close_date else None,
t.open_date.replace(tzinfo=timezone.utc),
t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
t.calc_profit(), t.calc_profit_percent(),
t.open_rate, t.close_rate, t.amount,
(round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
@@ -106,7 +123,7 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
t.stop_loss, t.initial_stop_loss,
t.strategy, t.ticker_interval
)
for t in Trade.query.all()],
for t in Trade.get_trades().all()],
columns=columns)
return trades
@@ -150,15 +167,21 @@ def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "c
return df_comb
def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str) -> pd.DataFrame:
def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
timeframe: str) -> pd.DataFrame:
"""
Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations
:return: Returns df with one additional column, col_name, containing the cumulative profit.
"""
# Use groupby/sum().cumsum() to avoid errors when multiple trades sold at the same candle.
df[col_name] = trades.groupby('close_time')['profitperc'].sum().cumsum()
from freqtrade.exchange import timeframe_to_minutes
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
# FFill to get continuous

10
freqtrade/data/converter.py
View File

@@ -10,13 +10,13 @@ from pandas import DataFrame, to_datetime
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list, ticker_interval: str, pair: str, *,
def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
:param ticker: ticker list, as returned by exchange.async_get_candle_history
:param ticker_interval: ticker_interval (e.g. 5m). Used to fill up eventual missing data
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
(see ohlcv_fill_up_missing_data for details)
@@ -52,12 +52,12 @@ def parse_ticker_dataframe(ticker: list, ticker_interval: str, pair: str, *,
logger.debug('Dropping last candle')
if fill_missing:
return ohlcv_fill_up_missing_data(frame, ticker_interval, pair)
return ohlcv_fill_up_missing_data(frame, timeframe, pair)
else:
return frame
def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair: str) -> DataFrame:
def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
"""
Fills up missing data with 0 volume rows,
using the previous close as price for "open", "high" "low" and "close", volume is set to 0
@@ -72,7 +72,7 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair:
'close': 'last',
'volume': 'sum'
}
ticker_minutes = timeframe_to_minutes(ticker_interval)
ticker_minutes = timeframe_to_minutes(timeframe)
# Resample to create "NAN" values
df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)

25
freqtrade/data/dataprovider.py
View File

@@ -37,52 +37,53 @@ class DataProvider:
@property
def available_pairs(self) -> List[Tuple[str, str]]:
"""
Return a list of tuples containing pair, ticker_interval for which data is currently cached.
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, ticker_interval: str = None, copy: bool = True) -> DataFrame:
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get ohlcv data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param ticker_interval: ticker interval to get data for
:param timeframe: Ticker timeframe to get data for
:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, ticker_interval or self._config['ticker_interval']),
return self._exchange.klines((pair, timeframe or self._config['ticker_interval']),
copy=copy)
else:
return DataFrame()
def historic_ohlcv(self, pair: str, ticker_interval: str = None) -> DataFrame:
def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Get stored historic ohlcv data
:param pair: pair to get the data for
:param ticker_interval: ticker interval to get data for
:param timeframe: timeframe to get data for
"""
return load_pair_history(pair=pair,
ticker_interval=ticker_interval or self._config['ticker_interval'],
timeframe=timeframe or self._config['ticker_interval'],
datadir=Path(self._config['datadir'])
)
def get_pair_dataframe(self, pair: str, ticker_interval: str = None) -> DataFrame:
def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Return pair ohlcv data, either live or cached historical -- depending
on the runmode.
:param pair: pair to get the data for
:param ticker_interval: ticker interval to get data for
:param timeframe: timeframe to get data for
:return: Dataframe for this pair
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
# Get live ohlcv data.
data = self.ohlcv(pair=pair, ticker_interval=ticker_interval)
data = self.ohlcv(pair=pair, timeframe=timeframe)
else:
# Get historic ohlcv data (cached on disk).
data = self.historic_ohlcv(pair=pair, ticker_interval=ticker_interval)
data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
if len(data) == 0:
logger.warning(f"No data found for ({pair}, {ticker_interval}).")
logger.warning(f"No data found for ({pair}, {timeframe}).")
return data
def market(self, pair: str) -> Optional[Dict[str, Any]]:

126
freqtrade/data/history.py
View File

@@ -8,7 +8,8 @@ Includes:
import logging
import operator
from datetime import datetime
from copy import deepcopy
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
@@ -18,7 +19,7 @@ from pandas import DataFrame
from freqtrade import OperationalException, misc
from freqtrade.configuration import TimeRange
from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
from freqtrade.exchange import Exchange, timeframe_to_minutes
from freqtrade.exchange import Exchange, timeframe_to_minutes, timeframe_to_seconds
logger = logging.getLogger(__name__)
@@ -49,13 +50,30 @@ def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
return tickerlist[start_index:stop_index]
def load_tickerdata_file(datadir: Path, pair: str, ticker_interval: str,
def trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date') -> DataFrame:
"""
Trim dataframe based on given timerange
:param df: Dataframe to trim
:param timerange: timerange (use start and end date if available)
:param: df_date_col: Column in the dataframe to use as Date column
:return: trimmed dataframe
"""
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]
if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
df = df.loc[df[df_date_col] <= stop, :]
return df
def load_tickerdata_file(datadir: Path, pair: str, timeframe: str,
timerange: Optional[TimeRange] = None) -> Optional[list]:
"""
Load a pair from file, either .json.gz or .json
:return: tickerlist or None if unsuccessful
"""
filename = pair_data_filename(datadir, pair, ticker_interval)
filename = pair_data_filename(datadir, pair, timeframe)
pairdata = misc.file_load_json(filename)
if not pairdata:
return []
@@ -66,11 +84,11 @@ def load_tickerdata_file(datadir: Path, pair: str, ticker_interval: str,
def store_tickerdata_file(datadir: Path, pair: str,
ticker_interval: str, data: list, is_zip: bool = False):
timeframe: str, data: list, is_zip: bool = False):
"""
Stores tickerdata to file
"""
filename = pair_data_filename(datadir, pair, ticker_interval)
filename = pair_data_filename(datadir, pair, timeframe)
misc.file_dump_json(filename, data, is_zip=is_zip)
@@ -107,18 +125,19 @@ def _validate_pairdata(pair, pairdata, timerange: TimeRange):
def load_pair_history(pair: str,
ticker_interval: str,
timeframe: str,
datadir: Path,
timerange: Optional[TimeRange] = None,
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
fill_up_missing: bool = True,
drop_incomplete: bool = True
drop_incomplete: bool = True,
startup_candles: int = 0,
) -> DataFrame:
"""
Loads cached ticker history for the given pair.
:param pair: Pair to load data for
:param ticker_interval: Ticker-interval (e.g. "5m")
:param timeframe: Ticker timeframe (e.g. "5m")
:param datadir: Path to the data storage location.
:param timerange: Limit data to be loaded to this timerange
:param refresh_pairs: Refresh pairs from exchange.
@@ -126,65 +145,88 @@ def load_pair_history(pair: str,
:param exchange: Exchange object (needed when using "refresh_pairs")
:param fill_up_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
:param startup_candles: Additional candles to load at the start of the period
:return: DataFrame with ohlcv data
"""
timerange_startup = deepcopy(timerange)
if startup_candles > 0 and timerange_startup:
timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
# The user forced the refresh of pairs
if refresh_pairs:
download_pair_history(datadir=datadir,
exchange=exchange,
pair=pair,
ticker_interval=ticker_interval,
timeframe=timeframe,
timerange=timerange)
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
pairdata = load_tickerdata_file(datadir, pair, timeframe, timerange=timerange_startup)
if pairdata:
if timerange:
_validate_pairdata(pair, pairdata, timerange)
return parse_ticker_dataframe(pairdata, ticker_interval, pair=pair,
if timerange_startup:
_validate_pairdata(pair, pairdata, timerange_startup)
return parse_ticker_dataframe(pairdata, timeframe, pair=pair,
fill_missing=fill_up_missing,
drop_incomplete=drop_incomplete)
else:
logger.warning(
f'No history data for pair: "{pair}", interval: {ticker_interval}. '
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
return None
def load_data(datadir: Path,
ticker_interval: str,
timeframe: str,
pairs: List[str],
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
timerange: Optional[TimeRange] = None,
fill_up_missing: bool = True,
startup_candles: int = 0,
fail_without_data: bool = False
) -> Dict[str, DataFrame]:
"""
Loads ticker history data for a list of pairs
:return: dict(<pair>:<tickerlist>)
:param datadir: Path to the data storage location.
:param timeframe: Ticker Timeframe (e.g. "5m")
:param pairs: List of pairs to load
:param refresh_pairs: Refresh pairs from exchange.
(Note: Requires exchange to be passed as well.)
:param exchange: Exchange object (needed when using "refresh_pairs")
:param timerange: Limit data to be loaded to this timerange
:param fill_up_missing: Fill missing values with "No action"-candles
:param startup_candles: Additional candles to load at the start of the period
:param fail_without_data: Raise OperationalException if no data is found.
:return: dict(<pair>:<Dataframe>)
TODO: refresh_pairs is still used by edge to keep the data uptodate.
This should be replaced in the future. Instead, writing the current candles to disk
from dataprovider should be implemented, as this would avoid loading ohlcv data twice.
exchange and refresh_pairs are then not needed here nor in load_pair_history.
"""
result: Dict[str, DataFrame] = {}
if startup_candles > 0 and timerange:
logger.info(f'Using indicator startup period: {startup_candles} ...')
for pair in pairs:
hist = load_pair_history(pair=pair, ticker_interval=ticker_interval,
hist = load_pair_history(pair=pair, timeframe=timeframe,
datadir=datadir, timerange=timerange,
refresh_pairs=refresh_pairs,
exchange=exchange,
fill_up_missing=fill_up_missing)
fill_up_missing=fill_up_missing,
startup_candles=startup_candles)
if hist is not None:
result[pair] = hist
if fail_without_data and not result:
raise OperationalException("No data found. Terminating.")
return result
def pair_data_filename(datadir: Path, pair: str, ticker_interval: str) -> Path:
def pair_data_filename(datadir: Path, pair: str, timeframe: str) -> Path:
pair_s = pair.replace("/", "_")
filename = datadir.joinpath(f'{pair_s}-{ticker_interval}.json')
filename = datadir.joinpath(f'{pair_s}-{timeframe}.json')
return filename
@@ -194,7 +236,7 @@ def pair_trades_filename(datadir: Path, pair: str) -> Path:
return filename
def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: str,
def _load_cached_data_for_updating(datadir: Path, pair: str, timeframe: str,
timerange: Optional[TimeRange]) -> Tuple[List[Any],
Optional[int]]:
"""
@@ -212,12 +254,12 @@ def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: st
if timerange.starttype == 'date':
since_ms = timerange.startts * 1000
elif timerange.stoptype == 'line':
num_minutes = timerange.stopts * timeframe_to_minutes(ticker_interval)
num_minutes = timerange.stopts * timeframe_to_minutes(timeframe)
since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
# read the cached file
# Intentionally don't pass timerange in - since we need to load the full dataset.
data = load_tickerdata_file(datadir, pair, ticker_interval)
data = load_tickerdata_file(datadir, pair, timeframe)
# remove the last item, could be incomplete candle
if data:
data.pop()
@@ -238,18 +280,18 @@ def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: st
def download_pair_history(datadir: Path,
exchange: Optional[Exchange],
pair: str,
ticker_interval: str = '5m',
timeframe: str = '5m',
timerange: Optional[TimeRange] = None) -> bool:
"""
Download the latest ticker intervals from the exchange for the pair passed in parameters
The data is downloaded starting from the last correct ticker interval data that
Download latest candles from the exchange for the pair and timeframe passed in parameters
The data is downloaded starting from the last correct data that
exists in a cache. If timerange starts earlier than the data in the cache,
the full data will be redownloaded
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pair: pair to download
:param ticker_interval: ticker interval
:param timeframe: Ticker Timeframe (e.g 5m)
:param timerange: range of time to download
:return: bool with success state
"""
@@ -260,17 +302,17 @@ def download_pair_history(datadir: Path,
try:
logger.info(
f'Download history data for pair: "{pair}", interval: {ticker_interval} '
f'Download history data for pair: "{pair}", timeframe: {timeframe} '
f'and store in {datadir}.'
)
data, since_ms = _load_cached_data_for_updating(datadir, pair, ticker_interval, timerange)
data, since_ms = _load_cached_data_for_updating(datadir, pair, timeframe, timerange)
logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
# Default since_ms to 30 days if nothing is given
new_data = exchange.get_historic_ohlcv(pair=pair, ticker_interval=ticker_interval,
new_data = exchange.get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms if since_ms
else
int(arrow.utcnow().shift(
@@ -280,12 +322,12 @@ def download_pair_history(datadir: Path,
logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
store_tickerdata_file(datadir, pair, ticker_interval, data=data)
store_tickerdata_file(datadir, pair, timeframe, data=data)
return True
except Exception as e:
logger.error(
f'Failed to download history data for pair: "{pair}", interval: {ticker_interval}. '
f'Failed to download history data for pair: "{pair}", timeframe: {timeframe}. '
f'Error: {e}'
)
return False
@@ -305,17 +347,17 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
pairs_not_available.append(pair)
logger.info(f"Skipping pair {pair}...")
continue
for ticker_interval in timeframes:
for timeframe in timeframes:
dl_file = pair_data_filename(dl_path, pair, ticker_interval)
dl_file = pair_data_filename(dl_path, pair, timeframe)
if erase and dl_file.exists():
logger.info(
f'Deleting existing data for pair {pair}, interval {ticker_interval}.')
f'Deleting existing data for pair {pair}, interval {timeframe}.')
dl_file.unlink()
logger.info(f'Downloading pair {pair}, interval {ticker_interval}.')
logger.info(f'Downloading pair {pair}, interval {timeframe}.')
download_pair_history(datadir=dl_path, exchange=exchange,
pair=pair, ticker_interval=str(ticker_interval),
pair=pair, timeframe=str(timeframe),
timerange=timerange)
return pairs_not_available
@@ -421,7 +463,7 @@ def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
max_date: datetime, ticker_interval_mins: int) -> bool:
max_date: datetime, timeframe_mins: int) -> bool:
"""
Validates preprocessed backtesting data for missing values and shows warnings about it that.
@@ -429,10 +471,10 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
:param pair: pair used for log output.
:param min_date: start-date of the data
:param max_date: end-date of the data
:param ticker_interval_mins: ticker interval in minutes
:param timeframe_mins: ticker Timeframe in minutes
"""
# total difference in minutes / interval-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
# total difference in minutes / timeframe-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_mins)
found_missing = False
dflen = len(data)
if dflen < expected_frames:

