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Merge branch 'develop' into pr/gmatheu/4746

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This commit is contained in:
Matthias
2021-05-23 16:03:11 +02:00
parent 77302ea178 971d5b2ecc
commit f760b4a789
166 changed files with 6354 additions and 2939 deletions
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7
freqtrade/commands/arguments.py
View File

@@ -17,7 +17,7 @@ ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"]
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
"max_open_trades", "stake_amount", "fee"]
"max_open_trades", "stake_amount", "fee", "pairs"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
"enable_protections", "dry_run_wallet",
@@ -60,8 +60,9 @@ ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "timerange", "download_trades", "exchange",
"timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "timerange",
"download_trades", "exchange", "timeframes", "erase", "dataformat_ohlcv",
"dataformat_trades"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
"db_url", "trade_source", "export", "exportfilename",

32
freqtrade/commands/build_config_commands.py
View File

@@ -1,9 +1,11 @@
import logging
import secrets
from pathlib import Path
from typing import Any, Dict, List
from questionary import Separator, prompt
from freqtrade.configuration.directory_operations import chown_user_directory
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, available_exchanges
@@ -138,6 +140,32 @@ def ask_user_config() -> Dict[str, Any]:
"message": "Insert Telegram chat id",
"when": lambda x: x['telegram']
},
{
"type": "confirm",
"name": "api_server",
"message": "Do you want to enable the Rest API (includes FreqUI)?",
"default": False,
},
{
"type": "text",
"name": "api_server_listen_addr",
"message": "Insert Api server Listen Address (best left untouched default!)",
"default": "127.0.0.1",
"when": lambda x: x['api_server']
},
{
"type": "text",
"name": "api_server_username",
"message": "Insert api-server username",
"default": "freqtrader",
"when": lambda x: x['api_server']
},
{
"type": "text",
"name": "api_server_password",
"message": "Insert api-server password",
"when": lambda x: x['api_server']
},
]
answers = prompt(questions)
@@ -145,6 +173,9 @@ def ask_user_config() -> Dict[str, Any]:
# Interrupted questionary sessions return an empty dict.
raise OperationalException("User interrupted interactive questions.")
# Force JWT token to be a random string
answers['api_server_jwt_key'] = secrets.token_hex()
return answers
@@ -186,6 +217,7 @@ def start_new_config(args: Dict[str, Any]) -> None:
"""
config_path = Path(args['config'][0])
chown_user_directory(config_path.parent)
if config_path.exists():
overwrite = ask_user_overwrite(config_path)
if overwrite:

13
freqtrade/commands/cli_options.py
View File

@@ -118,7 +118,7 @@ AVAILABLE_CLI_OPTIONS = {
# Optimize common
"timeframe": Arg(
'-i', '--timeframe', '--ticker-interval',
help='Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).',
help='Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).',
),
"timerange": Arg(
'--timerange',
@@ -195,6 +195,7 @@ AVAILABLE_CLI_OPTIONS = {
'--hyperopt',
help='Specify hyperopt class name which will be used by the bot.',
metavar='NAME',
required=False,
),
"hyperopt_path": Arg(
'--hyperopt-path',
@@ -266,7 +267,7 @@ AVAILABLE_CLI_OPTIONS = {
default=1,
),
"hyperopt_loss": Arg(
'--hyperopt-loss',
'--hyperopt-loss', '--hyperoptloss',
help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
'Different functions can generate completely different results, '
'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
@@ -329,7 +330,7 @@ AVAILABLE_CLI_OPTIONS = {
# Script options
"pairs": Arg(
'-p', '--pairs',
help='Show profits for only these pairs. Pairs are space-separated.',
help='Limit command to these pairs. Pairs are space-separated.',
nargs='+',
),
# Download data
@@ -344,6 +345,12 @@ AVAILABLE_CLI_OPTIONS = {
type=check_int_positive,
metavar='INT',
),
"new_pairs_days": Arg(
'--new-pairs-days',
help='Download data of new pairs for given number of days. Default: `%(default)s`.',
type=check_int_positive,
metavar='INT',
),
"download_trades": Arg(
'--dl-trades',
help='Download trades instead of OHLCV data. The bot will resample trades to the '

9
freqtrade/commands/data_commands.py
View File

@@ -62,8 +62,8 @@ def start_download_data(args: Dict[str, Any]) -> None:
if config.get('download_trades'):
pairs_not_available = refresh_backtest_trades_data(
exchange, pairs=expanded_pairs, datadir=config['datadir'],
timerange=timerange, erase=bool(config.get('erase')),
data_format=config['dataformat_trades'])
timerange=timerange, new_pairs_days=config['new_pairs_days'],
erase=bool(config.get('erase')), data_format=config['dataformat_trades'])
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
@@ -75,8 +75,9 @@ def start_download_data(args: Dict[str, Any]) -> None:
else:
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
data_format=config['dataformat_ohlcv'])
datadir=config['datadir'], timerange=timerange,
new_pairs_days=config['new_pairs_days'],
erase=bool(config.get('erase')), data_format=config['dataformat_ohlcv'])
except KeyboardInterrupt:
sys.exit("SIGINT received, aborting ...")

78
freqtrade/commands/hyperopt_commands.py
View File

@@ -7,6 +7,7 @@ from colorama import init as colorama_init
from freqtrade.configuration import setup_utils_configuration
from freqtrade.data.btanalysis import get_latest_hyperopt_file
from freqtrade.exceptions import OperationalException
from freqtrade.optimize.optimize_reports import show_backtest_result
from freqtrade.state import RunMode
@@ -125,6 +126,12 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
if epochs:
val = epochs[n]
metrics = val['results_metrics']
if 'strategy_name' in metrics:
show_backtest_result(metrics['strategy_name'], metrics,
metrics['stake_currency'])
HyperoptTools.print_epoch_details(val, total_epochs, print_json, no_header,
header_str="Epoch details")
@@ -132,11 +139,13 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
"""
Filter our items from the list of hyperopt results
TODO: after 2021.5 remove all "legacy" mode queries.
"""
if filteroptions['only_best']:
epochs = [x for x in epochs if x['is_best']]
if filteroptions['only_profitable']:
epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
epochs = [x for x in epochs if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total', 0)) > 0]
epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
@@ -153,34 +162,55 @@ def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
return epochs
def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
"""
Filter epochs with trade-counts > trades
"""
return [
x for x in epochs
if x['results_metrics'].get(
'trade_count', x['results_metrics'].get('total_trades', 0)
) > trade_count
]
def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_trades'] > 0:
epochs = [
x for x in epochs
if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
]
epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
if filteroptions['filter_max_trades'] > 0:
epochs = [
x for x in epochs
if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
if x['results_metrics'].get(
'trade_count', x['results_metrics'].get('total_trades')
) < filteroptions['filter_max_trades']
]
return epochs
def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
def get_duration_value(x):
# Duration in minutes ...
if 'duration' in x['results_metrics']:
return x['results_metrics']['duration']
else:
# New mode
avg = x['results_metrics']['holding_avg']
return avg.total_seconds() // 60
if filteroptions['filter_min_avg_time'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
if get_duration_value(x) > filteroptions['filter_min_avg_time']
]
if filteroptions['filter_max_avg_time'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
if get_duration_value(x) < filteroptions['filter_max_avg_time']
]
return epochs
@@ -189,28 +219,36 @@ def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_avg_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit']
if x['results_metrics'].get(
'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
) > filteroptions['filter_min_avg_profit']
]
if filteroptions['filter_max_avg_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit']
if x['results_metrics'].get(
'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
) < filteroptions['filter_max_avg_profit']
]
if filteroptions['filter_min_total_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total_abs', 0)
) > filteroptions['filter_min_total_profit']
]
if filteroptions['filter_max_total_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total_abs', 0)
) < filteroptions['filter_max_total_profit']
]
return epochs
@@ -218,11 +256,11 @@ def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_objective'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
if filteroptions['filter_max_objective'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]

18
freqtrade/commands/list_commands.py
View File

@@ -13,7 +13,7 @@ from tabulate import tabulate
from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import available_exchanges, ccxt_exchanges, market_is_active
from freqtrade.exchange import market_is_active, validate_exchanges
from freqtrade.misc import plural
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
@@ -28,14 +28,18 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
:param args: Cli args from Arguments()
:return: None
"""
exchanges = ccxt_exchanges() if args['list_exchanges_all'] else available_exchanges()
exchanges = validate_exchanges(args['list_exchanges_all'])
if args['print_one_column']:
print('\n'.join(exchanges))
print('\n'.join([e[0] for e in exchanges]))
else:
if args['list_exchanges_all']:
print(f"All exchanges supported by the ccxt library: {', '.join(exchanges)}")
print("All exchanges supported by the ccxt library:")
else:
print(f"Exchanges available for Freqtrade: {', '.join(exchanges)}")
print("Exchanges available for Freqtrade:")
exchanges = [e for e in exchanges if e[1] is not False]
print(tabulate(exchanges, headers=['Exchange name', 'Valid', 'reason']))
def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
@@ -99,7 +103,7 @@ def start_list_hyperopts(args: Dict[str, Any]) -> None:
def start_list_timeframes(args: Dict[str, Any]) -> None:
"""
Print ticker intervals (timeframes) available on Exchange
Print timeframes available on Exchange
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Do not use timeframe set in the config
@@ -177,7 +181,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
# human-readable formats.
print()
if len(pairs):
if pairs:
if args.get('print_list', False):
# print data as a list, with human-readable summary
print(f"{summary_str}: {', '.join(pairs.keys())}.")