5
freqtrade/edge/__init__.py
View File

@@ -97,10 +97,11 @@ class Edge:
data = history.load_data(
datadir=Path(self.config['datadir']),
pairs=pairs,
ticker_interval=self.strategy.ticker_interval,
timeframe=self.strategy.ticker_interval,
refresh_pairs=self._refresh_pairs,
exchange=self.exchange,
timerange=self._timerange
timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count,
)
if not data:

4
freqtrade/exchange/__init__.py
View File

@@ -1,4 +1,5 @@
from freqtrade.exchange.exchange import Exchange, MAP_EXCHANGE_CHILDCLASS # noqa: F401
from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS # noqa: F401
from freqtrade.exchange.exchange import Exchange # noqa: F401
from freqtrade.exchange.exchange import (get_exchange_bad_reason, # noqa: F401
is_exchange_bad,
is_exchange_known_ccxt,
@@ -14,3 +15,4 @@ from freqtrade.exchange.exchange import (market_is_active, # noqa: F401
symbol_is_pair)
from freqtrade.exchange.kraken import Kraken # noqa: F401
from freqtrade.exchange.binance import Binance # noqa: F401
from freqtrade.exchange.bibox import Bibox # noqa: F401

22
freqtrade/exchange/bibox.py Normal file
View File

@@ -0,0 +1,22 @@
""" Bibox exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Bibox(Exchange):
"""
Bibox exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
# fetchCurrencies API point requires authentication for Bibox,
# so switch it off for Freqtrade load_markets()
_ccxt_config: Dict = {"has": {"fetchCurrencies": False}}

124
freqtrade/exchange/common.py Normal file
View File

@@ -0,0 +1,124 @@
import logging
from freqtrade import DependencyException, TemporaryError
logger = logging.getLogger(__name__)
API_RETRY_COUNT = 4
BAD_EXCHANGES = {
"bitmex": "Various reasons.",
"bitstamp": "Does not provide history. "
"Details in https://github.com/freqtrade/freqtrade/issues/1983",
"hitbtc": "This API cannot be used with Freqtrade. "
"Use `hitbtc2` exchange id to access this exchange.",
**dict.fromkeys([
'adara',
'anxpro',
'bigone',
'coinbase',
'coinexchange',
'coinmarketcap',
'lykke',
'xbtce',
], "Does not provide timeframes. ccxt fetchOHLCV: False"),
**dict.fromkeys([
'bcex',
'bit2c',
'bitbay',
'bitflyer',
'bitforex',
'bithumb',
'bitso',
'bitstamp1',
'bl3p',
'braziliex',
'btcbox',
'btcchina',
'btctradeim',
'btctradeua',
'bxinth',
'chilebit',
'coincheck',
'coinegg',
'coinfalcon',
'coinfloor',
'coingi',
'coinmate',
'coinone',
'coinspot',
'coolcoin',
'crypton',
'deribit',
'exmo',
'exx',
'flowbtc',
'foxbit',
'fybse',
# 'hitbtc',
'ice3x',
'independentreserve',
'indodax',
'itbit',
'lakebtc',
'latoken',
'liquid',
'livecoin',
'luno',
'mixcoins',
'negociecoins',
'nova',
'paymium',
'southxchange',
'stronghold',
'surbitcoin',
'therock',
'tidex',
'vaultoro',
'vbtc',
'virwox',
'yobit',
'zaif',
], "Does not provide timeframes. ccxt fetchOHLCV: emulated"),
}
MAP_EXCHANGE_CHILDCLASS = {
'binanceus': 'binance',
'binanceje': 'binance',
}
def retrier_async(f):
async def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
try:
return await f(*args, **kwargs)
except (TemporaryError, DependencyException) as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
count -= 1
kwargs.update({'count': count})
logger.warning('retrying %s() still for %s times', f.__name__, count)
return await wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
raise ex
return wrapper
def retrier(f):
def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
try:
return f(*args, **kwargs)
except (TemporaryError, DependencyException) as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
count -= 1
kwargs.update({'count': count})
logger.warning('retrying %s() still for %s times', f.__name__, count)
return wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
raise ex
return wrapper

234
freqtrade/exchange/exchange.py
View File

@@ -14,141 +14,25 @@ from typing import Any, Dict, List, Optional, Tuple
import arrow
import ccxt
import ccxt.async_support as ccxt_async
from ccxt.base.decimal_to_precision import ROUND_UP, ROUND_DOWN
from ccxt.base.decimal_to_precision import ROUND_DOWN, ROUND_UP
from pandas import DataFrame
from freqtrade import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError, constants)
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
from freqtrade.misc import deep_merge_dicts
logger = logging.getLogger(__name__)
API_RETRY_COUNT = 4
BAD_EXCHANGES = {
"bitmex": "Various reasons.",
"bitstamp": "Does not provide history. "
"Details in https://github.com/freqtrade/freqtrade/issues/1983",
"hitbtc": "This API cannot be used with Freqtrade. "
"Use `hitbtc2` exchange id to access this exchange.",
**dict.fromkeys([
'adara',
'anxpro',
'bigone',
'coinbase',
'coinexchange',
'coinmarketcap',
'lykke',
'xbtce',
], "Does not provide timeframes. ccxt fetchOHLCV: False"),
**dict.fromkeys([
'bcex',
'bit2c',
'bitbay',
'bitflyer',
'bitforex',
'bithumb',
'bitso',
'bitstamp1',
'bl3p',
'braziliex',
'btcbox',
'btcchina',
'btctradeim',
'btctradeua',
'bxinth',
'chilebit',
'coincheck',
'coinegg',
'coinfalcon',
'coinfloor',
'coingi',
'coinmate',
'coinone',
'coinspot',
'coolcoin',
'crypton',
'deribit',
'exmo',
'exx',
'flowbtc',
'foxbit',
'fybse',
# 'hitbtc',
'ice3x',
'independentreserve',
'indodax',
'itbit',
'lakebtc',
'latoken',
'liquid',
'livecoin',
'luno',
'mixcoins',
'negociecoins',
'nova',
'paymium',
'southxchange',
'stronghold',
'surbitcoin',
'therock',
'tidex',
'vaultoro',
'vbtc',
'virwox',
'yobit',
'zaif',
], "Does not provide timeframes. ccxt fetchOHLCV: emulated"),
}
MAP_EXCHANGE_CHILDCLASS = {
'binanceus': 'binance',
'binanceje': 'binance',
}
def retrier_async(f):
async def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
try:
return await f(*args, **kwargs)
except (TemporaryError, DependencyException) as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
count -= 1
kwargs.update({'count': count})
logger.warning('retrying %s() still for %s times', f.__name__, count)
return await wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
raise ex
return wrapper
def retrier(f):
def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
try:
return f(*args, **kwargs)
except (TemporaryError, DependencyException) as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
count -= 1
kwargs.update({'count': count})
logger.warning('retrying %s() still for %s times', f.__name__, count)
return wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
raise ex
return wrapper
class Exchange:
_config: Dict = {}
# Parameters to add directly to ccxt sync/async initialization.
_ccxt_config: Dict = {}
# Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
_params: Dict = {}
@@ -210,10 +94,17 @@ class Exchange:
self._trades_pagination_arg = self._ft_has['trades_pagination_arg']
# Initialize ccxt objects
ccxt_config = self._ccxt_config.copy()
ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
ccxt_config)
self._api = self._init_ccxt(
exchange_config, ccxt_kwargs=exchange_config.get('ccxt_config'))
exchange_config, ccxt_kwargs=ccxt_config)
ccxt_async_config = self._ccxt_config.copy()
ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_async_config', {}),
ccxt_async_config)
self._api_async = self._init_ccxt(
exchange_config, ccxt_async, ccxt_kwargs=exchange_config.get('ccxt_async_config'))
exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
logger.info('Using Exchange "%s"', self.name)
@@ -228,6 +119,7 @@ class Exchange:
self.validate_pairs(config['exchange']['pair_whitelist'])
self.validate_ordertypes(config.get('order_types', {}))
self.validate_order_time_in_force(config.get('order_time_in_force', {}))
self.validate_required_startup_candles(config.get('startup_candle_count', 0))
# Converts the interval provided in minutes in config to seconds
self.markets_refresh_interval: int = exchange_config.get(
@@ -443,6 +335,16 @@ class Exchange:
raise OperationalException(
f'Time in force policies are not supported for {self.name} yet.')
def validate_required_startup_candles(self, startup_candles) -> None:
"""
Checks if required startup_candles is more than ohlcv_candle_limit.
Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
"""
if startup_candles + 5 > self._ft_has['ohlcv_candle_limit']:
raise OperationalException(
f"This strategy requires {startup_candles} candles to start. "
f"{self.name} only provides {self._ft_has['ohlcv_candle_limit']}.")
def exchange_has(self, endpoint: str) -> bool:
"""
Checks if exchange implements a specific API endpoint.
@@ -644,40 +546,40 @@ class Exchange:
logger.info("returning cached ticker-data for %s", pair)
return self._cached_ticker[pair]
def get_historic_ohlcv(self, pair: str, ticker_interval: str,
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int) -> List:
"""
Gets candle history using asyncio and returns the list of candles.
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param ticker_interval: Interval to get
:param timeframe: Ticker Timeframe to get
:param since_ms: Timestamp in milliseconds to get history from
:returns List of tickers
"""
return asyncio.get_event_loop().run_until_complete(
self._async_get_historic_ohlcv(pair=pair, ticker_interval=ticker_interval,
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms))
async def _async_get_historic_ohlcv(self, pair: str,
ticker_interval: str,
timeframe: str,
since_ms: int) -> List:
one_call = timeframe_to_msecs(ticker_interval) * self._ohlcv_candle_limit
one_call = timeframe_to_msecs(timeframe) * self._ohlcv_candle_limit
logger.debug(
"one_call: %s msecs (%s)",
one_call,
arrow.utcnow().shift(seconds=one_call // 1000).humanize(only_distance=True)
)
input_coroutines = [self._async_get_candle_history(
pair, ticker_interval, since) for since in
pair, timeframe, since) for since in
range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
# Combine tickers
data: List = []
for p, ticker_interval, ticker in tickers:
for p, timeframe, ticker in tickers:
if p == pair:
data.extend(ticker)
# Sort data again after extending the result - above calls return in "async order"
@@ -697,14 +599,14 @@ class Exchange:
input_coroutines = []
# Gather coroutines to run
for pair, ticker_interval in set(pair_list):
if (not ((pair, ticker_interval) in self._klines)
or self._now_is_time_to_refresh(pair, ticker_interval)):
input_coroutines.append(self._async_get_candle_history(pair, ticker_interval))
for pair, timeframe in set(pair_list):
if (not ((pair, timeframe) in self._klines)
or self._now_is_time_to_refresh(pair, timeframe)):
input_coroutines.append(self._async_get_candle_history(pair, timeframe))
else:
logger.debug(
"Using cached ohlcv data for pair %s, interval %s ...",
pair, ticker_interval
"Using cached ohlcv data for pair %s, timeframe %s ...",
pair, timeframe
)
tickers = asyncio.get_event_loop().run_until_complete(
@@ -716,40 +618,40 @@ class Exchange:
logger.warning("Async code raised an exception: %s", res.__class__.__name__)
continue
pair = res[0]
ticker_interval = res[1]
timeframe = res[1]
ticks = res[2]
# keeping last candle time as last refreshed time of the pair
if ticks:
self._pairs_last_refresh_time[(pair, ticker_interval)] = ticks[-1][0] // 1000
self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
self._klines[(pair, ticker_interval)] = parse_ticker_dataframe(
ticks, ticker_interval, pair=pair, fill_missing=True,
self._klines[(pair, timeframe)] = parse_ticker_dataframe(
ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
return tickers
def _now_is_time_to_refresh(self, pair: str, ticker_interval: str) -> bool:
def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
# Calculating ticker interval in seconds
interval_in_sec = timeframe_to_seconds(ticker_interval)
interval_in_sec = timeframe_to_seconds(timeframe)
return not ((self._pairs_last_refresh_time.get((pair, ticker_interval), 0)
return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
+ interval_in_sec) >= arrow.utcnow().timestamp)
@retrier_async
async def _async_get_candle_history(self, pair: str, ticker_interval: str,
async def _async_get_candle_history(self, pair: str, timeframe: str,
since_ms: Optional[int] = None) -> Tuple[str, str, List]:
"""
Asynchronously gets candle histories using fetch_ohlcv
returns tuple: (pair, ticker_interval, ohlcv_list)
returns tuple: (pair, timeframe, ohlcv_list)
"""
try:
# fetch ohlcv asynchronously
s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
logger.debug(
"Fetching pair %s, interval %s, since %s %s...",
pair, ticker_interval, since_ms, s
pair, timeframe, since_ms, s
)
data = await self._api_async.fetch_ohlcv(pair, timeframe=ticker_interval,
data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
since=since_ms)
# Because some exchange sort Tickers ASC and other DESC.
@@ -761,9 +663,9 @@ class Exchange:
data = sorted(data, key=lambda x: x[0])
except IndexError:
logger.exception("Error loading %s. Result was %s.", pair, data)
return pair, ticker_interval, []
logger.debug("Done fetching pair %s, interval %s ...", pair, ticker_interval)
return pair, ticker_interval, data
return pair, timeframe, []
logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
return pair, timeframe, data
except ccxt.NotSupported as e:
raise OperationalException(
@@ -910,7 +812,6 @@ class Exchange:
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param ticker_interval: Interval to get
:param since: Timestamp in milliseconds to get history from
:param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
:param from_id: Download data starting with ID (if id is known)
@@ -983,6 +884,22 @@ class Exchange:
@retrier
def get_trades_for_order(self, order_id: str, pair: str, since: datetime) -> List:
"""
Fetch Orders using the "fetch_my_trades" endpoint and filter them by order-id.
The "since" argument passed in is coming from the database and is in UTC,
as timezone-native datetime object.
From the python documentation:
> Naive datetime instances are assumed to represent local time
Therefore, calling "since.timestamp()" will get the UTC timestamp, after applying the
transformation from local timezone to UTC.
This works for timezones UTC+ since then the result will contain trades from a few hours
instead of from the last 5 seconds, however fails for UTC- timezones,
since we're then asking for trades with a "since" argument in the future.
:param order_id order_id: Order-id as given when creating the order
:param pair: Pair the order is for
:param since: datetime object of the order creation time. Assumes object is in UTC.
"""
if self._config['dry_run']:
return []
if not self.exchange_has('fetchMyTrades'):
@@ -990,7 +907,8 @@ class Exchange:
try:
# Allow 5s offset to catch slight time offsets (discovered in #1185)
# since needs to be int in milliseconds
my_trades = self._api.fetch_my_trades(pair, int((since.timestamp() - 5) * 1000))
my_trades = self._api.fetch_my_trades(
pair, int((since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000))
matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
return matched_trades
@@ -1049,27 +967,27 @@ def available_exchanges(ccxt_module=None) -> List[str]:
return [x for x in exchanges if not is_exchange_bad(x)]
def timeframe_to_seconds(ticker_interval: str) -> int:
def timeframe_to_seconds(timeframe: str) -> int:
"""
Translates the timeframe interval value written in the human readable
form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
of seconds for one timeframe interval.
"""
return ccxt.Exchange.parse_timeframe(ticker_interval)
return ccxt.Exchange.parse_timeframe(timeframe)
def timeframe_to_minutes(ticker_interval: str) -> int:
def timeframe_to_minutes(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns minutes.
"""
return ccxt.Exchange.parse_timeframe(ticker_interval) // 60
return ccxt.Exchange.parse_timeframe(timeframe) // 60
def timeframe_to_msecs(ticker_interval: str) -> int:
def timeframe_to_msecs(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns milliseconds.
"""
return ccxt.Exchange.parse_timeframe(ticker_interval) * 1000
return ccxt.Exchange.parse_timeframe(timeframe) * 1000
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:

108
freqtrade/freqtradebot.py
View File

@@ -20,9 +20,9 @@ from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
from freqtrade.persistence import Trade
from freqtrade.resolvers import (ExchangeResolver, PairListResolver,
StrategyResolver)
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.state import State
from freqtrade.strategy.interface import IStrategy, SellType
from freqtrade.wallets import Wallets
@@ -70,14 +70,13 @@ class FreqtradeBot:
# Attach Wallets to Strategy baseclass
IStrategy.wallets = self.wallets
pairlistname = self.config.get('pairlist', {}).get('method', 'StaticPairList')
self.pairlists = PairListResolver(pairlistname, self, self.config).pairlist
self.pairlists = PairListManager(self.exchange, self.config)
# Initializing Edge only if enabled
self.edge = Edge(self.config, self.exchange, self.strategy) if \
self.config.get('edge', {}).get('enabled', False) else None
self.active_pair_whitelist: List[str] = self.config['exchange']['pair_whitelist']
self.active_pair_whitelist = self._refresh_whitelist()
persistence.init(self.config.get('db_url', None),
clean_open_orders=self.config.get('dry_run', False))
@@ -123,21 +122,10 @@ class FreqtradeBot:
# Check whether markets have to be reloaded
self.exchange._reload_markets()
# Refresh whitelist
self.pairlists.refresh_pairlist()
self.active_pair_whitelist = self.pairlists.whitelist
# Calculating Edge positioning
if self.edge:
self.edge.calculate()
self.active_pair_whitelist = self.edge.adjust(self.active_pair_whitelist)
# Query trades from persistence layer
trades = Trade.get_open_trades()
# Extend active-pair whitelist with pairs from open trades
# It ensures that tickers are downloaded for open trades
self._extend_whitelist_with_trades(self.active_pair_whitelist, trades)
self.active_pair_whitelist = self._refresh_whitelist(trades)
# Refreshing candles
self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist),
@@ -150,21 +138,33 @@ class FreqtradeBot:
if len(trades) < self.config['max_open_trades']:
self.process_maybe_execute_buys()
if 'unfilledtimeout' in self.config:
# Check and handle any timed out open orders
self.check_handle_timedout()
Trade.session.flush()
# Check and handle any timed out open orders
self.check_handle_timedout()
Trade.session.flush()
if (self.heartbeat_interval
and (arrow.utcnow().timestamp - self._heartbeat_msg > self.heartbeat_interval)):
and (arrow.utcnow().timestamp - self._heartbeat_msg > self.heartbeat_interval)):
logger.info(f"Bot heartbeat. PID={getpid()}")
self._heartbeat_msg = arrow.utcnow().timestamp
def _extend_whitelist_with_trades(self, whitelist: List[str], trades: List[Any]):
def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
"""
Extend whitelist with pairs from open trades
Refresh whitelist from pairlist or edge and extend it with trades.
"""
whitelist.extend([trade.pair for trade in trades if trade.pair not in whitelist])
# Refresh whitelist
self.pairlists.refresh_pairlist()
_whitelist = self.pairlists.whitelist
# Calculating Edge positioning
if self.edge:
self.edge.calculate()
_whitelist = self.edge.adjust(_whitelist)
if trades:
# Extend active-pair whitelist with pairs from open trades
# It ensures that tickers are downloaded for open trades
_whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
return _whitelist
def _create_pair_whitelist(self, pairs: List[str]) -> List[Tuple[str, str]]:
"""
@@ -266,7 +266,11 @@ class FreqtradeBot:
amount_reserve_percent += self.strategy.stoploss
# it should not be more than 50%
amount_reserve_percent = max(amount_reserve_percent, 0.5)
return min(min_stake_amounts) / amount_reserve_percent
# The value returned should satisfy both limits: for amount (base currency) and
# for cost (quote, stake currency), so max() is used here.
# See also #2575 at github.
return max(min_stake_amounts) / amount_reserve_percent
def create_trades(self) -> bool:
"""
@@ -317,8 +321,7 @@ class FreqtradeBot:
(bidstrat_check_depth_of_market.get('bids_to_ask_delta', 0) > 0):
if self._check_depth_of_market_buy(_pair, bidstrat_check_depth_of_market):
buycount += self.execute_buy(_pair, stake_amount)
else:
continue
continue
buycount += self.execute_buy(_pair, stake_amount)
@@ -632,8 +635,8 @@ class FreqtradeBot:
Force-sells the pair (using EmergencySell reason) in case of Problems creating the order.
:return: True if the order succeeded, and False in case of problems.
"""
# Limit price threshold: As limit price should always be below price
LIMIT_PRICE_PCT = 0.99
# Limit price threshold: As limit price should always be below stop-price
LIMIT_PRICE_PCT = self.strategy.order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
try:
stoploss_order = self.exchange.stoploss_limit(pair=trade.pair, amount=trade.amount,
@@ -755,23 +758,28 @@ class FreqtradeBot:
return True
return False
def _check_timed_out(self, side: str, order: dict) -> bool:
"""
Check if timeout is active, and if the order is still open and timed out
"""
timeout = self.config.get('unfilledtimeout', {}).get(side)
ordertime = arrow.get(order['datetime']).datetime
if timeout is not None:
timeout_threshold = arrow.utcnow().shift(minutes=-timeout).datetime
return (order['status'] == 'open' and order['side'] == side
and ordertime < timeout_threshold)
return False
def check_handle_timedout(self) -> None:
"""
Check if any orders are timed out and cancel if neccessary
:param timeoutvalue: Number of minutes until order is considered timed out
:return: None
"""
buy_timeout = self.config['unfilledtimeout']['buy']
sell_timeout = self.config['unfilledtimeout']['sell']
buy_timeout_threshold = arrow.utcnow().shift(minutes=-buy_timeout).datetime
sell_timeout_threshold = arrow.utcnow().shift(minutes=-sell_timeout).datetime
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
for trade in Trade.get_open_order_trades():
try:
# FIXME: Somehow the query above returns results
# where the open_order_id is in fact None.
# This is probably because the record got
# updated via /forcesell in a different thread.
if not trade.open_order_id:
continue
order = self.exchange.get_order(trade.open_order_id, trade.pair)
@@ -781,23 +789,20 @@ class FreqtradeBot:
trade,
traceback.format_exc())
continue
ordertime = arrow.get(order['datetime']).datetime
# Check if trade is still actually open
if float(order['remaining']) == 0.0:
if float(order.get('remaining', 0.0)) == 0.0:
self.wallets.update()
continue
if ((order['side'] == 'buy' and order['status'] == 'canceled')
or (order['status'] == 'open'
and order['side'] == 'buy' and ordertime < buy_timeout_threshold)):
or (self._check_timed_out('buy', order))):
self.handle_timedout_limit_buy(trade, order)
self.wallets.update()
elif ((order['side'] == 'sell' and order['status'] == 'canceled')
or (order['status'] == 'open'
and order['side'] == 'sell' and ordertime < sell_timeout_threshold)):
or (self._check_timed_out('sell', order))):
self.handle_timedout_limit_sell(trade, order)
self.wallets.update()
@@ -812,7 +817,8 @@ class FreqtradeBot:
})
def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
"""Buy timeout - cancel order
"""
Buy timeout - cancel order
:return: True if order was fully cancelled
"""
reason = "cancelled due to timeout"
@@ -823,18 +829,22 @@ class FreqtradeBot:
corder = order
reason = "canceled on Exchange"
if corder['remaining'] == corder['amount']:
if corder.get('remaining', order['remaining']) == order['amount']:
# if trade is not partially completed, just delete the trade
self.handle_buy_order_full_cancel(trade, reason)
return True
# if trade is partially complete, edit the stake details for the trade
# and close the order
trade.amount = corder['amount'] - corder['remaining']
# cancel_order may not contain the full order dict, so we need to fallback
# to the order dict aquired before cancelling.
# we need to fall back to the values from order if corder does not contain these keys.
trade.amount = order['amount'] - corder.get('remaining', order['remaining'])
trade.stake_amount = trade.amount * trade.open_rate
# verify if fees were taken from amount to avoid problems during selling
try:
new_amount = self.get_real_amount(trade, corder, trade.amount)
new_amount = self.get_real_amount(trade, corder if 'fee' in corder else order,
trade.amount)
if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
trade.amount = new_amount
# Fee was applied, so set to 0

4
freqtrade/loggers.py
View File

@@ -36,8 +36,8 @@ def setup_logging(config: Dict[str, Any]) -> None:
# Log level
verbosity = config['verbosity']
# Log to stdout, not stderr
log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stdout)]
# Log to stderr
log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stderr)]
logfile = config.get('logfile')
if logfile:

23
freqtrade/main.py
View File

@@ -15,7 +15,6 @@ from typing import Any, List
from freqtrade import OperationalException
from freqtrade.configuration import Arguments
from freqtrade.worker import Worker
logger = logging.getLogger('freqtrade')
@@ -28,21 +27,23 @@ def main(sysargv: List[str] = None) -> None:
"""
return_code: Any = 1
worker = None
try:
arguments = Arguments(sysargv)
args = arguments.get_parsed_arg()
# A subcommand has been issued.
# Means if Backtesting or Hyperopt have been called we exit the bot
# Call subcommand.
if 'func' in args:
args['func'](args)
# TODO: fetch return_code as returned by the command function here
return_code = 0
return_code = args['func'](args)
else:
# Load and run worker
worker = Worker(args)
worker.run()
# No subcommand was issued.
raise OperationalException(
"Usage of Freqtrade requires a subcommand to be specified.\n"
"To have the previous behavior (bot executing trades in live/dry-run modes, "
"depending on the value of the `dry_run` setting in the config), run freqtrade "
"as `freqtrade trade [options...]`.\n"
"To see the full list of options available, please use "
"`freqtrade --help` or `freqtrade <command> --help`."
)
except SystemExit as e:
return_code = e
@@ -55,8 +56,6 @@ def main(sysargv: List[str] = None) -> None:
except Exception:
logger.exception('Fatal exception!')
finally:
if worker:
worker.exit()
sys.exit(return_code)

13
freqtrade/misc.py
View File

@@ -127,3 +127,16 @@ def round_dict(d, n):
def plural(num, singular: str, plural: str = None) -> str:
return singular if (num == 1 or num == -1) else plural or singular + 's'
def render_template(templatefile: str, arguments: dict = {}):
from jinja2 import Environment, PackageLoader, select_autoescape
env = Environment(
loader=PackageLoader('freqtrade', 'templates'),
autoescape=select_autoescape(['html', 'xml'])
)
template = env.get_template(templatefile)
return template.render(**arguments)

2
freqtrade/optimize/__init__.py
View File

@@ -78,7 +78,7 @@ def start_hyperopt(args: Dict[str, Any]) -> None:
except Timeout:
logger.info("Another running instance of freqtrade Hyperopt detected.")
logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
"Hyperopt module is resource hungry. Please run your Hyperopts sequentially "
"Hyperopt module is resource hungry. Please run your Hyperopt sequentially "
"or on separate machines.")
logger.info("Quitting now.")
# TODO: return False here in order to help freqtrade to exit

89
freqtrade/optimize/backtesting.py
View File

@@ -10,18 +10,19 @@ from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional
from pandas import DataFrame
from tabulate import tabulate
from freqtrade import OperationalException
from freqtrade.configuration import TimeRange
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.data import history
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.misc import file_dump_json
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
from freqtrade.strategy.interface import IStrategy, SellType
from tabulate import tabulate
logger = logging.getLogger(__name__)
@@ -57,11 +58,7 @@ class Backtesting:
self.config = config
# Reset keys for backtesting
self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
remove_credentials(self.config)
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange
@@ -79,17 +76,21 @@ class Backtesting:
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(StrategyResolver(stratconf).strategy)
validate_config_consistency(stratconf)
else:
# No strategy list specified, only one strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
validate_config_consistency(self.config)
if "ticker_interval" not in self.config:
raise OperationalException("Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`")
self.ticker_interval = str(self.config.get('ticker_interval'))
self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
self.timeframe = str(self.config.get('ticker_interval'))
self.timeframe_mins = timeframe_to_minutes(self.timeframe)
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
self._set_strategy(self.strategylist[0])
@@ -103,6 +104,31 @@ class Backtesting:
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
def load_bt_data(self):
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=Path(self.config['datadir']),
pairs=self.config['exchange']['pair_whitelist'],
timeframe=self.timeframe,
timerange=timerange,
startup_candles=self.required_startup,
fail_without_data=True,
)
min_date, max_date = history.get_timeframe(data)
logger.info(
'Loading data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date)
return data, timerange
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
skip_nan: bool = False) -> str:
"""
@@ -218,7 +244,8 @@ class Backtesting:
ticker: Dict = {}
# Create ticker dict
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
ticker_data = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
@@ -351,7 +378,7 @@ class Backtesting:
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
tmp = start_date + timedelta(minutes=self.timeframe_mins)
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
@@ -403,7 +430,7 @@ class Backtesting:
lock_pair_until[pair] = end_date.datetime
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.ticker_interval_mins)
tmp += timedelta(minutes=self.timeframe_mins)
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
@@ -412,39 +439,18 @@ class Backtesting:
:return: None
"""
data: Dict[str, Any] = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=Path(self.config['datadir']),
pairs=pairs,
ticker_interval=self.ticker_interval,
timerange=timerange,
)
if not data:
logger.critical("No data found. Terminating.")
return
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
data, timerange = self.load_bt_data()
all_results = {}
min_date, max_date = history.get_timeframe(data)
logger.info(
'Backtesting with data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
for strat in self.strategylist:
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
self._set_strategy(strat)
@@ -452,6 +458,15 @@ class Backtesting:
# need to reprocess data every time to populate signals
preprocessed = self.strategy.tickerdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = history.trim_dataframe(df, timerange)
min_date, max_date = history.get_timeframe(preprocessed)
logger.info(
'Backtesting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
{