14
freqtrade/configuration/check_exchange.py
View File

@@ -2,8 +2,8 @@ import logging
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, get_exchange_bad_reason, is_exchange_bad,
is_exchange_known_ccxt, is_exchange_officially_supported)
from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
is_exchange_officially_supported, validate_exchange)
from freqtrade.state import RunMode
@@ -57,9 +57,13 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
f'{", ".join(available_exchanges())}'
)
if check_for_bad and is_exchange_bad(exchange):
raise OperationalException(f'Exchange "{exchange}" is known to not work with the bot yet. '
f'Reason: {get_exchange_bad_reason(exchange)}')
valid, reason = validate_exchange(exchange)
if not valid:
if check_for_bad:
raise OperationalException(f'Exchange "{exchange}" will not work with Freqtrade. '
f'Reason: {reason}')
else:
logger.warning(f'Exchange "{exchange}" will not work with Freqtrade. Reason: {reason}')
if is_exchange_officially_supported(exchange):
logger.info(f'Exchange "{exchange}" is officially supported '

5
freqtrade/configuration/config_validation.py
View File

@@ -149,11 +149,6 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
if not conf.get('edge', {}).get('enabled'):
return
if conf.get('pairlist', {}).get('method') == 'VolumePairList':
raise OperationalException(
"Edge and VolumePairList are incompatible, "
"Edge will override whatever pairs VolumePairlist selects."
)
if not conf.get('ask_strategy', {}).get('use_sell_signal', True):
raise OperationalException(
"Edge requires `use_sell_signal` to be True, otherwise no sells will happen."

22
freqtrade/configuration/configuration.py
View File

@@ -11,10 +11,10 @@ from freqtrade import constants
from freqtrade.configuration.check_exchange import check_exchange
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.configuration.load_config import load_config_file, load_file
from freqtrade.exceptions import OperationalException
from freqtrade.loggers import setup_logging
from freqtrade.misc import deep_merge_dicts, json_load
from freqtrade.misc import deep_merge_dicts
from freqtrade.state import NON_UTIL_MODES, TRADING_MODES, RunMode
@@ -75,8 +75,6 @@ class Configuration:
# Normalize config
if 'internals' not in config:
config['internals'] = {}
# TODO: This can be deleted along with removal of deprecated
# experimental settings
if 'ask_strategy' not in config:
config['ask_strategy'] = {}
@@ -108,6 +106,8 @@ class Configuration:
self._process_plot_options(config)
self._process_data_options(config)
# Check if the exchange set by the user is supported
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
@@ -399,6 +399,11 @@ class Configuration:
self._args_to_config(config, argname='dataformat_trades',
logstring='Using "{}" to store trades data.')
def _process_data_options(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='new_pairs_days',
logstring='Detected --new-pairs-days: {}')
def _process_runmode(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='dry_run',
@@ -445,6 +450,7 @@ class Configuration:
"""
if "pairs" in config:
config['exchange']['pair_whitelist'] = config['pairs']
return
if "pairs_file" in self.args and self.args["pairs_file"]:
@@ -454,9 +460,8 @@ class Configuration:
# or if pairs file is specified explicitely
if not pairs_file.exists():
raise OperationalException(f'No pairs file found with path "{pairs_file}".')
with pairs_file.open('r') as f:
config['pairs'] = json_load(f)
config['pairs'].sort()
config['pairs'] = load_file(pairs_file)
config['pairs'].sort()
return
if 'config' in self.args and self.args['config']:
@@ -466,7 +471,6 @@ class Configuration:
# Fall back to /dl_path/pairs.json
pairs_file = config['datadir'] / 'pairs.json'
if pairs_file.exists():
with pairs_file.open('r') as f:
config['pairs'] = json_load(f)
config['pairs'] = load_file(pairs_file)
if 'pairs' in config:
config['pairs'].sort()

19
freqtrade/configuration/directory_operations.py
View File

@@ -24,6 +24,21 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Pat
return folder
def chown_user_directory(directory: Path) -> None:
"""
Use Sudo to change permissions of the home-directory if necessary
Only applies when running in docker!
"""
import os
if os.environ.get('FT_APP_ENV') == 'docker':
try:
import subprocess
subprocess.check_output(
['sudo', 'chown', '-R', 'ftuser:', str(directory.resolve())])
except Exception:
logger.warning(f"Could not chown {directory}")
def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
"""
Create userdata directory structure.
@@ -37,6 +52,7 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
"notebooks", "plot", "strategies", ]
folder = Path(directory)
chown_user_directory(folder)
if not folder.is_dir():
if create_dir:
folder.mkdir(parents=True)
@@ -72,6 +88,5 @@ def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
if not overwrite:
logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
continue
else:
logger.warning(f"File `{targetfile}` exists already, overwriting.")
logger.warning(f"File `{targetfile}` exists already, overwriting.")
shutil.copy(str(sourcedir / source), str(targetfile))

9
freqtrade/configuration/load_config.py
View File

@@ -38,6 +38,15 @@ def log_config_error_range(path: str, errmsg: str) -> str:
return ''
def load_file(path: Path) -> Dict[str, Any]:
try:
with path.open('r') as file:
config = rapidjson.load(file, parse_mode=CONFIG_PARSE_MODE)
except FileNotFoundError:
raise OperationalException(f'File file "{path}" not found!')
return config
def load_config_file(path: str) -> Dict[str, Any]:
"""
Loads a config file from the given path

7
freqtrade/configuration/timerange.py
View File

@@ -3,6 +3,7 @@ This module contains the argument manager class
"""
import logging
import re
from datetime import datetime
from typing import Optional
import arrow
@@ -43,7 +44,7 @@ class TimeRange:
self.startts = self.startts - seconds
def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
min_date: arrow.Arrow) -> None:
min_date: datetime) -> None:
"""
Adjust startts by <startup_candles> candles.
Applies only if no startup-candles have been available.
@@ -54,11 +55,11 @@ class TimeRange:
:return: None (Modifies the object in place)
"""
if (not self.starttype or (startup_candles
and min_date.int_timestamp >= self.startts)):
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.int_timestamp + timeframe_secs * startup_candles)
self.startts = int(min_date.timestamp() + timeframe_secs * startup_candles)
self.starttype = 'date'
@staticmethod

23
freqtrade/constants.py
View File

@@ -11,6 +11,7 @@ DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
TIMEOUT_UNITS = ['minutes', 'seconds']
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
UNLIMITED_STAKE_AMOUNT = 'unlimited'
@@ -26,7 +27,7 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'AgeFilter', 'PerformanceFilter', 'PrecisionFilter',
'PriceFilter', 'RangeStabilityFilter', 'ShuffleFilter',
'SpreadFilter']
'SpreadFilter', 'VolatilityFilter']
AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
DRY_RUN_WALLET = 1000
@@ -96,6 +97,7 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
'new_pairs_days': {'type': 'integer', 'default': 30},
'timeframe': {'type': 'string'},
'stake_currency': {'type': 'string'},
'stake_amount': {
@@ -136,7 +138,8 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'buy': {'type': 'number', 'minimum': 1},
'sell': {'type': 'number', 'minimum': 1}
'sell': {'type': 'number', 'minimum': 1},
'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
}
},
'bid_strategy': {
@@ -176,7 +179,7 @@ CONF_SCHEMA = {
'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
'use_sell_signal': {'type': 'boolean'},
'sell_profit_only': {'type': 'boolean'},
'sell_profit_offset': {'type': 'number', 'minimum': 0.0},
'sell_profit_offset': {'type': 'number'},
'ignore_roi_if_buy_signal': {'type': 'boolean'}
}
},
@@ -246,14 +249,24 @@ CONF_SCHEMA = {
'balance_dust_level': {'type': 'number', 'minimum': 0.0},
'notification_settings': {
'type': 'object',
'default': {},
'properties': {
'status': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}
'buy_fill': {'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'sell': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
}
}
},

56
freqtrade/data/btanalysis.py
View File

@@ -156,33 +156,35 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
data = data['strategy'][strategy]['trades']
df = pd.DataFrame(data)
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
if not df.empty:
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
else:
# old format - only with lists.
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
df['open_date'] = pd.to_datetime(df['open_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
# Create compatibility with new format
df['profit_abs'] = df['close_rate'] - df['open_rate']
if 'profit_ratio' not in df.columns:
df['profit_ratio'] = df['profit_percent']
df = df.sort_values("open_date").reset_index(drop=True)
if not df.empty:
df['open_date'] = pd.to_datetime(df['open_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
# Create compatibility with new format
df['profit_abs'] = df['close_rate'] - df['open_rate']
if not df.empty:
if 'profit_ratio' not in df.columns:
df['profit_ratio'] = df['profit_percent']
df = df.sort_values("open_date").reset_index(drop=True)
return df
@@ -337,7 +339,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
"""
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_date and profit_ratio)
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
: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.
@@ -349,8 +351,8 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
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_date'
)[['profit_ratio']].sum()
df.loc[:, col_name] = _trades_sum['profit_ratio'].cumsum()
)[['profit_abs']].sum()
df.loc[:, col_name] = _trades_sum['profit_abs'].cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
# FFill to get continuous