20
freqtrade/optimize/edge_cli.py
View File

@@ -4,12 +4,14 @@
This module contains the edge backtesting interface
"""
import logging
from typing import Dict, Any
from tabulate import tabulate
from freqtrade import constants
from freqtrade.edge import Edge
from typing import Any, Dict
from freqtrade.configuration import TimeRange
from tabulate import tabulate
from freqtrade import constants
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.edge import Edge
from freqtrade.exchange import Exchange
from freqtrade.resolvers import StrategyResolver
@@ -29,15 +31,13 @@ class EdgeCli:
self.config = config
# Reset keys for edge
self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
remove_credentials(self.config)
self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
self.config['dry_run'] = True
self.exchange = Exchange(self.config)
self.strategy = StrategyResolver(self.config).strategy
validate_config_consistency(self.config)
self.edge = Edge(config, self.exchange, self.strategy)
# Set refresh_pairs to false for edge-cli (it must be true for edge)
self.edge._refresh_pairs = False

93
freqtrade/optimize/hyperopt.py
View File

@@ -4,9 +4,9 @@
This module contains the hyperopt logic
"""
import locale
import logging
import sys
from collections import OrderedDict
from operator import itemgetter
from pathlib import Path
@@ -14,23 +14,22 @@ from pprint import pprint
from typing import Any, Dict, List, Optional
import rapidjson
from colorama import init as colorama_init
from colorama import Fore, Style
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
from colorama import init as colorama_init
from joblib import (Parallel, cpu_count, delayed, dump, load,
wrap_non_picklable_objects)
from pandas import DataFrame
from skopt import Optimizer
from skopt.space import Dimension
from freqtrade.configuration import TimeRange
from freqtrade.data.history import load_data, get_timeframe
from freqtrade.misc import round_dict
from freqtrade.data.history import get_timeframe, trim_dataframe
from freqtrade.misc import plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F4
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F4
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver, HyperOptLossResolver
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
HyperOptResolver)
logger = logging.getLogger(__name__)
@@ -78,6 +77,8 @@ class Hyperopt:
# Previous evaluations
self.trials: List = []
self.num_trials_saved = 0
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
self.backtesting.strategy.advise_indicators = \
@@ -133,13 +134,18 @@ class Hyperopt:
arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
return arg_dict
def save_trials(self) -> None:
def save_trials(self, final: bool = False) -> None:
"""
Save hyperopt trials to file
"""
if self.trials:
logger.info("Saving %d evaluations to '%s'", len(self.trials), self.trials_file)
num_trials = len(self.trials)
if num_trials > self.num_trials_saved:
logger.info(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
dump(self.trials, self.trials_file)
self.num_trials_saved = num_trials
if final:
logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
f"saved to '{self.trials_file}'.")
def read_trials(self) -> List:
"""
@@ -154,6 +160,12 @@ class Hyperopt:
"""
Display Best hyperopt result
"""
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.
if not self.trials:
print("No epochs evaluated yet, no best result.")
return
results = sorted(self.trials, key=itemgetter('loss'))
best_result = results[0]
params = best_result['params']
@@ -198,12 +210,20 @@ class Hyperopt:
# Also round to 5 digits after the decimal point
print(f"Stoploss: {round(params.get('stoploss'), 5)}")
def is_best(self, results) -> bool:
return results['loss'] < self.current_best_loss
def log_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
"""
print_all = self.config.get('print_all', False)
is_best_loss = results['loss'] < self.current_best_loss
is_best_loss = self.is_best(results)
if not print_all:
print('.', end='' if results['current_epoch'] % 100 != 0 else None) # type: ignore
sys.stdout.flush()
if print_all or is_best_loss:
if is_best_loss:
self.current_best_loss = results['loss']
@@ -217,14 +237,10 @@ class Hyperopt:
if print_all:
print(log_str)
else:
print('\n' + log_str)
else:
print('.', end='')
sys.stdout.flush()
print(f'\n{log_str}')
def format_results_logstring(self, results) -> str:
# Output human-friendly index here (starting from 1)
current = results['current_epoch'] + 1
current = results['current_epoch']
total = self.total_epochs
res = results['results_explanation']
loss = results['loss']
@@ -336,7 +352,9 @@ class Hyperopt:
return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
f'Total profit {total_profit: 11.8f} {stake_cur} '
f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.')
f'({profit: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). '
f'Avg duration {duration:5.1f} mins.'
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
def get_optimizer(self, dimensions, cpu_count) -> Optimizer:
return Optimizer(
@@ -379,30 +397,19 @@ class Hyperopt:
)
def start(self) -> None:
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = load_data(
datadir=Path(self.config['datadir']),
pairs=self.config['exchange']['pair_whitelist'],
ticker_interval=self.backtesting.ticker_interval,
timerange=timerange
)
data, timerange = self.backtesting.load_bt_data()
if not data:
logger.critical("No data found. Terminating.")
return
preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = get_timeframe(data)
logger.info(
'Hyperopting with data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
dump(preprocessed, self.tickerdata_pickle)
# We don't need exchange instance anymore while running hyperopt
@@ -432,15 +439,19 @@ class Hyperopt:
self.opt.tell(asked, [v['loss'] for v in f_val])
self.fix_optimizer_models_list()
for j in range(jobs):
current = i * jobs + j
# Use human-friendly index here (starting from 1)
current = i * jobs + j + 1
val = f_val[j]
val['current_epoch'] = current
val['is_initial_point'] = current < INITIAL_POINTS
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = self.is_best(val)
self.log_results(val)
self.trials.append(val)
logger.debug(f"Optimizer epoch evaluated: {val}")
if is_best or current % 100 == 0:
self.save_trials()
except KeyboardInterrupt:
print('User interrupted..')
self.save_trials()
self.save_trials(final=True)
self.log_trials_result()

26
freqtrade/optimize/hyperopt_interface.py
View File

@@ -1,14 +1,13 @@
"""
IHyperOpt interface
This module defines the interface to apply for hyperopts
This module defines the interface to apply for hyperopt
"""
import logging
import math
from abc import ABC, abstractmethod
from abc import ABC
from typing import Dict, Any, Callable, List
from pandas import DataFrame
from skopt.space import Dimension, Integer, Real
from freqtrade import OperationalException
@@ -28,8 +27,8 @@ def _format_exception_message(method: str, space: str) -> str:
class IHyperOpt(ABC):
"""
Interface for freqtrade hyperopts
Defines the mandatory structure must follow any custom hyperopts
Interface for freqtrade hyperopt
Defines the mandatory structure must follow any custom hyperopt
Class attributes you can use:
ticker_interval -> int: value of the ticker interval to use for the strategy
@@ -42,15 +41,6 @@ class IHyperOpt(ABC):
# Assign ticker_interval to be used in hyperopt
IHyperOpt.ticker_interval = str(config['ticker_interval'])
@staticmethod
@abstractmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe().
:return: A Dataframe with all mandatory indicators for the strategies.
"""
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
@@ -116,10 +106,10 @@ class IHyperOpt(ABC):
roi_t_alpha = 1.0
roi_p_alpha = 1.0
ticker_interval_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
timeframe_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
# We define here limits for the ROI space parameters automagically adapted to the
# ticker_interval used by the bot:
# timeframe used by the bot:
#
# * 'roi_t' (limits for the time intervals in the ROI tables) components
# are scaled linearly.
@@ -127,8 +117,8 @@ class IHyperOpt(ABC):
#
# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
# method for the 5m ticker interval.
roi_t_scale = ticker_interval_mins / 5
roi_p_scale = math.log1p(ticker_interval_mins) / math.log1p(5)
roi_t_scale = timeframe_mins / 5
roi_p_scale = math.log1p(timeframe_mins) / math.log1p(5)
roi_limits = {
'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),

4
freqtrade/optimize/hyperopt_loss_interface.py
View File

@@ -1,6 +1,6 @@
"""
IHyperOptLoss interface
This module defines the interface for the loss-function for hyperopts
This module defines the interface for the loss-function for hyperopt
"""
from abc import ABC, abstractmethod
@@ -11,7 +11,7 @@ from pandas import DataFrame
class IHyperOptLoss(ABC):
"""
Interface for freqtrade hyperopts Loss functions.
Interface for freqtrade hyperopt Loss functions.
Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
"""
ticker_interval: str

93
freqtrade/pairlist/IPairList.py
View File

@@ -5,22 +5,31 @@ Provides lists as configured in config.json
"""
import logging
from abc import ABC, abstractmethod
from typing import List
from abc import ABC, abstractmethod, abstractproperty
from copy import deepcopy
from typing import Dict, List
from freqtrade.exchange import market_is_active
logger = logging.getLogger(__name__)
class IPairList(ABC):
def __init__(self, freqtrade, config: dict) -> None:
self._freqtrade = freqtrade
def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
pairlist_pos: int) -> None:
"""
:param exchange: Exchange instance
:param pairlistmanager: Instanciating Pairlist manager
:param config: Global bot configuration
:param pairlistconfig: Configuration for this pairlist - can be empty.
:param pairlist_pos: Position of the filter in the pairlist-filter-list
"""
self._exchange = exchange
self._pairlistmanager = pairlistmanager
self._config = config
self._whitelist = self._config['exchange']['pair_whitelist']
self._blacklist = self._config['exchange'].get('pair_blacklist', [])
self._pairlistconfig = pairlistconfig
self._pairlist_pos = pairlist_pos
@property
def name(self) -> str:
@@ -30,21 +39,13 @@ class IPairList(ABC):
"""
return self.__class__.__name__
@property
def whitelist(self) -> List[str]:
@abstractproperty
def needstickers(self) -> bool:
"""
Has the current whitelist
-> no need to overwrite in subclasses
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return self._whitelist
@property
def blacklist(self) -> List[str]:
"""
Has the current blacklist
-> no need to overwrite in subclasses
"""
return self._blacklist
@abstractmethod
def short_desc(self) -> str:
@@ -54,36 +55,62 @@ class IPairList(ABC):
"""
@abstractmethod
def refresh_pairlist(self) -> None:
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Refreshes pairlists and assigns them to self._whitelist and self._blacklist respectively
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
-> Please overwrite in subclasses
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
def _validate_whitelist(self, whitelist: List[str]) -> List[str]:
@staticmethod
def verify_blacklist(pairlist: List[str], blacklist: List[str]) -> List[str]:
"""
Verify and remove items from pairlist - returning a filtered pairlist.
"""
for pair in deepcopy(pairlist):
if pair in blacklist:
logger.warning(f"Pair {pair} in your blacklist. Removing it from whitelist...")
pairlist.remove(pair)
return pairlist
def _verify_blacklist(self, pairlist: List[str]) -> List[str]:
"""
Proxy method to verify_blacklist for easy access for child classes.
"""
return IPairList.verify_blacklist(pairlist, self._pairlistmanager.blacklist)
def _whitelist_for_active_markets(self, pairlist: List[str]) -> List[str]:
"""
Check available markets and remove pair from whitelist if necessary
:param whitelist: the sorted list of pairs the user might want to trade
:return: the list of pairs the user wants to trade without those unavailable or
black_listed
"""
markets = self._freqtrade.exchange.markets
markets = self._exchange.markets
sanitized_whitelist = set()
for pair in whitelist:
# pair is not in the generated dynamic market, or in the blacklist ... ignore it
if (pair in self.blacklist or pair not in markets
or not pair.endswith(self._config['stake_currency'])):
sanitized_whitelist: List[str] = []
for pair in pairlist:
# pair is not in the generated dynamic market or has the wrong stake currency
if pair not in markets:
logger.warning(f"Pair {pair} is not compatible with exchange "
f"{self._freqtrade.exchange.name} or contained in "
f"your blacklist. Removing it from whitelist..")
f"{self._exchange.name}. Removing it from whitelist..")
continue
if not pair.endswith(self._config['stake_currency']):
logger.warning(f"Pair {pair} is not compatible with your stake currency "
f"{self._config['stake_currency']}. Removing it from whitelist..")
continue
# Check if market is active
market = markets[pair]
if not market_is_active(market):
logger.info(f"Ignoring {pair} from whitelist. Market is not active.")
continue
sanitized_whitelist.add(pair)
if pair not in sanitized_whitelist:
sanitized_whitelist.append(pair)
sanitized_whitelist = self._verify_blacklist(sanitized_whitelist)
# We need to remove pairs that are unknown
return list(sanitized_whitelist)
return sanitized_whitelist

62
freqtrade/pairlist/PrecisionFilter.py Normal file
View File

@@ -0,0 +1,62 @@
import logging
from copy import deepcopy
from typing import Dict, List
from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class PrecisionFilter(IPairList):
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return True
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
"""
return f"{self.name} - Filtering untradable pairs."
def _validate_precision_filter(self, ticker: dict, stoploss: float) -> bool:
"""
Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
low value pairs.
:param ticker: ticker dict as returned from ccxt.load_markets()
:param stoploss: stoploss value as set in the configuration
(already cleaned to be 1 - stoploss)
:return: True if the pair can stay, false if it should be removed
"""
stop_price = ticker['ask'] * stoploss
# Adjust stop-prices to precision
sp = self._exchange.symbol_price_prec(ticker["symbol"], stop_price)
stop_gap_price = self._exchange.symbol_price_prec(ticker["symbol"], stop_price * 0.99)
logger.debug(f"{ticker['symbol']} - {sp} : {stop_gap_price}")
if sp <= stop_gap_price:
logger.info(f"Removed {ticker['symbol']} from whitelist, "
f"because stop price {sp} would be <= stop limit {stop_gap_price}")
return False
return True
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlists and assigns and returns them again.
"""
if self._config.get('stoploss') is not None:
# Precalculate sanitized stoploss value to avoid recalculation for every pair
stoploss = 1 - abs(self._config.get('stoploss'))
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
ticker = tickers.get(p)
# Filter out assets which would not allow setting a stoploss
if not ticker or (stoploss and not self._validate_precision_filter(ticker, stoploss)):
pairlist.remove(p)
continue
return pairlist

69
freqtrade/pairlist/PriceFilter.py Normal file
View File

@@ -0,0 +1,69 @@
import logging
from copy import deepcopy
from typing import Dict, List
from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class PriceFilter(IPairList):
def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._low_price_ratio = pairlistconfig.get('low_price_ratio', 0)
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return True
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
"""
return f"{self.name} - Filtering pairs priced below {self._low_price_ratio * 100}%."
def _validate_ticker_lowprice(self, ticker) -> bool:
"""
Check if if one price-step (pip) is > than a certain barrier.
:param ticker: ticker dict as returned from ccxt.load_markets()
:param precision: Precision
:return: True if the pair can stay, false if it should be removed
"""
precision = self._exchange.markets[ticker['symbol']]['precision']['price']
compare = ticker['last'] + 1 / pow(10, precision)
changeperc = (compare - ticker['last']) / ticker['last']
if changeperc > self._low_price_ratio:
logger.info(f"Removed {ticker['symbol']} from whitelist, "
f"because 1 unit is {changeperc * 100:.3f}%")
return False
return True
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
ticker = tickers.get(p)
if not ticker:
pairlist.remove(p)
# Filter out assets which would not allow setting a stoploss
if self._low_price_ratio and not self._validate_ticker_lowprice(ticker):
pairlist.remove(p)
return pairlist

23
freqtrade/pairlist/StaticPairList.py
View File

@@ -5,6 +5,7 @@ Provides lists as configured in config.json
"""
import logging
from typing import Dict, List
from freqtrade.pairlist.IPairList import IPairList
@@ -13,18 +14,28 @@ logger = logging.getLogger(__name__)
class StaticPairList(IPairList):
def __init__(self, freqtrade, config: dict) -> None:
super().__init__(freqtrade, config)
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return False
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
-> Please overwrite in subclasses
"""
return f"{self.name}: {self.whitelist}"
return f"{self.name}"
def refresh_pairlist(self) -> None:
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Refreshes pairlists and assigns them to self._whitelist and self._blacklist respectively
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
self._whitelist = self._validate_whitelist(self._config['exchange']['pair_whitelist'])
return self._whitelist_for_active_markets(self._config['exchange']['pair_whitelist'])

104
freqtrade/pairlist/VolumePairList.py
View File

@@ -5,11 +5,12 @@ Provides lists as configured in config.json
"""
import logging
from typing import List
from cachetools import TTLCache, cached
from datetime import datetime
from typing import Dict, List
from freqtrade.pairlist.IPairList import IPairList
from freqtrade import OperationalException
from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
@@ -17,18 +18,19 @@ SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
class VolumePairList(IPairList):
def __init__(self, freqtrade, config: dict) -> None:
super().__init__(freqtrade, config)
self._whitelistconf = self._config.get('pairlist', {}).get('config')
if 'number_assets' not in self._whitelistconf:
def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
if 'number_assets' not in self._pairlistconfig:
raise OperationalException(
f'`number_assets` not specified. Please check your configuration '
'for "pairlist.config.number_assets"')
self._number_pairs = self._whitelistconf['number_assets']
self._sort_key = self._whitelistconf.get('sort_key', 'quoteVolume')
self._precision_filter = self._whitelistconf.get('precision_filter', False)
self._number_pairs = self._pairlistconfig['number_assets']
self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
if not self._freqtrade.exchange.exchange_has('fetchTickers'):
if not self._exchange.exchange_has('fetchTickers'):
raise OperationalException(
'Exchange does not support dynamic whitelist.'
'Please edit your config and restart the bot'
@@ -36,6 +38,16 @@ class VolumePairList(IPairList):
if not self._validate_keys(self._sort_key):
raise OperationalException(
f'key {self._sort_key} not in {SORT_VALUES}')
self._last_refresh = 0
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return True
def _validate_keys(self, key):
return key in SORT_VALUES
@@ -43,54 +55,54 @@ class VolumePairList(IPairList):
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
-> Please overwrite in subclasses
"""
return f"{self.name} - top {self._whitelistconf['number_assets']} volume pairs."
return f"{self.name} - top {self._pairlistconfig['number_assets']} volume pairs."
def refresh_pairlist(self) -> None:
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Refreshes pairlists and assigns them to self._whitelist and self._blacklist respectively
-> Please overwrite in subclasses
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
# Generate dynamic whitelist
self._whitelist = self._gen_pair_whitelist(
self._config['stake_currency'], self._sort_key)[:self._number_pairs]
if self._last_refresh + self.refresh_period < datetime.now().timestamp():
self._last_refresh = int(datetime.now().timestamp())
return self._gen_pair_whitelist(pairlist,
tickers,
self._config['stake_currency'],
self._sort_key,
)
else:
return pairlist
@cached(TTLCache(maxsize=1, ttl=1800))
def _gen_pair_whitelist(self, base_currency: str, key: str) -> List[str]:
def _gen_pair_whitelist(self, pairlist, tickers, base_currency: str, key: str) -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param base_currency: base currency as str
:param key: sort key (defaults to 'quoteVolume')
:param tickers: Tickers (from exchange.get_tickers()).
:return: List of pairs
"""
tickers = self._freqtrade.exchange.get_tickers()
# check length so that we make sure that '/' is actually in the string
tickers = [v for k, v in tickers.items()
if (len(k.split('/')) == 2 and k.split('/')[1] == base_currency
and v[key] is not None)]
sorted_tickers = sorted(tickers, reverse=True, key=lambda t: t[key])
if self._pairlist_pos == 0:
# If VolumePairList is the first in the list, use fresh pairlist
# check length so that we make sure that '/' is actually in the string
filtered_tickers = [v for k, v in tickers.items()
if (len(k.split('/')) == 2 and k.split('/')[1] == base_currency
and v[key] is not None)]
else:
# If other pairlist is in front, use the incomming pairlist.
filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
sorted_tickers = sorted(filtered_tickers, reverse=True, key=lambda t: t[key])
# Validate whitelist to only have active market pairs
valid_pairs = self._validate_whitelist([s['symbol'] for s in sorted_tickers])
valid_tickers = [t for t in sorted_tickers if t["symbol"] in valid_pairs]
if self._freqtrade.strategy.stoploss is not None and self._precision_filter:
stop_prices = [self._freqtrade.get_target_bid(t["symbol"], t)
* (1 - abs(self._freqtrade.strategy.stoploss)) for t in valid_tickers]
rates = [sp * 0.99 for sp in stop_prices]
logger.debug("\n".join([f"{sp} : {r}" for sp, r in zip(stop_prices[:10], rates[:10])]))
for i, t in enumerate(valid_tickers):
sp = self._freqtrade.exchange.symbol_price_prec(t["symbol"], stop_prices[i])
r = self._freqtrade.exchange.symbol_price_prec(t["symbol"], rates[i])
logger.debug(f"{t['symbol']} - {sp} : {r}")
if sp <= r:
logger.info(f"Removed {t['symbol']} from whitelist, "
f"because stop price {sp} would be <= stop limit {r}")
valid_tickers.remove(t)
pairs = [s['symbol'] for s in valid_tickers]
logger.info(f"Searching pairs: {self._whitelist}")
pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
pairs = self._verify_blacklist(pairs)
# Limit to X number of pairs
pairs = pairs[:self._number_pairs]
logger.info(f"Searching {self._number_pairs} pairs: {pairs}")
return pairs

95
freqtrade/pairlist/pairlistmanager.py Normal file
View File

@@ -0,0 +1,95 @@
"""
Static List provider
Provides lists as configured in config.json
"""
from cachetools import TTLCache, cached
import logging
from typing import Dict, List
from freqtrade import OperationalException
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.resolvers import PairListResolver
logger = logging.getLogger(__name__)
class PairListManager():
def __init__(self, exchange, config: dict) -> None:
self._exchange = exchange
self._config = config
self._whitelist = self._config['exchange'].get('pair_whitelist')
self._blacklist = self._config['exchange'].get('pair_blacklist', [])
self._pairlists: List[IPairList] = []
self._tickers_needed = False
for pl in self._config.get('pairlists', None):
if 'method' not in pl:
logger.warning(f"No method in {pl}")
continue
pairl = PairListResolver(pl.get('method'),
exchange=exchange,
pairlistmanager=self,
config=config,
pairlistconfig=pl,
pairlist_pos=len(self._pairlists)
).pairlist
self._tickers_needed = pairl.needstickers or self._tickers_needed
self._pairlists.append(pairl)
if not self._pairlists:
raise OperationalException("No Pairlist defined!")
@property
def whitelist(self) -> List[str]:
"""
Has the current whitelist
"""
return self._whitelist
@property
def blacklist(self) -> List[str]:
"""
Has the current blacklist
-> no need to overwrite in subclasses
"""
return self._blacklist
@property
def name_list(self) -> List[str]:
"""
Get list of loaded pairlists names
"""
return [p.name for p in self._pairlists]
def short_desc(self) -> List[Dict]:
"""
List of short_desc for each pairlist
"""
return [{p.name: p.short_desc()} for p in self._pairlists]
@cached(TTLCache(maxsize=1, ttl=1800))
def _get_cached_tickers(self):
return self._exchange.get_tickers()
def refresh_pairlist(self) -> None:
"""
Run pairlist through all configured pairlists.
"""
pairlist = self._whitelist.copy()
# tickers should be cached to avoid calling the exchange on each call.
tickers: Dict = {}
if self._tickers_needed:
tickers = self._get_cached_tickers()
# Process all pairlists in chain
for pl in self._pairlists:
pairlist = pl.filter_pairlist(pairlist, tickers)
# Validation against blacklist happens after the pairlists to ensure blacklist is respected.
pairlist = IPairList.verify_blacklist(pairlist, self.blacklist)
self._whitelist = pairlist

77
freqtrade/persistence.py
View File

@@ -8,17 +8,16 @@ from typing import Any, Dict, List, Optional
import arrow
from sqlalchemy import (Boolean, Column, DateTime, Float, Integer, String,
create_engine, inspect)
create_engine, desc, func, inspect)
from sqlalchemy.exc import NoSuchModuleError
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import Query
from sqlalchemy.orm.scoping import scoped_session
from sqlalchemy.orm.session import sessionmaker
from sqlalchemy import func
from sqlalchemy.pool import StaticPool
from freqtrade import OperationalException
logger = logging.getLogger(__name__)
@@ -52,9 +51,11 @@ def init(db_url: str, clean_open_orders: bool = False) -> None:
raise OperationalException(f"Given value for db_url: '{db_url}' "
f"is no valid database URL! (See {_SQL_DOCS_URL})")
session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
Trade.session = session()
Trade.query = session.query_property()
# https://docs.sqlalchemy.org/en/13/orm/contextual.html#thread-local-scope
# Scoped sessions proxy requests to the appropriate thread-local session.
# We should use the scoped_session object - not a seperately initialized version
Trade.session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
Trade.query = Trade.session.query_property()
_DECL_BASE.metadata.create_all(engine)
check_migrate(engine)
@@ -393,6 +394,37 @@ class Trade(_DECL_BASE):
profit_percent = (close_trade_price / open_trade_price) - 1
return float(f"{profit_percent:.8f}")
@staticmethod
def get_trades(trade_filter=None) -> Query:
"""
Helper function to query Trades using filters.
:param trade_filter: Optional filter to apply to trades
Can be either a Filter object, or a List of filters
e.g. `(trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True),])`
e.g. `(trade_filter=Trade.id == trade_id)`
:return: unsorted query object
"""
if trade_filter is not None:
if not isinstance(trade_filter, list):
trade_filter = [trade_filter]
return Trade.query.filter(*trade_filter)
else:
return Trade.query
@staticmethod
def get_open_trades() -> List[Any]:
"""
Query trades from persistence layer
"""
return Trade.get_trades(Trade.is_open.is_(True)).all()
@staticmethod
def get_open_order_trades():
"""
Returns all open trades
"""
return Trade.get_trades(Trade.open_order_id.isnot(None)).all()
@staticmethod
def total_open_trades_stakes() -> float:
"""
@@ -405,11 +437,38 @@ class Trade(_DECL_BASE):
return total_open_stake_amount or 0
@staticmethod
def get_open_trades() -> List[Any]:
def get_overall_performance() -> List[Dict[str, Any]]:
"""
Query trades from persistence layer
Returns List of dicts containing all Trades, including profit and trade count
"""
return Trade.query.filter(Trade.is_open.is_(True)).all()
pair_rates = Trade.session.query(
Trade.pair,
func.sum(Trade.close_profit).label('profit_sum'),
func.count(Trade.pair).label('count')
).filter(Trade.is_open.is_(False))\
.group_by(Trade.pair) \
.order_by(desc('profit_sum')) \
.all()
return [
{
'pair': pair,
'profit': rate,
'count': count
}
for pair, rate, count in pair_rates
]
@staticmethod
def get_best_pair():
"""
Get best pair with closed trade.
"""
best_pair = Trade.session.query(
Trade.pair, func.sum(Trade.close_profit).label('profit_sum')
).filter(Trade.is_open.is_(False)) \
.group_by(Trade.pair) \
.order_by(desc('profit_sum')).first()
return best_pair
@staticmethod
def stoploss_reinitialization(desired_stoploss):

35
freqtrade/plot/plotting.py
View File

@@ -39,7 +39,7 @@ def init_plotscript(config):
tickers = history.load_data(
datadir=Path(str(config.get("datadir"))),
pairs=pairs,
ticker_interval=config.get('ticker_interval', '5m'),
timeframe=config.get('ticker_interval', '5m'),
timerange=timerange,
)
@@ -47,7 +47,7 @@ def init_plotscript(config):
db_url=config.get('db_url'),
exportfilename=config.get('exportfilename'),
)
trades = history.trim_dataframe(trades, timerange, 'open_time')
return {"tickers": tickers,
"trades": trades,
"pairs": pairs,
@@ -264,12 +264,12 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
trades: pd.DataFrame) -> go.Figure:
trades: pd.DataFrame, timeframe: str) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
df_comb = combine_tickers_with_mean(tickers, "close")
# Add combined cumulative profit
df_comb = create_cum_profit(df_comb, trades, 'cum_profit')
df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
# Plot the pairs average close prices, and total profit growth
avgclose = go.Scatter(
@@ -293,19 +293,19 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
for pair in pairs:
profit_col = f'cum_profit_{pair}'
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col)
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col, timeframe)
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
return fig
def generate_plot_filename(pair, ticker_interval) -> str:
def generate_plot_filename(pair, timeframe) -> str:
"""
Generate filenames per pair/ticker_interval to be used for storing plots
Generate filenames per pair/timeframe to be used for storing plots
"""
pair_name = pair.replace("/", "_")
file_name = 'freqtrade-plot-' + pair_name + '-' + ticker_interval + '.html'
file_name = 'freqtrade-plot-' + pair_name + '-' + timeframe + '.html'
logger.info('Generate plot file for %s', pair)
@@ -316,8 +316,9 @@ def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False
"""
Generate a plot html file from pre populated fig plotly object
:param fig: Plotly Figure to plot
:param pair: Pair to plot (used as filename and Plot title)
:param ticker_interval: Used as part of the filename
:param filename: Name to store the file as
:param directory: Directory to store the file in
:param auto_open: Automatically open files saved
:return: None
"""
directory.mkdir(parents=True, exist_ok=True)
@@ -376,15 +377,17 @@ def plot_profit(config: Dict[str, Any]) -> None:
in helping out to find a good algorithm.
"""
plot_elements = init_plotscript(config)
trades = load_trades(config['trade_source'],
db_url=str(config.get('db_url')),
exportfilename=str(config.get('exportfilename')),
)
trades = plot_elements['trades']
# Filter trades to relevant pairs
trades = trades[trades['pair'].isin(plot_elements["pairs"])]
# Remove open pairs - we don't know the profit yet so can't calculate profit for these.
# Also, If only one open pair is left, then the profit-generation would fail.
trades = trades[(trades['pair'].isin(plot_elements["pairs"]))
& (~trades['close_time'].isnull())
]
# Create an average close price of all the pairs that were involved.
# this could be useful to gauge the overall market trend
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"], trades)
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"],
trades, config.get('ticker_interval', '5m'))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / "plot", auto_open=True)