46
freqtrade/data/converter.py
View File

@@ -110,28 +110,62 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
df.reset_index(inplace=True)
len_before = len(dataframe)
len_after = len(df)
pct_missing = (len_after - len_before) / len_before if len_before > 0 else 0
if len_before != len_after:
logger.info(f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}")
message = (f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}"
f" - {round(pct_missing * 100, 2)}%")
if pct_missing > 0.01:
logger.info(message)
else:
# Don't be verbose if only a small amount is missing
logger.debug(message)
return df
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date') -> DataFrame:
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
startup_candles: int = 0) -> 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
:param df_date_col: Column in the dataframe to use as Date column
:param startup_candles: When not 0, is used instead the timerange start date
:return: trimmed dataframe
"""
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]
if startup_candles:
# Trim candles instead of timeframe in case of given startup_candle count
df = df.iloc[startup_candles:, :]
else:
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 trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
startup_candles: int) -> Dict[str, DataFrame]:
"""
Trim startup period from analyzed dataframes
:param preprocessed: Dict of pair: dataframe
:param timerange: timerange (use start and end date if available)
:param startup_candles: Startup-candles that should be removed
:return: Dict of trimmed dataframes
"""
processed: Dict[str, DataFrame] = {}
for pair, df in preprocessed.items():
trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles)
if not trimed_df.empty:
processed[pair] = trimed_df
else:
logger.warning(f'{pair} has no data left after adjusting for startup candles, '
f'skipping.')
return processed
def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
"""
TODO: This should get a dedicated test

176
freqtrade/data/dataprovider.py
View File

@@ -19,14 +19,25 @@ from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
NO_EXCHANGE_EXCEPTION = 'Exchange is not available to DataProvider.'
MAX_DATAFRAME_CANDLES = 1000
class DataProvider:
def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None:
def __init__(self, config: dict, exchange: Optional[Exchange], pairlists=None) -> None:
self._config = config
self._exchange = exchange
self._pairlists = pairlists
self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
self.__slice_index: Optional[int] = None
def _set_dataframe_max_index(self, limit_index: int):
"""
Limit analyzed dataframe to max specified index.
:param limit_index: dataframe index.
"""
self.__slice_index = limit_index
def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None:
"""
@@ -45,40 +56,6 @@ class DataProvider:
"""
self._pairlists = pairlists
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
"""
Refresh data, called with each cycle
"""
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
@property
def available_pairs(self) -> ListPairsWithTimeframes:
"""
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, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get candle (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 timeframe: 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, timeframe or self._config['timeframe']),
copy=copy)
else:
return DataFrame()
def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Get stored historical candle (OHLCV) data
@@ -111,47 +88,27 @@ class DataProvider:
def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
"""
Retrieve the analyzed dataframe. Returns the full dataframe in trade mode (live / dry),
and the last 1000 candles (up to the time evaluated at this moment) in all other modes.
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
:return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe
combination.
Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
"""
if (pair, timeframe) in self.__cached_pairs:
return self.__cached_pairs[(pair, timeframe)]
pair_key = (pair, timeframe)
if pair_key in self.__cached_pairs:
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
df, date = self.__cached_pairs[pair_key]
else:
df, date = self.__cached_pairs[pair_key]
if self.__slice_index is not None:
max_index = self.__slice_index
df = df.iloc[max(0, max_index - MAX_DATAFRAME_CANDLES):max_index]
return df, date
else:
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
return self._exchange.markets.get(pair)
def ticker(self, pair: str):
"""
Return last ticker data from exchange
:param pair: Pair to get the data for
:return: Ticker dict from exchange or empty dict if ticker is not available for the pair
"""
try:
return self._exchange.fetch_ticker(pair)
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
Fetch latest l2 orderbook data
Warning: Does a network request - so use with common sense.
:param pair: pair to get the data for
:param maximum: Maximum number of orderbook entries to query
:return: dict including bids/asks with a total of `maximum` entries.
"""
return self._exchange.fetch_l2_order_book(pair, maximum)
@property
def runmode(self) -> RunMode:
"""
@@ -170,6 +127,89 @@ class DataProvider:
"""
if self._pairlists:
return self._pairlists.whitelist
return self._pairlists.whitelist.copy()
else:
raise OperationalException("Dataprovider was not initialized with a pairlist provider.")
def clear_cache(self):
"""
Clear pair dataframe cache.
"""
self.__cached_pairs = {}
# Exchange functions
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
"""
Refresh data, called with each cycle
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
@property
def available_pairs(self) -> ListPairsWithTimeframes:
"""
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get candle (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 timeframe: 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._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, timeframe or self._config['timeframe']),
copy=copy)
else:
return DataFrame()
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return self._exchange.markets.get(pair)
def ticker(self, pair: str):
"""
Return last ticker data from exchange
:param pair: Pair to get the data for
:return: Ticker dict from exchange or empty dict if ticker is not available for the pair
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
try:
return self._exchange.fetch_ticker(pair)
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
Fetch latest l2 orderbook data
Warning: Does a network request - so use with common sense.
:param pair: pair to get the data for
:param maximum: Maximum number of orderbook entries to query
:return: dict including bids/asks with a total of `maximum` entries.
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return self._exchange.fetch_l2_order_book(pair, maximum)

2
freqtrade/data/history/hdf5datahandler.py
View File

@@ -89,7 +89,7 @@ class HDF5DataHandler(IDataHandler):
if timerange.starttype == 'date':
where.append(f"date >= Timestamp({timerange.startts * 1e9})")
if timerange.stoptype == 'date':
where.append(f"date < Timestamp({timerange.stopts * 1e9})")
where.append(f"date <= Timestamp({timerange.stopts * 1e9})")
pairdata = pd.read_hdf(filename, key=key, mode="r", where=where)

28
freqtrade/data/history/history_utils.py
View File

@@ -155,6 +155,7 @@ def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optiona
def _download_pair_history(datadir: Path,
exchange: Exchange,
pair: str, *,
new_pairs_days: int = 30,
timeframe: str = '5m',
timerange: Optional[TimeRange] = None,
data_handler: IDataHandler = None) -> bool:
@@ -193,7 +194,7 @@ def _download_pair_history(datadir: Path,
timeframe=timeframe,
since_ms=since_ms if since_ms else
int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000
days=-new_pairs_days).float_timestamp) * 1000
)
# TODO: Maybe move parsing to exchange class (?)
new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
@@ -223,7 +224,8 @@ def _download_pair_history(datadir: Path,
def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
datadir: Path, timerange: Optional[TimeRange] = None,
erase: bool = False, data_format: str = None) -> List[str]:
new_pairs_days: int = 30, erase: bool = False,
data_format: str = None) -> List[str]:
"""
Refresh stored ohlcv data for backtesting and hyperopt operations.
Used by freqtrade download-data subcommand.
@@ -246,12 +248,14 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
logger.info(f'Downloading pair {pair}, interval {timeframe}.')
_download_pair_history(datadir=datadir, exchange=exchange,
pair=pair, timeframe=str(timeframe),
new_pairs_days=new_pairs_days,
timerange=timerange, data_handler=data_handler)
return pairs_not_available
def _download_trades_history(exchange: Exchange,
pair: str, *,
new_pairs_days: int = 30,
timerange: Optional[TimeRange] = None,
data_handler: IDataHandler
) -> bool:
@@ -261,9 +265,13 @@ def _download_trades_history(exchange: Exchange,
"""
try:
since = timerange.startts * 1000 if \
(timerange and timerange.starttype == 'date') else int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000
until = None
if (timerange and timerange.starttype == 'date'):
since = timerange.startts * 1000
if timerange.stoptype == 'date':
until = timerange.stopts * 1000
else:
since = int(arrow.utcnow().shift(days=-new_pairs_days).float_timestamp) * 1000
trades = data_handler.trades_load(pair)
@@ -291,6 +299,7 @@ def _download_trades_history(exchange: Exchange,
# Default since_ms to 30 days if nothing is given
new_trades = exchange.get_historic_trades(pair=pair,
since=since,
until=until,
from_id=from_id,
)
trades.extend(new_trades[1])
@@ -311,8 +320,8 @@ def _download_trades_history(exchange: Exchange,
def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
timerange: TimeRange, erase: bool = False,
data_format: str = 'jsongz') -> List[str]:
timerange: TimeRange, new_pairs_days: int = 30,
erase: bool = False, data_format: str = 'jsongz') -> List[str]:
"""
Refresh stored trades data for backtesting and hyperopt operations.
Used by freqtrade download-data subcommand.
@@ -333,6 +342,7 @@ def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir:
logger.info(f'Downloading trades for pair {pair}.')
_download_trades_history(exchange=exchange,
pair=pair,
new_pairs_days=new_pairs_days,
timerange=timerange,
data_handler=data_handler)
return pairs_not_available
@@ -362,7 +372,7 @@ def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
logger.exception(f'Could not convert {pair} to OHLCV.')
def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
def get_timerange(data: Dict[str, DataFrame]) -> Tuple[datetime, datetime]:
"""
Get the maximum common timerange for the given backtest data.
@@ -370,7 +380,7 @@ def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
:return: tuple containing min_date, max_date
"""
timeranges = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
(frame['date'].min().to_pydatetime(), frame['date'].max().to_pydatetime())
for frame in data.values()
]
return (min(timeranges, key=operator.itemgetter(0))[0],