36
freqtrade/resolvers/hyperopt_resolver.py
View File

@@ -1,14 +1,14 @@
# pragma pylint: disable=attribute-defined-outside-init
"""
This module load custom hyperopts
This module load custom hyperopt
"""
import logging
from pathlib import Path
from typing import Optional, Dict
from freqtrade import OperationalException
from freqtrade.constants import DEFAULT_HYPEROPT, DEFAULT_HYPEROPT_LOSS
from freqtrade.constants import DEFAULT_HYPEROPT_LOSS, USERPATH_HYPEROPTS
from freqtrade.optimize.hyperopt_interface import IHyperOpt
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
from freqtrade.resolvers import IResolver
@@ -20,7 +20,6 @@ class HyperOptResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt class
"""
__slots__ = ['hyperopt']
def __init__(self, config: Dict) -> None:
@@ -28,12 +27,18 @@ class HyperOptResolver(IResolver):
Load the custom class from config parameter
:param config: configuration dictionary
"""
if not config.get('hyperopt'):
raise OperationalException("No Hyperopt set. Please use `--hyperopt` to specify "
"the Hyperopt class to use.")
hyperopt_name = config['hyperopt']
# Verify the hyperopt is in the configuration, otherwise fallback to the default hyperopt
hyperopt_name = config.get('hyperopt') or DEFAULT_HYPEROPT
self.hyperopt = self._load_hyperopt(hyperopt_name, config,
extra_dir=config.get('hyperopt_path'))
if not hasattr(self.hyperopt, 'populate_indicators'):
logger.warning("Hyperopt class does not provide populate_indicators() method. "
"Using populate_indicators from the strategy.")
if not hasattr(self.hyperopt, 'populate_buy_trend'):
logger.warning("Hyperopt class does not provide populate_buy_trend() method. "
"Using populate_buy_trend from the strategy.")
@@ -53,7 +58,7 @@ class HyperOptResolver(IResolver):
current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
abs_paths = self.build_search_paths(config, current_path=current_path,
user_subdir='hyperopts', extra_dir=extra_dir)
user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
hyperopt = self._load_object(paths=abs_paths, object_type=IHyperOpt,
object_name=hyperopt_name, kwargs={'config': config})
@@ -69,27 +74,28 @@ class HyperOptLossResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt loss class
"""
__slots__ = ['hyperoptloss']
def __init__(self, config: Dict = None) -> None:
def __init__(self, config: Dict) -> None:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
:param config: configuration dictionary
"""
config = config or {}
# Verify the hyperopt is in the configuration, otherwise fallback to the default hyperopt
hyperopt_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS
# Verify the hyperopt_loss is in the configuration, otherwise fallback to the
# default hyperopt loss
hyperoptloss_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS
self.hyperoptloss = self._load_hyperoptloss(
hyperopt_name, config, extra_dir=config.get('hyperopt_path'))
hyperoptloss_name, config, extra_dir=config.get('hyperopt_path'))
# Assign ticker_interval to be used in hyperopt
self.hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
if not hasattr(self.hyperoptloss, 'hyperopt_loss_function'):
raise OperationalException(
f"Found hyperopt {hyperopt_name} does not implement `hyperopt_loss_function`.")
f"Found HyperoptLoss class {hyperoptloss_name} does not "
"implement `hyperopt_loss_function`.")
def _load_hyperoptloss(
self, hyper_loss_name: str, config: Dict,
@@ -104,7 +110,7 @@ class HyperOptLossResolver(IResolver):
current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
abs_paths = self.build_search_paths(config, current_path=current_path,
user_subdir='hyperopts', extra_dir=extra_dir)
user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
hyperoptloss = self._load_object(paths=abs_paths, object_type=IHyperOptLoss,
object_name=hyper_loss_name)

10
freqtrade/resolvers/iresolver.py
View File

@@ -17,13 +17,13 @@ class IResolver:
This class contains all the logic to load custom classes
"""
def build_search_paths(self, config, current_path: Path, user_subdir: str,
def build_search_paths(self, config, current_path: Path, user_subdir: Optional[str] = None,
extra_dir: Optional[str] = None) -> List[Path]:
abs_paths = [
config['user_data_dir'].joinpath(user_subdir),
current_path,
]
abs_paths: List[Path] = [current_path]
if user_subdir:
abs_paths.insert(0, config['user_data_dir'].joinpath(user_subdir))
if extra_dir:
# Add extra directory to the top of the search paths

13
freqtrade/resolvers/pairlist_resolver.py
View File

@@ -20,13 +20,18 @@ class PairListResolver(IResolver):
__slots__ = ['pairlist']
def __init__(self, pairlist_name: str, freqtrade, config: dict) -> None:
def __init__(self, pairlist_name: str, exchange, pairlistmanager,
config: dict, pairlistconfig: dict, pairlist_pos: int) -> None:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
"""
self.pairlist = self._load_pairlist(pairlist_name, config, kwargs={'freqtrade': freqtrade,
'config': config})
self.pairlist = self._load_pairlist(pairlist_name, config,
kwargs={'exchange': exchange,
'pairlistmanager': pairlistmanager,
'config': config,
'pairlistconfig': pairlistconfig,
'pairlist_pos': pairlist_pos})
def _load_pairlist(
self, pairlist_name: str, config: dict, kwargs: dict) -> IPairList:
@@ -40,7 +45,7 @@ class PairListResolver(IResolver):
current_path = Path(__file__).parent.parent.joinpath('pairlist').resolve()
abs_paths = self.build_search_paths(config, current_path=current_path,
user_subdir='pairlist', extra_dir=None)
user_subdir=None, extra_dir=None)
pairlist = self._load_object(paths=abs_paths, object_type=IPairList,
object_name=pairlist_name, kwargs=kwargs)

11
freqtrade/resolvers/strategy_resolver.py
View File

@@ -32,8 +32,11 @@ class StrategyResolver(IResolver):
"""
config = config or {}
# Verify the strategy is in the configuration, otherwise fallback to the default strategy
strategy_name = config.get('strategy') or constants.DEFAULT_STRATEGY
if not config.get('strategy'):
raise OperationalException("No strategy set. Please use `--strategy` to specify "
"the strategy class to use.")
strategy_name = config['strategy']
self.strategy: IStrategy = self._load_strategy(strategy_name,
config=config,
extra_dir=config.get('strategy_path'))
@@ -57,6 +60,7 @@ class StrategyResolver(IResolver):
("order_time_in_force", None, False),
("stake_currency", None, False),
("stake_amount", None, False),
("startup_candle_count", None, False),
("use_sell_signal", True, True),
("sell_profit_only", False, True),
("ignore_roi_if_buy_signal", False, True),
@@ -125,7 +129,8 @@ class StrategyResolver(IResolver):
current_path = Path(__file__).parent.parent.joinpath('strategy').resolve()
abs_paths = self.build_search_paths(config, current_path=current_path,
user_subdir='strategies', extra_dir=extra_dir)
user_subdir=constants.USERPATH_STRATEGY,
extra_dir=extra_dir)
if ":" in strategy_name:
logger.info("loading base64 encoded strategy")

33
freqtrade/rpc/api_server.py
View File

@@ -169,6 +169,10 @@ class ApiServer(RPC):
view_func=self._status, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/version', 'version',
view_func=self._version, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/show_config', 'show_config',
view_func=self._show_config, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/ping', 'ping',
view_func=self._ping, methods=['GET'])
# Combined actions and infos
self.app.add_url_rule(f'{BASE_URI}/blacklist', 'blacklist', view_func=self._blacklist,
@@ -224,6 +228,13 @@ class ApiServer(RPC):
msg = self._rpc_stopbuy()
return self.rest_dump(msg)
@rpc_catch_errors
def _ping(self):
"""
simple poing version
"""
return self.rest_dump({"status": "pong"})
@require_login
@rpc_catch_errors
def _version(self):
@@ -232,6 +243,14 @@ class ApiServer(RPC):
"""
return self.rest_dump({"version": __version__})
@require_login
@rpc_catch_errors
def _show_config(self):
"""
Prints the bot's version
"""
return self.rest_dump(self._rpc_show_config())
@require_login
@rpc_catch_errors
def _reload_conf(self):
@@ -265,7 +284,7 @@ class ApiServer(RPC):
stats = self._rpc_daily_profit(timescale,
self._config['stake_currency'],
self._config['fiat_display_currency']
self._config.get('fiat_display_currency', '')
)
return self.rest_dump(stats)
@@ -293,7 +312,7 @@ class ApiServer(RPC):
logger.info("LocalRPC - Profit Command Called")
stats = self._rpc_trade_statistics(self._config['stake_currency'],
self._config['fiat_display_currency']
self._config.get('fiat_display_currency')
)
return self.rest_dump(stats)
@@ -321,8 +340,11 @@ class ApiServer(RPC):
Returns the current status of the trades in json format
"""
results = self._rpc_trade_status()
return self.rest_dump(results)
try:
results = self._rpc_trade_status()
return self.rest_dump(results)
except RPCException:
return self.rest_dump([])
@require_login
@rpc_catch_errors
@@ -332,7 +354,8 @@ class ApiServer(RPC):
Returns the current status of the trades in json format
"""
results = self._rpc_balance(self._config.get('fiat_display_currency', ''))
results = self._rpc_balance(self._config['stake_currency'],
self._config.get('fiat_display_currency', ''))
return self.rest_dump(results)
@require_login

150
freqtrade/rpc/rpc.py
View File

@@ -3,17 +3,15 @@ This module contains class to define a RPC communications
"""
import logging
from abc import abstractmethod
from datetime import timedelta, datetime, date
from decimal import Decimal
from datetime import date, datetime, timedelta
from enum import Enum
from typing import Dict, Any, List, Optional
from math import isnan
from typing import Any, Dict, List, Optional, Tuple
import arrow
import sqlalchemy as sql
from numpy import mean, NAN
from pandas import DataFrame
from numpy import NAN, mean
from freqtrade import TemporaryError, DependencyException
from freqtrade import DependencyException, TemporaryError
from freqtrade.misc import shorten_date
from freqtrade.persistence import Trade
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
@@ -82,6 +80,29 @@ class RPC:
def send_msg(self, msg: Dict[str, str]) -> None:
""" Sends a message to all registered rpc modules """
def _rpc_show_config(self) -> Dict[str, Any]:
"""
Return a dict of config options.
Explicitly does NOT return the full config to avoid leakage of sensitive
information via rpc.
"""
config = self._freqtrade.config
val = {
'dry_run': config.get('dry_run', False),
'stake_currency': config['stake_currency'],
'stake_amount': config['stake_amount'],
'minimal_roi': config['minimal_roi'].copy(),
'stoploss': config['stoploss'],
'trailing_stop': config['trailing_stop'],
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset'),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached'),
'ticker_interval': config['ticker_interval'],
'exchange': config['exchange']['name'],
'strategy': config['strategy'],
}
return val
def _rpc_trade_status(self) -> List[Dict[str, Any]]:
"""
Below follows the RPC backend it is prefixed with rpc_ to raise awareness that it is
@@ -118,7 +139,7 @@ class RPC:
results.append(trade_dict)
return results
def _rpc_status_table(self) -> DataFrame:
def _rpc_status_table(self, stake_currency, fiat_display_currency: str) -> Tuple[List, List]:
trades = Trade.get_open_trades()
if not trades:
raise RPCException('no active order')
@@ -131,17 +152,28 @@ class RPC:
except DependencyException:
current_rate = NAN
trade_perc = (100 * trade.calc_profit_percent(current_rate))
trade_profit = trade.calc_profit(current_rate)
profit_str = f'{trade_perc:.2f}%'
if self._fiat_converter:
fiat_profit = self._fiat_converter.convert_amount(
trade_profit,
stake_currency,
fiat_display_currency
)
if fiat_profit and not isnan(fiat_profit):
profit_str += f" ({fiat_profit:.2f})"
trades_list.append([
trade.id,
trade.pair,
shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
f'{trade_perc:.2f}%'
profit_str
])
profitcol = "Profit"
if self._fiat_converter:
profitcol += " (" + fiat_display_currency + ")"
columns = ['ID', 'Pair', 'Since', 'Profit']
df_statuses = DataFrame.from_records(trades_list, columns=columns)
df_statuses = df_statuses.set_index(columns[0])
return df_statuses
columns = ['ID', 'Pair', 'Since', profitcol]
return trades_list, columns
def _rpc_daily_profit(
self, timescale: int,
@@ -154,12 +186,11 @@ class RPC:
for day in range(0, timescale):
profitday = today - timedelta(days=day)
trades = Trade.query \
.filter(Trade.is_open.is_(False)) \
.filter(Trade.close_date >= profitday)\
.filter(Trade.close_date < (profitday + timedelta(days=1)))\
.order_by(Trade.close_date)\
.all()
trades = Trade.get_trades(trade_filter=[
Trade.is_open.is_(False),
Trade.close_date >= profitday,
Trade.close_date < (profitday + timedelta(days=1))
]).order_by(Trade.close_date).all()
curdayprofit = sum(trade.calc_profit() for trade in trades)
profit_days[profitday] = {
'amount': f'{curdayprofit:.8f}',
@@ -192,7 +223,7 @@ class RPC:
def _rpc_trade_statistics(
self, stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
""" Returns cumulative profit statistics """
trades = Trade.query.order_by(Trade.id).all()
trades = Trade.get_trades().order_by(Trade.id).all()
profit_all_coin = []
profit_all_perc = []
@@ -221,15 +252,11 @@ class RPC:
profit_percent = trade.calc_profit_percent(rate=current_rate)
profit_all_coin.append(
trade.calc_profit(rate=Decimal(trade.close_rate or current_rate))
trade.calc_profit(rate=trade.close_rate or current_rate)
)
profit_all_perc.append(profit_percent)
best_pair = Trade.session.query(
Trade.pair, sql.func.sum(Trade.close_profit).label('profit_sum')
).filter(Trade.is_open.is_(False)) \
.group_by(Trade.pair) \
.order_by(sql.text('profit_sum DESC')).first()
best_pair = Trade.get_best_pair()
if not best_pair:
raise RPCException('no closed trade')
@@ -270,34 +297,42 @@ class RPC:
'best_rate': round(bp_rate * 100, 2),
}
def _rpc_balance(self, fiat_display_currency: str) -> Dict:
def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
""" Returns current account balance per crypto """
output = []
total = 0.0
for coin, balance in self._freqtrade.exchange.get_balances().items():
if not balance['total']:
try:
tickers = self._freqtrade.exchange.get_tickers()
except (TemporaryError, DependencyException):
raise RPCException('Error getting current tickers.')
for coin, balance in self._freqtrade.wallets.get_all_balances().items():
if not balance.total:
continue
if coin == 'BTC':
est_stake: float = 0
if coin == stake_currency:
rate = 1.0
est_stake = balance.total
else:
try:
pair = self._freqtrade.exchange.get_valid_pair_combination(coin, "BTC")
if pair.startswith("BTC"):
rate = 1.0 / self._freqtrade.get_sell_rate(pair, False)
else:
rate = self._freqtrade.get_sell_rate(pair, False)
pair = self._freqtrade.exchange.get_valid_pair_combination(coin, stake_currency)
rate = tickers.get(pair, {}).get('bid', None)
if rate:
if pair.startswith(stake_currency):
rate = 1.0 / rate
est_stake = rate * balance.total
except (TemporaryError, DependencyException):
logger.warning(f" Could not get rate for pair {coin}.")
continue
est_btc: float = rate * balance['total']
total = total + est_btc
total = total + (est_stake or 0)
output.append({
'currency': coin,
'free': balance['free'] if balance['free'] is not None else 0,
'balance': balance['total'] if balance['total'] is not None else 0,
'used': balance['used'] if balance['used'] is not None else 0,
'est_btc': est_btc,
'free': balance.free if balance.free is not None else 0,
'balance': balance.total if balance.total is not None else 0,
'used': balance.used if balance.used is not None else 0,
'est_stake': est_stake or 0,
'stake': stake_currency,
})
if total == 0.0:
if self._freqtrade.config.get('dry_run', False):
@@ -389,11 +424,8 @@ class RPC:
return {'result': 'Created sell orders for all open trades.'}
# Query for trade
trade = Trade.query.filter(
sql.and_(
Trade.id == trade_id,
Trade.is_open.is_(True)
)
trade = Trade.get_trades(
trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True), ]
).first()
if not trade:
logger.warning('forcesell: Invalid argument received')
@@ -423,7 +455,7 @@ class RPC:
# check if valid pair
# check if pair already has an open pair
trade = Trade.query.filter(Trade.is_open.is_(True)).filter(Trade.pair.is_(pair)).first()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair.is_(pair)]).first()
if trade:
raise RPCException(f'position for {pair} already open - id: {trade.id}')
@@ -432,28 +464,20 @@ class RPC:
# execute buy
if self._freqtrade.execute_buy(pair, stakeamount, price):
trade = Trade.query.filter(Trade.is_open.is_(True)).filter(Trade.pair.is_(pair)).first()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair.is_(pair)]).first()
return trade
else:
return None
def _rpc_performance(self) -> List[Dict]:
def _rpc_performance(self) -> List[Dict[str, Any]]:
"""
Handler for performance.
Shows a performance statistic from finished trades
"""
pair_rates = Trade.session.query(Trade.pair,
sql.func.sum(Trade.close_profit).label('profit_sum'),
sql.func.count(Trade.pair).label('count')) \
.filter(Trade.is_open.is_(False)) \
.group_by(Trade.pair) \
.order_by(sql.text('profit_sum DESC')) \
.all()
return [
{'pair': pair, 'profit': round(rate * 100, 2), 'count': count}
for pair, rate, count in pair_rates
]
pair_rates = Trade.get_overall_performance()
# Round and convert to %
[x.update({'profit': round(x['profit'] * 100, 2)}) for x in pair_rates]
return pair_rates
def _rpc_count(self) -> Dict[str, float]:
""" Returns the number of trades running """
@@ -469,7 +493,7 @@ class RPC:
def _rpc_whitelist(self) -> Dict:
""" Returns the currently active whitelist"""
res = {'method': self._freqtrade.pairlists.name,
res = {'method': self._freqtrade.pairlists.name_list,
'length': len(self._freqtrade.active_pair_whitelist),
'whitelist': self._freqtrade.active_pair_whitelist
}
@@ -484,7 +508,7 @@ class RPC:
and pair not in self._freqtrade.pairlists.blacklist):
self._freqtrade.pairlists.blacklist.append(pair)
res = {'method': self._freqtrade.pairlists.name,
res = {'method': self._freqtrade.pairlists.name_list,
'length': len(self._freqtrade.pairlists.blacklist),
'blacklist': self._freqtrade.pairlists.blacklist,
}

34
freqtrade/rpc/telegram.py
View File

@@ -95,6 +95,7 @@ class Telegram(RPC):
CommandHandler('daily', self._daily),
CommandHandler('count', self._count),
CommandHandler('reload_conf', self._reload_conf),
CommandHandler('show_config', self._show_config),
CommandHandler('stopbuy', self._stopbuy),
CommandHandler('whitelist', self._whitelist),
CommandHandler('blacklist', self._blacklist),
@@ -234,8 +235,9 @@ class Telegram(RPC):
:return: None
"""
try:
df_statuses = self._rpc_status_table()
message = tabulate(df_statuses, headers='keys', tablefmt='simple')
statlist, head = self._rpc_status_table(self._config['stake_currency'],
self._config.get('fiat_display_currency', ''))
message = tabulate(statlist, headers=head, tablefmt='simple')
self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@@ -323,15 +325,16 @@ class Telegram(RPC):
def _balance(self, update: Update, context: CallbackContext) -> None:
""" Handler for /balance """
try:
result = self._rpc_balance(self._config.get('fiat_display_currency', ''))
result = self._rpc_balance(self._config['stake_currency'],
self._config.get('fiat_display_currency', ''))
output = ''
for currency in result['currencies']:
if currency['est_btc'] > 0.0001:
if currency['est_stake'] > 0.0001:
curr_output = "*{currency}:*\n" \
"\t`Available: {free: .8f}`\n" \
"\t`Balance: {balance: .8f}`\n" \
"\t`Pending: {used: .8f}`\n" \
"\t`Est. BTC: {est_btc: .8f}`\n".format(**currency)
"\t`Est. {stake}: {est_stake: .8f}`\n".format(**currency)
else:
curr_output = "*{currency}:* not showing <1$ amount \n".format(**currency)
@@ -549,6 +552,7 @@ class Telegram(RPC):
"*/balance:* `Show account balance per currency`\n" \
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n" \
"*/reload_conf:* `Reload configuration file` \n" \
"*/show_config:* `Show running configuration` \n" \
"*/whitelist:* `Show current whitelist` \n" \
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs " \
"to the blacklist.` \n" \
@@ -569,6 +573,26 @@ class Telegram(RPC):
"""
self._send_msg('*Version:* `{}`'.format(__version__))
@authorized_only
def _show_config(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /show_config.
Show config information information
:param bot: telegram bot
:param update: message update
:return: None
"""
val = self._rpc_show_config()
self._send_msg(
f"*Mode:* `{'Dry-run' if val['dry_run'] else 'Live'}`\n"
f"*Exchange:* `{val['exchange']}`\n"
f"*Stake per trade:* `{val['stake_amount']} {val['stake_currency']}`\n"
f"*Minimum ROI:* `{val['minimal_roi']}`\n"
f"*{'Trailing ' if val['trailing_stop'] else ''}Stoploss:* `{val['stoploss']}`\n"
f"*Ticker Interval:* `{val['ticker_interval']}`\n"
f"*Strategy:* `{val['strategy']}`'"
)
def _send_msg(self, msg: str, parse_mode: ParseMode = ParseMode.MARKDOWN) -> None:
"""
Send given markdown message

9
freqtrade/state.py
View File

@@ -25,5 +25,12 @@ class RunMode(Enum):
BACKTEST = "backtest"
EDGE = "edge"
HYPEROPT = "hyperopt"
UTIL_EXCHANGE = "util_exchange"
UTIL_NO_EXCHANGE = "util_no_exchange"
PLOT = "plot"
OTHER = "other" # Used for plotting scripts and test
OTHER = "other"
TRADING_MODES = [RunMode.LIVE, RunMode.DRY_RUN]
OPTIMIZE_MODES = [RunMode.BACKTEST, RunMode.EDGE, RunMode.HYPEROPT]
NON_UTIL_MODES = TRADING_MODES + OPTIMIZE_MODES

6
freqtrade/strategy/default_strategy.py
View File

@@ -39,6 +39,9 @@ class DefaultStrategy(IStrategy):
'stoploss_on_exchange': False
}
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 20
# Optional time in force for orders
order_time_in_force = {
'buy': 'gtc',
@@ -105,9 +108,6 @@ class DefaultStrategy(IStrategy):
# EMA - Exponential Moving Average
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

8
freqtrade/strategy/interface.py
View File

@@ -103,11 +103,14 @@ class IStrategy(ABC):
# run "populate_indicators" only for new candle
process_only_new_candles: bool = False
# Count of candles the strategy requires before producing valid signals
startup_candle_count: int = 0
# Class level variables (intentional) containing
# the dataprovider (dp) (access to other candles, historic data, ...)
# and wallets - access to the current balance.
dp: DataProvider
wallets: Wallets
dp: Optional[DataProvider] = None
wallets: Optional[Wallets] = None
def __init__(self, config: dict) -> None:
self.config = config
@@ -421,6 +424,7 @@ class IStrategy(ABC):
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
Used by optimize operations only, not during dry / live runs.
"""
return {pair: self.advise_indicators(pair_data, {'pair': pair})
for pair, pair_data in tickerdata.items()}

127
freqtrade/templates/base_hyperopt.py.j2 Normal file
View File

@@ -0,0 +1,127 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# --- Do not remove these libs ---
from functools import reduce
from typing import Any, Callable, Dict, List
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer, Real # noqa
from freqtrade.optimize.hyperopt_interface import IHyperOpt
# --------------------------------
# Add your lib to import here
import talib.abstract as ta # noqa
import freqtrade.vendor.qtpylib.indicators as qtpylib
class {{ hyperopt }}(IHyperOpt):
"""
This is a Hyperopt template to get you started.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
You should:
- Add any lib you need to build your hyperopt.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
Sample implementation of these methods can be found in
https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
"""
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
{{ buy_guards | indent(12) }}
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
{{ buy_space | indent(12) }}
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
{{ sell_guards | indent(12) }}
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
{{ sell_space | indent(12) }}
]

138
freqtrade/templates/base_strategy.py.j2 Normal file
View File

@@ -0,0 +1,138 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class {{ strategy }}(IStrategy):
"""
This is a strategy template to get you started.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 2
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"60": 0.01,
"30": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.10
# Trailing stoploss
trailing_stop = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal ticker interval for the strategy.
ticker_interval = '5m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 20
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
{{ indicators | indent(8) }}
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
{{ buy_trend | indent(16) }}
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
{{ sell_trend | indent(16) }}
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

196
freqtrade/templates/sample_hyperopt.py Normal file
View File

@@ -0,0 +1,196 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# --- Do not remove these libs ---
from functools import reduce
from typing import Any, Callable, Dict, List
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer, Real # noqa
from freqtrade.optimize.hyperopt_interface import IHyperOpt
# --------------------------------
# Add your lib to import here
import talib.abstract as ta # noqa
import freqtrade.vendor.qtpylib.indicators as qtpylib
class SampleHyperOpt(IHyperOpt):
"""
This is a sample Hyperopt to inspire you.
Feel free to customize it.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
You should:
- Rename the class name to some unique name.
- Add any methods you want to build your hyperopt.
- Add any lib you need to build your hyperopt.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
Sample implementation of these methods can be found in
https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
"""
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include buy space.
"""
dataframe.loc[
(
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['mfi'] < 16) &
(dataframe['adx'] > 25) &
(dataframe['rsi'] < 21)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include sell space.
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe

271
freqtrade/templates/sample_hyperopt_advanced.py Normal file
View File

@@ -0,0 +1,271 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# --- Do not remove these libs ---
from functools import reduce
from typing import Any, Callable, Dict, List
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer, Real # noqa
from freqtrade.optimize.hyperopt_interface import IHyperOpt
# --------------------------------
# Add your lib to import here
import talib.abstract as ta # noqa
import freqtrade.vendor.qtpylib.indicators as qtpylib
class AdvancedSampleHyperOpt(IHyperOpt):
"""
This is a sample hyperopt to inspire you.
Feel free to customize it.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
You should:
- Rename the class name to some unique name.
- Add any methods you want to build your hyperopt.
- Add any lib you need to build your hyperopt.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
This sample illustrates how to override these methods.
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
"""
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by hyperopt
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use
"""
# print(params)
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
This implementation generates the default legacy Freqtrade ROI tables.
Change it if you need different number of steps in the generated
ROI tables or other structure of the ROI tables.
Please keep it aligned with parameters in the 'roi' optimization
hyperspace defined by the roi_space method.
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
Override it if you need some different ranges for the parameters in the
'roi' optimization hyperspace.
Please keep it aligned with the implementation of the
generate_roi_table method.
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss Value to search
Override it if you need some different range for the parameter in the
'stoploss' optimization hyperspace.
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators.
Can be a copy of the corresponding method from the strategy,
or will be loaded from the strategy.
Must align to populate_indicators used (either from this File, or from the strategy)
Only used when --spaces does not include buy
"""
dataframe.loc[
(
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['mfi'] < 16) &
(dataframe['adx'] > 25) &
(dataframe['rsi'] < 21)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators.
Can be a copy of the corresponding method from the strategy,
or will be loaded from the strategy.
Must align to populate_indicators used (either from this File, or from the strategy)
Only used when --spaces does not include sell
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe

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from math import exp
from datetime import datetime
from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss
# Define some constants:
# set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
TARGET_TRADES = 600
# This is assumed to be expected avg profit * expected trade count.
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
# self.expected_max_profit = 3.85
# Check that the reported Σ% values do not exceed this!
# Note, this is ratio. 3.85 stated above means 385Σ%.
EXPECTED_MAX_PROFIT = 3.0
# max average trade duration in minutes
# if eval ends with higher value, we consider it a failed eval
MAX_ACCEPTED_TRADE_DURATION = 300
class SampleHyperOptLoss(IHyperOptLoss):
"""
Defines the default loss function for hyperopt
This is intended to give you some inspiration for your own loss function.
The Function needs to return a number (float) - which becomes for better backtest results.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
"""
total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss
return result