52
freqtrade/edge/edge_positioning.py
View File

@@ -1,6 +1,8 @@
# pragma pylint: disable=W0603
""" Edge positioning package """
import logging
from collections import defaultdict
from copy import deepcopy
from typing import Any, Dict, List, NamedTuple
import arrow
@@ -12,8 +14,10 @@ from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange.exchange import timeframe_to_seconds
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.strategy.interface import SellType
from freqtrade.state import RunMode
from freqtrade.strategy.interface import IStrategy, SellType
logger = logging.getLogger(__name__)
@@ -45,7 +49,7 @@ class Edge:
self.config = config
self.exchange = exchange
self.strategy = strategy
self.strategy: IStrategy = strategy
self.edge_config = self.config.get('edge', {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
@@ -81,12 +85,16 @@ class Edge:
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(symbol=expand_pairlist(
self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
try:
self.fee = self.exchange.get_fee(symbol=expand_pairlist(
self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
except IndexError:
self.fee = None
def calculate(self, pairs: List[str]) -> bool:
if self.fee is None and pairs:
self.fee = self.exchange.get_fee(pairs[0])
def calculate(self) -> bool:
pairs = expand_pairlist(self.config['exchange']['pair_whitelist'],
list(self.exchange.markets))
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (
@@ -98,14 +106,33 @@ class Edge:
logger.info('Using local backtesting data (using whitelist in given config) ...')
if self._refresh_pairs:
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
self.strategy.timeframe) * self.strategy.startup_candle_count)
refresh_data(
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
# Download informative pairs too
res = defaultdict(list)
for p, t in self.strategy.informative_pairs():
res[t].append(p)
for timeframe, inf_pairs in res.items():
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
timeframe) * self.strategy.startup_candle_count)
refresh_data(
datadir=self.config['datadir'],
pairs=inf_pairs,
exchange=self.exchange,
timeframe=timeframe,
timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
data = load_data(
datadir=self.config['datadir'],
@@ -121,8 +148,11 @@ class Edge:
self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...")
return False
# Fake run-mode to Edge
prior_rm = self.config['runmode']
self.config['runmode'] = RunMode.EDGE
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
self.config['runmode'] = prior_rm
# Print timeframe
min_date, max_date = get_timerange(preprocessed)
@@ -179,7 +209,7 @@ class Edge:
if pair in self._cached_pairs:
return self._cached_pairs[pair].stoploss
else:
logger.warning('tried to access stoploss of a non-existing pair, '
logger.warning(f'Tried to access stoploss of non-existing pair {pair}, '
'strategy stoploss is returned instead.')
return self.strategy.stoploss
@@ -210,7 +240,7 @@ class Edge:
return self._final_pairs
def accepted_pairs(self) -> list:
def accepted_pairs(self) -> List[Dict[str, Any]]:
"""
return a list of accepted pairs along with their winrate, expectancy and stoploss
"""

6
freqtrade/exchange/__init__.py
View File

@@ -8,10 +8,12 @@ from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bittrex import Bittrex
from freqtrade.exchange.bybit import Bybit
from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
get_exchange_bad_reason, is_exchange_bad,
is_exchange_known_ccxt, is_exchange_officially_supported,
market_is_active, timeframe_to_minutes, timeframe_to_msecs,
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds)
timeframe_to_seconds, validate_exchange,
validate_exchanges)
from freqtrade.exchange.ftx import Ftx
from freqtrade.exchange.hitbtc import Hitbtc
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.kucoin import Kucoin

2
freqtrade/exchange/binance.py
View File

@@ -52,7 +52,7 @@ class Binance(Exchange):
'In stoploss limit order, stop price should be more than limit price')
if self._config['dry_run']:
dry_order = self.dry_run_order(
dry_order = self.create_dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order

4
freqtrade/exchange/bittrex.py
View File

@@ -12,10 +12,6 @@ class Bittrex(Exchange):
"""
Bittrex 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.
"""
_ft_has: Dict = {

95
freqtrade/exchange/common.py
View File

@@ -18,78 +18,8 @@ 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.",
"phemex": "Does not provide history. ",
"poloniex": "Does not provide fetch_order endpoint to fetch both open and closed orders.",
**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 = {
@@ -98,6 +28,29 @@ MAP_EXCHANGE_CHILDCLASS = {
}
EXCHANGE_HAS_REQUIRED = [
# Required / private
'fetchOrder',
'cancelOrder',
'createOrder',
# 'createLimitOrder', 'createMarketOrder',
'fetchBalance',
# Public endpoints
'loadMarkets',
'fetchOHLCV',
]
EXCHANGE_HAS_OPTIONAL = [
# Private
'fetchMyTrades', # Trades for order - fee detection
# Public
'fetchOrderBook', 'fetchL2OrderBook', 'fetchTicker', # OR for pricing
'fetchTickers', # For volumepairlist?
'fetchTrades', # Downloading trades data
]
def calculate_backoff(retrycount, max_retries):
"""
Calculate backoff
@@ -140,7 +93,7 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
logger.warning('retrying %s() still for %s times', f.__name__, count)
count -= 1
kwargs.update({'count': count})
if isinstance(ex, DDosProtection) or isinstance(ex, RetryableOrderError):
if isinstance(ex, (DDosProtection, RetryableOrderError)):
# increasing backoff
backoff_delay = calculate_backoff(count + 1, retries)
logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")