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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# This class is a sample. Feel free to customize it.
class SampleStrategy(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 2
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"60": 0.01,
"30": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.10
# Trailing stoploss
trailing_stop = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal ticker interval for the strategy.
ticker_interval = '5m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 20
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# # Awesome oscillator
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
# dataframe['cci'] = ta.CCI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stoch
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stoch RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Overlap Studies
# ------------------------------------
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
# # Hammer: values [0, 100]
# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# # Inverted Hammer: values [0, 100]
# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# # Dragonfly Doji: values [0, 100]
# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# # Piercing Line: values [0, 100]
# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# # Morningstar: values [0, 100]
# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# # Three White Soldiers: values [0, 100]
# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
# # Hanging Man: values [0, 100]
# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# # Shooting Star: values [0, 100]
# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# # Gravestone Doji: values [0, 100]
# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# # Dark Cloud Cover: values [0, 100]
# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# # Evening Doji Star: values [0, 100]
# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# # Evening Star: values [0, 100]
# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
# # Three Line Strike: values [0, -100, 100]
# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# # Spinning Top: values [0, -100, 100]
# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# # Engulfing: values [0, -100, 100]
# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# # Harami: values [0, -100, 100]
# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# # Three Outside Up/Down: values [0, -100, 100]
# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# # Three Inside Up/Down: values [0, -100, 100]
# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
# # Chart type
# # ------------------------------------
# # Heikinashi stategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']
# dataframe['ha_high'] = heikinashi['high']
# dataframe['ha_low'] = heikinashi['low']
# Retrieve best bid and best ask from the orderbook
# ------------------------------------
"""
# first check if dataprovider is available
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
"""
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

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freqtrade/templates/strategy_analysis_example.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Strategy analysis example\n",
"\n",
"Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"# Customize these according to your needs.\n",
"\n",
"# Define some constants\n",
"timeframe = \"5m\"\n",
"# Name of the strategy class\n",
"strategy_name = 'SampleStrategy'\n",
"# Path to user data\n",
"user_data_dir = Path('user_data')\n",
"# Location of the strategy\n",
"strategy_location = user_data_dir / 'strategies'\n",
"# Location of the data\n",
"data_location = Path(user_data_dir, 'data', 'binance')\n",
"# Pair to analyze - Only use one pair here\n",
"pair = \"BTC_USDT\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load data using values set above\n",
"from freqtrade.data.history import load_pair_history\n",
"\n",
"candles = load_pair_history(datadir=data_location,\n",
" timeframe=timeframe,\n",
" pair=pair)\n",
"\n",
"# Confirm success\n",
"print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
"candles.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load and run strategy\n",
"* Rerun each time the strategy file is changed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load strategy using values set above\n",
"from freqtrade.resolvers import StrategyResolver\n",
"strategy = StrategyResolver({'strategy': strategy_name,\n",
" 'user_data_dir': user_data_dir,\n",
" 'strategy_path': strategy_location}).strategy\n",
"\n",
"# Generate buy/sell signals using strategy\n",
"df = strategy.analyze_ticker(candles, {'pair': pair})\n",
"df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Display the trade details\n",
"\n",
"* Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
"* Some possible problems\n",
" * Columns with NaN values at the end of the dataframe\n",
" * Columns used in `crossed*()` functions with completely different units\n",
"* Comparison with full backtest\n",
" * having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.\n",
" * Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple \"buy\" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot (\"max_open_trades\") is still available), or on one of the middle signals, as soon as a \"slot\" becomes available. \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Report results\n",
"print(f\"Generated {df['buy'].sum()} buy signals\")\n",
"data = df.set_index('date', drop=False)\n",
"data.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load existing objects into a Jupyter notebook\n",
"\n",
"The following cells assume that you have already generated data using the cli. \n",
"They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load backtest results to pandas dataframe\n",
"\n",
"Analyze a trades dataframe (also used below for plotting)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.data.btanalysis import load_backtest_data\n",
"\n",
"# Load backtest results\n",
"trades = load_backtest_data(user_data_dir / \"backtest_results/backtest-result.json\")\n",
"\n",
"# Show value-counts per pair\n",
"trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load live trading results into a pandas dataframe\n",
"\n",
"In case you did already some trading and want to analyze your performance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.data.btanalysis import load_trades_from_db\n",
"\n",
"# Fetch trades from database\n",
"trades = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
"\n",
"# Display results\n",
"trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyze the loaded trades for trade parallelism\n",
"This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.\n",
"\n",
"`analyze_trade_parallelism()` returns a timeseries dataframe with an \"open_trades\" column, specifying the number of open trades for each candle."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.data.btanalysis import analyze_trade_parallelism\n",
"\n",
"# Analyze the above\n",
"parallel_trades = analyze_trade_parallelism(trades, '5m')\n",
"\n",
"\n",
"parallel_trades.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot results\n",
"\n",
"Freqtrade offers interactive plotting capabilities based on plotly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.plot.plotting import generate_candlestick_graph\n",
"# Limit graph period to keep plotly quick and reactive\n",
"\n",
"data_red = data['2019-06-01':'2019-06-10']\n",
"# Generate candlestick graph\n",
"graph = generate_candlestick_graph(pair=pair,\n",
" data=data_red,\n",
" trades=trades,\n",
" indicators1=['sma20', 'ema50', 'ema55'],\n",
" indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']\n",
" )\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Show graph inline\n",
"# graph.show()\n",
"\n",
"# Render graph in a seperate window\n",
"graph.show(renderer=\"browser\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data."
]
}
],
"metadata": {
"file_extension": ".py",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
},
"mimetype": "text/x-python",
"name": "python",
"npconvert_exporter": "python",
"pygments_lexer": "ipython3",
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
},
"version": 3
},
"nbformat": 4,
"nbformat_minor": 4
}

3
freqtrade/templates/subtemplates/buy_trend_full.j2 Normal file
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@@ -0,0 +1,3 @@
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising

1
freqtrade/templates/subtemplates/buy_trend_minimal.j2 Normal file
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@@ -0,0 +1 @@
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30

8
freqtrade/templates/subtemplates/hyperopt_buy_guards_full.j2 Normal file
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@@ -0,0 +1,8 @@
if params.get('mfi-enabled'):
conditions.append(dataframe['mfi'] < params['mfi-value'])
if params.get('fastd-enabled'):
conditions.append(dataframe['fastd'] < params['fastd-value'])
if params.get('adx-enabled'):
conditions.append(dataframe['adx'] > params['adx-value'])
if params.get('rsi-enabled'):
conditions.append(dataframe['rsi'] < params['rsi-value'])

2
freqtrade/templates/subtemplates/hyperopt_buy_guards_minimal.j2 Normal file
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@@ -0,0 +1,2 @@
if params.get('rsi-enabled'):
conditions.append(dataframe['rsi'] < params['rsi-value'])

9
freqtrade/templates/subtemplates/hyperopt_buy_space_full.j2 Normal file
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@@ -0,0 +1,9 @@
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

3
freqtrade/templates/subtemplates/hyperopt_buy_space_minimal.j2 Normal file
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@@ -0,0 +1,3 @@
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

8
freqtrade/templates/subtemplates/hyperopt_sell_guards_full.j2 Normal file
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@@ -0,0 +1,8 @@
if params.get('sell-mfi-enabled'):
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if params.get('sell-fastd-enabled'):
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if params.get('sell-adx-enabled'):
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if params.get('sell-rsi-enabled'):
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

2
freqtrade/templates/subtemplates/hyperopt_sell_guards_minimal.j2 Normal file
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@@ -0,0 +1,2 @@
if params.get('sell-rsi-enabled'):
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

11
freqtrade/templates/subtemplates/hyperopt_sell_space_full.j2 Normal file
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@@ -0,0 +1,11 @@
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')

5
freqtrade/templates/subtemplates/hyperopt_sell_space_minimal.j2 Normal file
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@@ -0,0 +1,5 @@
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')

161
freqtrade/templates/subtemplates/indicators_full.j2 Normal file
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@@ -0,0 +1,161 @@
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# # Awesome oscillator
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
# dataframe['cci'] = ta.CCI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stoch
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stoch RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Overlap Studies
# ------------------------------------
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
# # Hammer: values [0, 100]
# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# # Inverted Hammer: values [0, 100]
# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# # Dragonfly Doji: values [0, 100]
# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# # Piercing Line: values [0, 100]
# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# # Morningstar: values [0, 100]
# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# # Three White Soldiers: values [0, 100]
# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
# # Hanging Man: values [0, 100]
# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# # Shooting Star: values [0, 100]
# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# # Gravestone Doji: values [0, 100]
# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# # Dark Cloud Cover: values [0, 100]
# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# # Evening Doji Star: values [0, 100]
# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# # Evening Star: values [0, 100]
# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
# # Three Line Strike: values [0, -100, 100]
# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# # Spinning Top: values [0, -100, 100]
# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# # Engulfing: values [0, -100, 100]
# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# # Harami: values [0, -100, 100]
# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# # Three Outside Up/Down: values [0, -100, 100]
# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# # Three Inside Up/Down: values [0, -100, 100]
# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
# # Chart type
# # ------------------------------------
# # Heikinashi stategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']
# dataframe['ha_high'] = heikinashi['high']
# dataframe['ha_low'] = heikinashi['low']
# Retrieve best bid and best ask from the orderbook
# ------------------------------------
"""
# first check if dataprovider is available
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
"""

17
freqtrade/templates/subtemplates/indicators_minimal.j2 Normal file
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@@ -0,0 +1,17 @@
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Retrieve best bid and best ask from the orderbook
# ------------------------------------
"""
# first check if dataprovider is available
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
"""

3
freqtrade/templates/subtemplates/sell_trend_full.j2 Normal file
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@@ -0,0 +1,3 @@
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling

1
freqtrade/templates/subtemplates/sell_trend_minimal.j2 Normal file
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@@ -0,0 +1 @@
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70

131
freqtrade/utils.py
View File

@@ -1,3 +1,4 @@
import csv
import logging
import sys
from collections import OrderedDict
@@ -5,19 +6,21 @@ from pathlib import Path
from typing import Any, Dict, List
import arrow
import csv
import rapidjson
from tabulate import tabulate
from freqtrade import OperationalException
from freqtrade.configuration import Configuration, TimeRange
from freqtrade.configuration.directory_operations import create_userdata_dir
from freqtrade.configuration import (Configuration, TimeRange,
remove_credentials)
from freqtrade.configuration.directory_operations import (copy_sample_files,
create_userdata_dir)
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGY
from freqtrade.data.history import (convert_trades_to_ohlcv,
refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
from freqtrade.exchange import (available_exchanges, ccxt_exchanges, market_is_active,
symbol_is_pair)
from freqtrade.misc import plural
from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
market_is_active, symbol_is_pair)
from freqtrade.misc import plural, render_template
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
@@ -33,14 +36,31 @@ def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str
configuration = Configuration(args, method)
config = configuration.get_config()
config['exchange']['dry_run'] = True
# Ensure we do not use Exchange credentials
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
remove_credentials(config)
return config
def start_trading(args: Dict[str, Any]) -> int:
"""
Main entry point for trading mode
"""
from freqtrade.worker import Worker
# Load and run worker
worker = None
try:
worker = Worker(args)
worker.run()
except KeyboardInterrupt:
logger.info('SIGINT received, aborting ...')
finally:
if worker:
logger.info("worker found ... calling exit")
worker.exit()
return 0
def start_list_exchanges(args: Dict[str, Any]) -> None:
"""
Print available exchanges
@@ -59,22 +79,105 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
def start_create_userdir(args: Dict[str, Any]) -> None:
"""
Create "user_data" directory to contain user data strategies, hyperopts, ...)
Create "user_data" directory to contain user data strategies, hyperopt, ...)
:param args: Cli args from Arguments()
:return: None
"""
if "user_data_dir" in args and args["user_data_dir"]:
create_userdata_dir(args["user_data_dir"], create_dir=True)
userdir = create_userdata_dir(args["user_data_dir"], create_dir=True)
copy_sample_files(userdir, overwrite=args["reset"])
else:
logger.warning("`create-userdir` requires --userdir to be set.")
sys.exit(1)
def deploy_new_strategy(strategy_name, strategy_path: Path, subtemplate: str):
"""
Deploy new strategy from template to strategy_path
"""
indicators = render_template(templatefile=f"subtemplates/indicators_{subtemplate}.j2",)
buy_trend = render_template(templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",)
sell_trend = render_template(templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",)
strategy_text = render_template(templatefile='base_strategy.py.j2',
arguments={"strategy": strategy_name,
"indicators": indicators,
"buy_trend": buy_trend,
"sell_trend": sell_trend,
})
logger.info(f"Writing strategy to `{strategy_path}`.")
strategy_path.write_text(strategy_text)
def start_new_strategy(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if "strategy" in args and args["strategy"]:
if args["strategy"] == "DefaultStrategy":
raise OperationalException("DefaultStrategy is not allowed as name.")
new_path = config['user_data_dir'] / USERPATH_STRATEGY / (args["strategy"] + ".py")
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "
"Please choose another Strategy Name.")
deploy_new_strategy(args['strategy'], new_path, args['template'])
else:
raise OperationalException("`new-strategy` requires --strategy to be set.")
def deploy_new_hyperopt(hyperopt_name, hyperopt_path: Path, subtemplate: str):
"""
Deploys a new hyperopt template to hyperopt_path
"""
buy_guards = render_template(
templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",)
sell_guards = render_template(
templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",)
buy_space = render_template(
templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",)
sell_space = render_template(
templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",)
strategy_text = render_template(templatefile='base_hyperopt.py.j2',
arguments={"hyperopt": hyperopt_name,
"buy_guards": buy_guards,
"sell_guards": sell_guards,
"buy_space": buy_space,
"sell_space": sell_space,
})
logger.info(f"Writing hyperopt to `{hyperopt_path}`.")
hyperopt_path.write_text(strategy_text)
def start_new_hyperopt(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if "hyperopt" in args and args["hyperopt"]:
if args["hyperopt"] == "DefaultHyperopt":
raise OperationalException("DefaultHyperopt is not allowed as name.")
new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args["hyperopt"] + ".py")
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "
"Please choose another Strategy Name.")
deploy_new_hyperopt(args['hyperopt'], new_path, args['template'])
else:
raise OperationalException("`new-hyperopt` requires --hyperopt to be set.")
def start_download_data(args: Dict[str, Any]) -> None:
"""
Download data (former download_backtest_data.py script)
"""
config = setup_utils_configuration(args, RunMode.OTHER)
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
timerange = TimeRange()
if 'days' in config:
@@ -123,7 +226,7 @@ def start_list_timeframes(args: Dict[str, Any]) -> None:
"""
Print ticker intervals (timeframes) available on Exchange
"""
config = setup_utils_configuration(args, RunMode.OTHER)
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Do not use ticker_interval set in the config
config['ticker_interval'] = None
@@ -144,7 +247,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
:param pairs_only: if True print only pairs, otherwise print all instruments (markets)
:return: None
"""
config = setup_utils_configuration(args, RunMode.OTHER)
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Init exchange
exchange = ExchangeResolver(config['exchange']['name'], config, validate=False).exchange

5
freqtrade/wallets.py
View File

@@ -2,7 +2,7 @@
""" Wallet """
import logging
from typing import Dict, NamedTuple
from typing import Dict, NamedTuple, Any
from freqtrade.exchange import Exchange
from freqtrade import constants
@@ -72,3 +72,6 @@ class Wallets:
)
logger.info('Wallets synced.')
def get_all_balances(self) -> Dict[str, Any]:
return self._wallets