142
freqtrade/exchange/exchange.py
View File

@@ -14,6 +14,7 @@ from typing import Any, Dict, List, Optional, Tuple
import arrow
import ccxt
import ccxt.async_support as ccxt_async
from cachetools import TTLCache
from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE,
decimal_to_precision)
from pandas import DataFrame
@@ -23,7 +24,8 @@ from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, RetryableOrderError,
TemporaryError)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES, retrier,
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED, retrier,
retrier_async)
from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@@ -57,11 +59,13 @@ class Exchange:
_ft_has_default: Dict = {
"stoploss_on_exchange": False,
"order_time_in_force": ["gtc"],
"ohlcv_params": {},
"ohlcv_candle_limit": 500,
"ohlcv_partial_candle": True,
"trades_pagination": "time", # Possible are "time" or "id"
"trades_pagination_arg": "since",
"l2_limit_range": None,
"l2_limit_range_required": True, # Allow Empty L2 limit (kucoin)
}
_ft_has: Dict = {}
@@ -82,6 +86,9 @@ class Exchange:
# Timestamp of last markets refresh
self._last_markets_refresh: int = 0
# Cache for 10 minutes ...
self._fetch_tickers_cache: TTLCache = TTLCache(maxsize=1, ttl=60 * 10)
# Holds candles
self._klines: Dict[Tuple[str, str], DataFrame] = {}
@@ -357,7 +364,6 @@ class Exchange:
invalid_pairs = []
for pair in extended_pairs:
# Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
# TODO: add a support for having coins in BTC/USDT format
if self.markets and pair not in self.markets:
raise OperationalException(
f'Pair {pair} is not available on {self.name}. '
@@ -460,7 +466,7 @@ class Exchange:
def amount_to_precision(self, pair: str, amount: float) -> float:
'''
Returns the amount to buy or sell to a precision the Exchange accepts
Reimplementation of ccxt internal methods - ensuring we can test the result is correct
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
'''
if self.markets[pair]['precision']['amount']:
@@ -474,7 +480,7 @@ class Exchange:
def price_to_precision(self, pair: str, price: float) -> float:
'''
Returns the price rounded up to the precision the Exchange accepts.
Partial Reimplementation of ccxt internal method decimal_to_precision(),
Partial Re-implementation of ccxt internal method decimal_to_precision(),
which does not support rounding up
TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
align with amount_to_precision().
@@ -533,7 +539,9 @@ class Exchange:
# reserve some percent defined in config (5% default) + stoploss
amount_reserve_percent = 1.0 + self._config.get('amount_reserve_percent',
DEFAULT_AMOUNT_RESERVE_PERCENT)
amount_reserve_percent += abs(stoploss)
amount_reserve_percent = (
amount_reserve_percent / (1 - abs(stoploss)) if abs(stoploss) != 1 else 1.5
)
# it should not be more than 50%
amount_reserve_percent = max(min(amount_reserve_percent, 1.5), 1)
@@ -542,8 +550,8 @@ class Exchange:
# See also #2575 at github.
return max(min_stake_amounts) * amount_reserve_percent
def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
rate: float, params: Dict = {}) -> Dict[str, Any]:
def create_dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
rate: float, params: Dict = {}) -> Dict[str, Any]:
order_id = f'dry_run_{side}_{datetime.now().timestamp()}'
_amount = self.amount_to_precision(pair, amount)
dry_order = {
@@ -617,7 +625,7 @@ class Exchange:
rate: float, time_in_force: str) -> Dict:
if self._config['dry_run']:
dry_order = self.dry_run_order(pair, ordertype, "buy", amount, rate)
dry_order = self.create_dry_run_order(pair, ordertype, "buy", amount, rate)
return dry_order
params = self._params.copy()
@@ -630,7 +638,7 @@ class Exchange:
rate: float, time_in_force: str = 'gtc') -> Dict:
if self._config['dry_run']:
dry_order = self.dry_run_order(pair, ordertype, "sell", amount, rate)
dry_order = self.create_dry_run_order(pair, ordertype, "sell", amount, rate)
return dry_order
params = self._params.copy()
@@ -659,23 +667,8 @@ class Exchange:
raise OperationalException(f"stoploss is not implemented for {self.name}.")
@retrier
def get_balance(self, currency: str) -> float:
if self._config['dry_run']:
return self._config['dry_run_wallet']
# ccxt exception is already handled by get_balances
balances = self.get_balances()
balance = balances.get(currency)
if balance is None:
raise TemporaryError(
f'Could not get {currency} balance due to malformed exchange response: {balances}')
return balance['free']
@retrier
def get_balances(self) -> dict:
if self._config['dry_run']:
return {}
try:
balances = self._api.fetch_balance()
@@ -695,9 +688,19 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def get_tickers(self) -> Dict:
def get_tickers(self, cached: bool = False) -> Dict:
"""
:param cached: Allow cached result
:return: fetch_tickers result
"""
if cached:
tickers = self._fetch_tickers_cache.get('fetch_tickers')
if tickers:
return tickers
try:
return self._api.fetch_tickers()
tickers = self._api.fetch_tickers()
self._fetch_tickers_cache['fetch_tickers'] = tickers
return tickers
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching tickers in batch. '
@@ -806,7 +809,7 @@ class Exchange:
# Gather coroutines to run
for pair, timeframe in set(pair_list):
if (not ((pair, timeframe) in self._klines)
if (((pair, timeframe) not in self._klines)
or self._now_is_time_to_refresh(pair, timeframe)):
input_coroutines.append(self._async_get_candle_history(pair, timeframe,
since_ms=since_ms))
@@ -860,10 +863,11 @@ class Exchange:
"Fetching pair %s, interval %s, since %s %s...",
pair, timeframe, since_ms, s
)
params = self._ft_has.get('ohlcv_params', {})
data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
since=since_ms,
limit=self.ohlcv_candle_limit(timeframe))
limit=self.ohlcv_candle_limit(timeframe),
params=params)
# Some exchanges sort OHLCV in ASC order and others in DESC.
# Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
@@ -958,7 +962,7 @@ class Exchange:
while True:
t = await self._async_fetch_trades(pair,
params={self._trades_pagination_arg: from_id})
if len(t):
if t:
# Skip last id since its the key for the next call
trades.extend(t[:-1])
if from_id == t[-1][1] or t[-1][0] > until:
@@ -990,7 +994,7 @@ class Exchange:
# DEFAULT_TRADES_COLUMNS: 1 -> id
while True:
t = await self._async_fetch_trades(pair, since=since)
if len(t):
if t:
since = t[-1][0]
trades.extend(t)
# Reached the end of the defined-download period
@@ -1116,6 +1120,27 @@ class Exchange:
return order
def cancel_stoploss_order_with_result(self, order_id: str, pair: str, amount: float) -> Dict:
"""
Cancel stoploss order returning a result.
Creates a fake result if cancel order returns a non-usable result
and fetch_order does not work (certain exchanges don't return cancelled orders)
:param order_id: stoploss-order-id to cancel
:param pair: Pair corresponding to order_id
:param amount: Amount to use for fake response
:return: Result from either cancel_order if usable, or fetch_order
"""
corder = self.cancel_stoploss_order(order_id, pair)
if self.is_cancel_order_result_suitable(corder):
return corder
try:
order = self.fetch_stoploss_order(order_id, pair)
except InvalidOrderException:
logger.warning(f"Could not fetch cancelled stoploss order {order_id}.")
order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
return order
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
@@ -1157,14 +1182,20 @@ class Exchange:
return self.fetch_order(order_id, pair)
@staticmethod
def get_next_limit_in_list(limit: int, limit_range: Optional[List[int]]):
def get_next_limit_in_list(limit: int, limit_range: Optional[List[int]],
range_required: bool = True):
"""
Get next greater value in the list.
Used by fetch_l2_order_book if the api only supports a limited range
"""
if not limit_range:
return limit
return min([x for x in limit_range if limit <= x] + [max(limit_range)])
result = min([x for x in limit_range if limit <= x] + [max(limit_range)])
if not range_required and limit > result:
# Range is not required - we can use None as parameter.
return None
return result
@retrier
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
@@ -1174,7 +1205,8 @@ class Exchange:
Returns a dict in the format
{'asks': [price, volume], 'bids': [price, volume]}
"""
limit1 = self.get_next_limit_in_list(limit, self._ft_has['l2_limit_range'])
limit1 = self.get_next_limit_in_list(limit, self._ft_has['l2_limit_range'],
self._ft_has['l2_limit_range_required'])
try:
return self._api.fetch_l2_order_book(pair, limit1)
@@ -1228,6 +1260,9 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
return order['id']
@retrier
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: str = 'maker') -> float:
@@ -1306,14 +1341,6 @@ class Exchange:
self.calculate_fee_rate(order))
def is_exchange_bad(exchange_name: str) -> bool:
return exchange_name in BAD_EXCHANGES
def get_exchange_bad_reason(exchange_name: str) -> str:
return BAD_EXCHANGES.get(exchange_name, "")
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
@@ -1334,7 +1361,36 @@ def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
"""
exchanges = ccxt_exchanges(ccxt_module)
return [x for x in exchanges if not is_exchange_bad(x)]
return [x for x in exchanges if validate_exchange(x)[0]]
def validate_exchange(exchange: str) -> Tuple[bool, str]:
ex_mod = getattr(ccxt, exchange.lower())()
if not ex_mod or not ex_mod.has:
return False, ''
missing = [k for k in EXCHANGE_HAS_REQUIRED if ex_mod.has.get(k) is not True]
if missing:
return False, f"missing: {', '.join(missing)}"
missing_opt = [k for k in EXCHANGE_HAS_OPTIONAL if not ex_mod.has.get(k)]
if exchange.lower() in BAD_EXCHANGES:
return False, BAD_EXCHANGES.get(exchange.lower(), '')
if missing_opt:
return True, f"missing opt: {', '.join(missing_opt)}"
return True, ''
def validate_exchanges(all_exchanges: bool) -> List[Tuple[str, bool, str]]:
"""
:return: List of tuples with exchangename, valid, reason.
"""
exchanges = ccxt_exchanges() if all_exchanges else available_exchanges()
exchanges_valid = [
(e, *validate_exchange(e)) for e in exchanges
]
return exchanges_valid
def timeframe_to_seconds(timeframe: str) -> int:

11
freqtrade/exchange/ftx.py
View File

@@ -8,6 +8,7 @@ from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, Invali
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT, retrier
from freqtrade.misc import safe_value_fallback2
logger = logging.getLogger(__name__)
@@ -53,7 +54,7 @@ class Ftx(Exchange):
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.dry_run_order(
dry_order = self.create_dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order
@@ -63,10 +64,11 @@ class Ftx(Exchange):
# set orderPrice to place limit order, otherwise it's a market order
params['orderPrice'] = limit_rate
params['stopPrice'] = stop_price
amount = self.amount_to_precision(pair, amount)
order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
amount=amount, price=stop_price, params=params)
amount=amount, params=params)
logger.info('stoploss order added for %s. '
'stop price: %s.', pair, stop_price)
return order
@@ -134,3 +136,8 @@ class Ftx(Exchange):
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
if order['type'] == 'stop':
return safe_value_fallback2(order['info'], order, 'orderId', 'id')
return order['id']

24
freqtrade/exchange/hitbtc.py Normal file
View File

@@ -0,0 +1,24 @@
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Hitbtc(Exchange):
"""
Hitbtc 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 Hitbtc,
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ohlcv_params": {"sort": "DESC"}
}

4
freqtrade/exchange/kraken.py
View File

@@ -53,6 +53,8 @@ class Kraken(Exchange):
# x["side"], x["amount"],
) for x in orders]
for bal in balances:
if not isinstance(balances[bal], dict):
continue
balances[bal]['used'] = sum(order[1] for order in order_list if order[0] == bal)
balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used']
@@ -92,7 +94,7 @@ class Kraken(Exchange):
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.dry_run_order(
dry_order = self.create_dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order

24
freqtrade/exchange/kucoin.py Normal file
View File

@@ -0,0 +1,24 @@
""" Kucoin exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Kucoin(Exchange):
"""
Kucoin 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.
"""
_ft_has: Dict = {
"l2_limit_range": [20, 100],
"l2_limit_range_required": False,
}

141
freqtrade/freqtradebot.py
View File

@@ -28,7 +28,7 @@ from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.state import State
from freqtrade.strategy.interface import IStrategy, SellType
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
@@ -113,7 +113,7 @@ class FreqtradeBot(LoggingMixin):
via RPC about changes in the bot status.
"""
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'type': RPCMessageType.STATUS,
'status': msg
})
@@ -187,7 +187,7 @@ class FreqtradeBot(LoggingMixin):
if self.get_free_open_trades():
self.enter_positions()
Trade.session.flush()
Trade.query.session.flush()
def process_stopped(self) -> None:
"""
@@ -205,7 +205,7 @@ class FreqtradeBot(LoggingMixin):
if len(open_trades) != 0:
msg = {
'type': RPCMessageType.WARNING_NOTIFICATION,
'type': RPCMessageType.WARNING,
'status': f"{len(open_trades)} open trades active.\n\n"
f"Handle these trades manually on {self.exchange.name}, "
f"or '/start' the bot again and use '/stopbuy' "
@@ -225,7 +225,7 @@ class FreqtradeBot(LoggingMixin):
# Calculating Edge positioning
if self.edge:
self.edge.calculate()
self.edge.calculate(_whitelist)
_whitelist = self.edge.adjust(_whitelist)
if trades:
@@ -267,7 +267,7 @@ class FreqtradeBot(LoggingMixin):
def update_closed_trades_without_assigned_fees(self):
"""
Update closed trades without close fees assigned.
Only acts when Orders are in the database, otherwise the last orderid is unknown.
Only acts when Orders are in the database, otherwise the last order-id is unknown.
"""
if self.config['dry_run']:
# Updating open orders in dry-run does not make sense and will fail.
@@ -378,7 +378,7 @@ class FreqtradeBot(LoggingMixin):
if lock:
self.log_once(f"Global pairlock active until "
f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}. "
"Not creating new trades.", logger.info)
f"Not creating new trades, reason: {lock.reason}.", logger.info)
else:
self.log_once("Global pairlock active. Not creating new trades.", logger.info)
return trades_created
@@ -410,9 +410,7 @@ class FreqtradeBot(LoggingMixin):
bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in bid_strategy and bid_strategy.get('use_order_book', False):
logger.info(
f"Getting price from order book {bid_strategy['price_side'].capitalize()} side."
)
order_book_top = bid_strategy.get('order_book_top', 1)
order_book = self.exchange.fetch_l2_order_book(pair, order_book_top)
logger.debug('order_book %s', order_book)
@@ -425,7 +423,8 @@ class FreqtradeBot(LoggingMixin):
f"Orderbook: {order_book}"
)
raise PricingError from e
logger.info(f'...top {order_book_top} order book buy rate {rate_from_l2:.8f}')
logger.info(f"Buy price from orderbook {bid_strategy['price_side'].capitalize()} side "
f"- top {order_book_top} order book buy rate {rate_from_l2:.8f}")
used_rate = rate_from_l2
else:
logger.info(f"Using Last {bid_strategy['price_side'].capitalize()} / Last Price")
@@ -457,7 +456,8 @@ class FreqtradeBot(LoggingMixin):
lock = PairLocks.get_pair_longest_lock(pair, nowtime)
if lock:
self.log_once(f"Pair {pair} is still locked until "
f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}.",
f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)} "
f"due to {lock.reason}.",
logger.info)
else:
self.log_once(f"Pair {pair} is still locked.", logger.info)
@@ -473,25 +473,22 @@ class FreqtradeBot(LoggingMixin):
(buy, sell) = self.strategy.get_signal(pair, self.strategy.timeframe, analyzed_df)
if buy and not sell:
stake_amount = self.wallets.get_trade_stake_amount(pair, self.get_free_open_trades(),
self.edge)
stake_amount = self.wallets.get_trade_stake_amount(pair, self.edge)
if not stake_amount:
logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
return False
logger.info(f"Buy signal found: about create a new trade with stake_amount: "
logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
f"{stake_amount} ...")
bid_check_dom = self.config.get('bid_strategy', {}).get('check_depth_of_market', {})
if ((bid_check_dom.get('enabled', False)) and
(bid_check_dom.get('bids_to_ask_delta', 0) > 0)):
if self._check_depth_of_market_buy(pair, bid_check_dom):
logger.info(f'Executing Buy for {pair}.')
return self.execute_buy(pair, stake_amount)
else:
return False
logger.info(f'Executing Buy for {pair}')
return self.execute_buy(pair, stake_amount)
else:
return False
@@ -555,7 +552,7 @@ class FreqtradeBot(LoggingMixin):
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force):
time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of buying {pair}")
return False
amount = self.exchange.amount_to_precision(pair, amount)
@@ -621,8 +618,8 @@ class FreqtradeBot(LoggingMixin):
if order_status == 'closed':
self.update_trade_state(trade, order_id, order)
Trade.session.add(trade)
Trade.session.flush()
Trade.query.session.add(trade)
Trade.query.session.flush()
# Updating wallets
self.wallets.update()
@@ -633,11 +630,11 @@ class FreqtradeBot(LoggingMixin):
def _notify_buy(self, trade: Trade, order_type: str) -> None:
"""
Sends rpc notification when a buy occured.
Sends rpc notification when a buy occurred.
"""
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_NOTIFICATION,
'type': RPCMessageType.BUY,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'limit': trade.open_rate,
@@ -655,13 +652,13 @@ class FreqtradeBot(LoggingMixin):
def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a buy cancel occured.
Sends rpc notification when a buy cancel occurred.
"""
current_rate = self.get_buy_rate(trade.pair, False)
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
'type': RPCMessageType.BUY_CANCEL,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'limit': trade.open_rate,
@@ -678,6 +675,21 @@ class FreqtradeBot(LoggingMixin):
# Send the message
self.rpc.send_msg(msg)
def _notify_buy_fill(self, trade: Trade) -> None:
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_FILL,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'open_rate': trade.open_rate,
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'amount': trade.amount,
'open_date': trade.open_date,
}
self.rpc.send_msg(msg)
#
# SELL / exit positions / close trades logic and methods
#
@@ -701,7 +713,7 @@ class FreqtradeBot(LoggingMixin):
except DependencyException as exception:
logger.warning('Unable to sell trade %s: %s', trade.pair, exception)
# Updating wallets if any trade occured
# Updating wallets if any trade occurred
if trades_closed:
self.wallets.update()
@@ -838,7 +850,8 @@ class FreqtradeBot(LoggingMixin):
trade.stoploss_order_id = None
logger.error(f'Unable to place a stoploss order on exchange. {e}')
logger.warning('Selling the trade forcefully')
self.execute_sell(trade, trade.stop_loss, sell_reason=SellType.EMERGENCY_SELL)
self.execute_sell(trade, trade.stop_loss, sell_reason=SellCheckTuple(
sell_type=SellType.EMERGENCY_SELL))
except ExchangeError:
trade.stoploss_order_id = None
@@ -919,14 +932,15 @@ class FreqtradeBot(LoggingMixin):
:return: None
"""
if self.exchange.stoploss_adjust(trade.stop_loss, order):
# we check if the update is neccesary
# we check if the update is necessary
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
# cancelling the current stoploss on exchange first
logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
f"(orderid:{order['id']}) in order to add another one ...")
try:
co = self.exchange.cancel_stoploss_order(order['id'], trade.pair)
co = self.exchange.cancel_stoploss_order_with_result(order['id'], trade.pair,
trade.amount)
trade.update_order(co)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {order['id']} "
@@ -949,7 +963,7 @@ class FreqtradeBot(LoggingMixin):
if should_sell.sell_flag:
logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
self.execute_sell(trade, sell_rate, should_sell.sell_type)
self.execute_sell(trade, sell_rate, should_sell)
return True
return False
@@ -960,15 +974,16 @@ class FreqtradeBot(LoggingMixin):
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
timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
timeout_kwargs = {timeout_unit: -timeout}
timeout_threshold = arrow.utcnow().shift(**timeout_kwargs).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
Check if any orders are timed out and cancel if necessary
:param timeoutvalue: Number of minutes until order is considered timed out
:return: None
"""
@@ -1030,6 +1045,16 @@ class FreqtradeBot(LoggingMixin):
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in ('cancelled', 'canceled', 'closed'):
filled_val = order.get('filled', 0.0) or 0.0
filled_stake = filled_val * trade.open_rate
minstake = self.exchange.get_min_pair_stake_amount(
trade.pair, trade.open_rate, self.strategy.stoploss)
if filled_val > 0 and filled_stake < minstake:
logger.warning(
f"Order {trade.open_order_id} for {trade.pair} not cancelled, "
f"as the filled amount of {filled_val} would result in an unsellable trade.")
return False
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
# Avoid race condition where the order could not be cancelled coz its already filled.
@@ -1138,16 +1163,16 @@ class FreqtradeBot(LoggingMixin):
raise DependencyException(
f"Not enough amount to sell. Trade-amount: {amount}, Wallet: {wallet_amount}")
def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> bool:
def execute_sell(self, trade: Trade, limit: float, sell_reason: SellCheckTuple) -> bool:
"""
Executes a limit sell for the given trade and limit
:param trade: Trade instance
:param limit: limit rate for the sell order
:param sellreason: Reason the sell was triggered
:param sell_reason: Reason the sell was triggered
:return: True if it succeeds (supported) False (not supported)
"""
sell_type = 'sell'
if sell_reason in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
if sell_reason.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
sell_type = 'stoploss'
# if stoploss is on exchange and we are on dry_run mode,
@@ -1159,15 +1184,17 @@ class FreqtradeBot(LoggingMixin):
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
try:
self.exchange.cancel_stoploss_order(trade.stoploss_order_id, trade.pair)
co = self.exchange.cancel_stoploss_order_with_result(trade.stoploss_order_id,
trade.pair, trade.amount)
trade.update_order(co)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
order_type = self.strategy.order_types[sell_type]
if sell_reason == SellType.EMERGENCY_SELL:
if sell_reason.sell_type == SellType.EMERGENCY_SELL:
# Emergency sells (default to market!)
order_type = self.strategy.order_types.get("emergencysell", "market")
if sell_reason == SellType.FORCE_SELL:
if sell_reason.sell_type == SellType.FORCE_SELL:
# Force sells (default to the sell_type defined in the strategy,
# but we allow this value to be changed)
order_type = self.strategy.order_types.get("forcesell", order_type)
@@ -1177,8 +1204,8 @@ class FreqtradeBot(LoggingMixin):
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit,
time_in_force=time_in_force,
sell_reason=sell_reason.value):
time_in_force=time_in_force, sell_reason=sell_reason.sell_reason,
current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of selling {trade.pair}")
return False
@@ -1201,13 +1228,13 @@ class FreqtradeBot(LoggingMixin):
trade.open_order_id = order['id']
trade.sell_order_status = ''
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
trade.sell_reason = sell_reason.sell_reason
# In case of market sell orders the order can be closed immediately
if order.get('status', 'unknown') == 'closed':
self.update_trade_state(trade, trade.open_order_id, order)
Trade.session.flush()
Trade.query.session.flush()
# Lock pair for one candle to prevent immediate rebuys
# Lock pair for one candle to prevent immediate re-buys
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
@@ -1215,19 +1242,20 @@ class FreqtradeBot(LoggingMixin):
return True
def _notify_sell(self, trade: Trade, order_type: str) -> None:
def _notify_sell(self, trade: Trade, order_type: str, fill: bool = False) -> None:
"""
Sends rpc notification when a sell occured.
Sends rpc notification when a sell occurred.
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
# Use cached rates here - it was updated seconds ago.
current_rate = self.get_sell_rate(trade.pair, False)
current_rate = self.get_sell_rate(trade.pair, False) if not fill else None
profit_ratio = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'type': (RPCMessageType.SELL_FILL if fill
else RPCMessageType.SELL),
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
@@ -1236,6 +1264,7 @@ class FreqtradeBot(LoggingMixin):
'order_type': order_type,
'amount': trade.amount,
'open_rate': trade.open_rate,
'close_rate': trade.close_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_ratio': profit_ratio,
@@ -1256,7 +1285,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a sell cancel occured.
Sends rpc notification when a sell cancel occurred.
"""
if trade.sell_order_status == reason:
return
@@ -1270,7 +1299,7 @@ class FreqtradeBot(LoggingMixin):
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_CANCEL_NOTIFICATION,
'type': RPCMessageType.SELL_CANCEL,
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
@@ -1309,7 +1338,7 @@ class FreqtradeBot(LoggingMixin):
Handles closing both buy and sell orders.
:param trade: Trade object of the trade we're analyzing
:param order_id: Order-id of the order we're analyzing
:param action_order: Already aquired order object
:param action_order: Already acquired order object
:return: True if order has been cancelled without being filled partially, False otherwise
"""
if not order_id:
@@ -1347,9 +1376,15 @@ class FreqtradeBot(LoggingMixin):
# Updating wallets when order is closed
if not trade.is_open:
if not stoploss_order and not trade.open_order_id:
self._notify_sell(trade, '', True)
self.protections.stop_per_pair(trade.pair)
self.protections.global_stop()
self.wallets.update()
elif not trade.open_order_id:
# Buy fill
self._notify_buy_fill(trade)
return False
def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
@@ -1373,7 +1408,7 @@ class FreqtradeBot(LoggingMixin):
def get_real_amount(self, trade: Trade, order: Dict) -> float:
"""
Detect and update trade fee.
Calls trade.update_fee() uppon correct detection.
Calls trade.update_fee() upon correct detection.
Returns modified amount if the fee was taken from the destination currency.
Necessary for exchanges which charge fees in base currency (e.g. binance)
:return: identical (or new) amount for the trade
@@ -1406,8 +1441,8 @@ class FreqtradeBot(LoggingMixin):
"""
fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
"""
trades = self.exchange.get_trades_for_order(order['id'], trade.pair,
trade.open_date)
trades = self.exchange.get_trades_for_order(self.exchange.get_order_id_conditional(order),
trade.pair, trade.open_date)
if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)

15
freqtrade/misc.py
View File

@@ -6,7 +6,7 @@ import logging
import re
from datetime import datetime
from pathlib import Path
from typing import Any
from typing import Any, Iterator, List
from typing.io import IO
import rapidjson
@@ -81,7 +81,7 @@ def json_load(datafile: IO) -> Any:
"""
load data with rapidjson
Use this to have a consistent experience,
sete number_mode to "NM_NATIVE" for greatest speed
set number_mode to "NM_NATIVE" for greatest speed
"""
return rapidjson.load(datafile, number_mode=rapidjson.NM_NATIVE)
@@ -202,3 +202,14 @@ def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
return render_template(templatefile, arguments)
except TemplateNotFound:
return render_template(templatefallbackfile, arguments)
def chunks(lst: List[Any], n: int) -> Iterator[List[Any]]:
"""
Split lst into chunks of the size n.
:param lst: list to split into chunks
:param n: number of max elements per chunk
:return: None
"""
for chunk in range(0, len(lst), n):
yield (lst[chunk:chunk + n])

135
freqtrade/optimize/backtesting.py
View File

@@ -15,7 +15,7 @@ from freqtrade.configuration import TimeRange, remove_credentials, validate_conf
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@@ -63,9 +63,7 @@ class Backtesting:
self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
self.dataprovider = DataProvider(self.config, None)
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
@@ -96,7 +94,7 @@ class Backtesting:
"PrecisionFilter not allowed for backtesting multiple strategies."
)
dataprovider.add_pairlisthandler(self.pairlists)
self.dataprovider.add_pairlisthandler(self.pairlists)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
@@ -112,15 +110,11 @@ class Backtesting:
PairLocks.timeframe = self.config['timeframe']
PairLocks.use_db = False
PairLocks.reset_locks()
if self.config.get('enable_protections', False):
self.protections = ProtectionManager(self.config)
self.wallets = Wallets(self.config, self.exchange, log=False)
# 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])
def __del__(self):
LoggingMixin.show_output = True
@@ -132,10 +126,17 @@ class Backtesting:
Load strategy into backtesting
"""
self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
if self.config.get('enable_protections', False):
conf = self.config
if hasattr(strategy, 'protections'):
conf = deepcopy(conf)
conf['protections'] = strategy.protections
self.protections = ProtectionManager(conf)
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
"""
@@ -159,7 +160,7 @@ class Backtesting:
logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
f'({(max_date - min_date).days} days).')
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
@@ -176,6 +177,8 @@ class Backtesting:
Trade.use_db = False
PairLocks.reset_locks()
Trade.reset_trades()
self.rejected_trades = 0
self.dataprovider.clear_cache()
def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
"""
@@ -189,8 +192,9 @@ class Backtesting:
data: Dict = {}
# Create dict with data
for pair, pair_data in processed.items():
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
if not pair_data.empty:
pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
@@ -214,6 +218,12 @@ class Backtesting:
"""
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
if trade.stop_loss > sell_row[HIGH_IDX]:
# our stoploss was already higher than candle high,
# possibly due to a cancelled trade exit.
# sell at open price.
return sell_row[OPEN_IDX]
# Set close_rate to stoploss
return trade.stop_loss
elif sell.sell_type == (SellType.ROI):
@@ -239,7 +249,7 @@ class Backtesting:
# Use the maximum between close_rate and low as we
# cannot sell outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return max(close_rate, sell_row[LOW_IDX])
return min(max(close_rate, sell_row[LOW_IDX]), sell_row[HIGH_IDX])
else:
# This should not be reached...
@@ -250,12 +260,13 @@ class Backtesting:
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX], sell_row[BUY_IDX], sell_row[SELL_IDX],
sell_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_row[DATE_IDX]
trade.sell_reason = sell.sell_type.value
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = sell.sell_reason
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
@@ -265,7 +276,8 @@ class Backtesting:
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_type.value):
sell_reason=sell.sell_reason,
current_time=sell_row[DATE_IDX].to_pydatetime()):
return None
trade.close(closerate, show_msg=False)
@@ -273,11 +285,9 @@ class Backtesting:
return None
def _enter_trade(self, pair: str, row: List, max_open_trades: int,
open_trade_count: int) -> Optional[LocalTrade]:
def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
try:
stake_amount = self.wallets.get_trade_stake_amount(
pair, max_open_trades - open_trade_count, None)
stake_amount = self.wallets.get_trade_stake_amount(pair, None)
except DependencyException:
return None
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05)
@@ -287,7 +297,7 @@ class Backtesting:
# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
time_in_force=time_in_force):
time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
return None
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
@@ -295,7 +305,7 @@ class Backtesting:
trade = LocalTrade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX],
open_date=row[DATE_IDX].to_pydatetime(),
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
@@ -317,7 +327,7 @@ class Backtesting:
for trade in open_trades[pair]:
sell_row = data[pair][-1]
trade.close_date = sell_row[DATE_IDX]
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = SellType.FORCE_SELL.value
trade.close(sell_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
@@ -327,10 +337,18 @@ class Backtesting:
trades.append(trade1)
return trades
def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
if max_open_trades <= 0 or open_trade_count < max_open_trades:
return True
# Rejected trade
self.rejected_trades += 1
return False
def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False,
enable_protections: bool = False) -> DataFrame:
enable_protections: bool = False) -> Dict[str, Any]:
"""
Implement backtesting functionality
@@ -349,12 +367,16 @@ class Backtesting:
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Update dataprovider cache
for pair, dataframe in processed.items():
self.dataprovider._set_cached_df(pair, self.timeframe, dataframe)
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = {}
indexes: Dict = defaultdict(int)
tmp = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
@@ -365,11 +387,9 @@ class Backtesting:
open_trade_count_start = open_trade_count
for i, pair in enumerate(data):
if pair not in indexes:
indexes[pair] = 0
row_index = indexes[pair]
try:
row = data[pair][indexes[pair]]
row = data[pair][row_index]
except IndexError:
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
@@ -378,17 +398,23 @@ class Backtesting:
# Waits until the time-counter reaches the start of the data for this pair.
if row[DATE_IDX] > tmp:
continue
indexes[pair] += 1
row_index += 1
self.dataprovider._set_dataframe_max_index(row_index)
indexes[pair] = row_index
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
if ((position_stacking or len(open_trades[pair]) == 0)
and (max_open_trades <= 0 or open_trade_count_start < max_open_trades)
and tmp != end_date
and row[BUY_IDX] == 1 and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
trade = self._enter_trade(pair, row, max_open_trades, open_trade_count_start)
if (
(position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and tmp != end_date
and row[BUY_IDX] == 1
and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
):
trade = self._enter_trade(pair, row)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
@@ -420,7 +446,14 @@ class Backtesting:
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
return trade_list_to_dataframe(trades)
results = trade_list_to_dataframe(trades)
return {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'rejected_signals': self.rejected_trades,
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@@ -442,31 +475,32 @@ class Backtesting:
preprocessed = self.strategy.ohlcvdata_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 = history.get_timerange(preprocessed)
preprocessed = trim_dataframes(preprocessed, timerange, self.required_startup)
if not preprocessed:
raise OperationalException(
"No data left after adjusting for startup candles.")
min_date, max_date = history.get_timerange(preprocessed)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
f'({(max_date - min_date).days} days).')
# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
start_date=min_date.datetime,
end_date=max_date.datetime,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
self.all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
}
})
self.all_results[self.strategy.get_strategy_name()] = results
return min_date, max_date
def start(self) -> None:
@@ -477,6 +511,7 @@ class Backtesting:
data: Dict[str, Any] = {}
data, timerange = self.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
for strat in self.strategylist:
min_date, max_date = self.backtest_one_strategy(strat, data, timerange)

2
freqtrade/optimize/edge_cli.py
View File

@@ -44,7 +44,7 @@ class EdgeCli:
'timerange') is None else str(self.config.get('timerange')))
def start(self) -> None:
result = self.edge.calculate()
result = self.edge.calculate(self.config['exchange']['pair_whitelist'])
if result:
print('') # blank line for readability
print(generate_edge_table(self.edge._cached_pairs))

275
freqtrade/optimize/hyperopt.py
View File

@@ -4,33 +4,33 @@
This module contains the hyperopt logic
"""
import locale
import logging
import random
import warnings
from datetime import datetime
from datetime import datetime, timezone
from math import ceil
from operator import itemgetter
from pathlib import Path
from typing import Any, Dict, List, Optional
import progressbar
import rapidjson
from colorama import Fore, Style
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 freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.misc import file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_tools import HyperoptTools
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
from freqtrade.strategy import IStrategy
# Suppress scikit-learn FutureWarnings from skopt
@@ -61,22 +61,33 @@ class Hyperopt:
hyperopt = Hyperopt(config)
hyperopt.start()
"""
custom_hyperopt: IHyperOpt
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config
self.backtesting = Backtesting(self.config)
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.custom_hyperopt.__class__.strategy = self.backtesting.strategy
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
else:
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
strategy = str(self.config['strategy'])
self.results_file = (self.config['user_data_dir'] /
'hyperopt_results' /
f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
f'strategy_{strategy}_{time_now}.fthypt')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
@@ -86,9 +97,7 @@ class Hyperopt:
self.clean_hyperopt()
self.num_epochs_saved = 0
# Previous evaluations
self.epochs: List = []
self.current_best_epoch: Optional[Dict[str, Any]] = None
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
@@ -109,7 +118,7 @@ class Hyperopt:
self.max_open_trades = 0
self.position_stacking = self.config.get('position_stacking', False)
if self.has_space('sell'):
if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_sell_signal is enabled
if 'ask_strategy' not in self.config:
self.config['ask_strategy'] = {}
@@ -135,9 +144,7 @@ class Hyperopt:
logger.info(f"Removing `{p}`.")
p.unlink()
def _get_params_dict(self, raw_params: List[Any]) -> Dict:
dimensions: List[Dimension] = self.dimensions
def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
# Ensure the number of dimensions match
# the number of parameters in the list.
@@ -148,21 +155,24 @@ class Hyperopt:
# and the values are taken from the list of parameters.
return {d.name: v for d, v in zip(dimensions, raw_params)}
def _save_results(self) -> None:
def _save_result(self, epoch: Dict) -> None:
"""
Save hyperopt results to file
Store one line per epoch.
While not a valid json object - this allows appending easily.
:param epoch: result dictionary for this epoch.
"""
num_epochs = len(self.epochs)
if num_epochs > self.num_epochs_saved:
logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
dump(self.epochs, self.results_file)
self.num_epochs_saved = num_epochs
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
# Store hyperopt filename
latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
with self.results_file.open('a') as f:
rapidjson.dump(epoch, f, default=str, number_mode=rapidjson.NM_NATIVE)
f.write("\n")
self.num_epochs_saved += 1
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
# Store hyperopt filename
latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
def _get_params_details(self, params: Dict) -> Dict:
"""
@@ -170,18 +180,16 @@ class Hyperopt:
"""
result: Dict = {}
if self.has_space('buy'):
result['buy'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('buy')}
if self.has_space('sell'):
result['sell'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('sell')}
if self.has_space('roi'):
result['roi'] = self.custom_hyperopt.generate_roi_table(params)
if self.has_space('stoploss'):
result['stoploss'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('stoploss')}
if self.has_space('trailing'):
if HyperoptTools.has_space(self.config, 'buy'):
result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
if HyperoptTools.has_space(self.config, 'sell'):
result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
if HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = {str(k): v for k, v in
self.custom_hyperopt.generate_roi_table(params).items()}
if HyperoptTools.has_space(self.config, 'stoploss'):
result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
return result
@@ -203,71 +211,58 @@ class Hyperopt:
)
self.hyperopt_table_header = 2
def has_space(self, space: str) -> bool:
def init_spaces(self):
"""
Tell if the space value is contained in the configuration
Assign the dimensions in the hyperoptimization space.
"""
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
return any(s in self.config['spaces'] for s in [space, 'all'])
else:
return any(s in self.config['spaces'] for s in [space, 'all', 'default'])
def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
"""
Return the dimensions in the hyperoptimization space.
:param space: Defines hyperspace to return dimensions for.
If None, then the self.has_space() will be used to return dimensions
for all hyperspaces used.
"""
spaces: List[Dimension] = []
if space == 'buy' or (space is None and self.has_space('buy')):
if HyperoptTools.has_space(self.config, 'buy'):
logger.debug("Hyperopt has 'buy' space")
spaces += self.custom_hyperopt.indicator_space()
self.buy_space = self.custom_hyperopt.indicator_space()
if space == 'sell' or (space is None and self.has_space('sell')):
if HyperoptTools.has_space(self.config, 'sell'):
logger.debug("Hyperopt has 'sell' space")
spaces += self.custom_hyperopt.sell_indicator_space()
self.sell_space = self.custom_hyperopt.sell_indicator_space()
if space == 'roi' or (space is None and self.has_space('roi')):
if HyperoptTools.has_space(self.config, 'roi'):
logger.debug("Hyperopt has 'roi' space")
spaces += self.custom_hyperopt.roi_space()
self.roi_space = self.custom_hyperopt.roi_space()
if space == 'stoploss' or (space is None and self.has_space('stoploss')):
if HyperoptTools.has_space(self.config, 'stoploss'):
logger.debug("Hyperopt has 'stoploss' space")
spaces += self.custom_hyperopt.stoploss_space()
self.stoploss_space = self.custom_hyperopt.stoploss_space()
if space == 'trailing' or (space is None and self.has_space('trailing')):
if HyperoptTools.has_space(self.config, 'trailing'):
logger.debug("Hyperopt has 'trailing' space")
spaces += self.custom_hyperopt.trailing_space()
return spaces
self.trailing_space = self.custom_hyperopt.trailing_space()
self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
self.stoploss_space + self.trailing_space)
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
"""
Used Optimize function. Called once per epoch to optimize whatever is configured.
Keep this function as optimized as possible!
"""
params_dict = self._get_params_dict(raw_params)
params_details = self._get_params_details(params_dict)
backtest_start_time = datetime.now(timezone.utc)
params_dict = self._get_params_dict(self.dimensions, raw_params)
if self.has_space('roi'):
# Apply parameters
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict))
if self.has_space('buy'):
if HyperoptTools.has_space(self.config, 'buy'):
self.backtesting.strategy.advise_buy = ( # type: ignore
self.custom_hyperopt.buy_strategy_generator(params_dict))
if self.has_space('sell'):
if HyperoptTools.has_space(self.config, 'sell'):
self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.sell_strategy_generator(params_dict))
if self.has_space('stoploss'):
if HyperoptTools.has_space(self.config, 'stoploss'):
self.backtesting.strategy.stoploss = params_dict['stoploss']
if self.has_space('trailing'):
if HyperoptTools.has_space(self.config, 'trailing'):
d = self.custom_hyperopt.generate_trailing_params(params_dict)
self.backtesting.strategy.trailing_stop = d['trailing_stop']
self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
@@ -276,30 +271,42 @@ class Hyperopt:
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
processed = load(self.data_pickle_file)
min_date, max_date = get_timerange(processed)
backtesting_results = self.backtesting.backtest(
with self.data_pickle_file.open('rb') as f:
processed = load(f, mmap_mode='r')
bt_results = self.backtesting.backtest(
processed=processed,
start_date=min_date.datetime,
end_date=max_date.datetime,
start_date=self.min_date,
end_date=self.max_date,
max_open_trades=self.max_open_trades,
position_stacking=self.position_stacking,
enable_protections=self.config.get('enable_protections', False),
)
return self._get_results_dict(backtesting_results, min_date, max_date,
params_dict, params_details,
backtest_end_time = datetime.now(timezone.utc)
bt_results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
return self._get_results_dict(bt_results, self.min_date, self.max_date,
params_dict,
processed=processed)
def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict, params_details, processed: Dict[str, DataFrame]):
results_metrics = self._calculate_results_metrics(backtesting_results)
results_explanation = self._format_results_explanation_string(results_metrics)
params_dict, processed: Dict[str, DataFrame]
) -> Dict[str, Any]:
params_details = self._get_params_details(params_dict)
trade_count = results_metrics['trade_count']
total_profit = results_metrics['total_profit']
strat_stats = generate_strategy_stats(
processed, self.backtesting.strategy.get_strategy_name(),
backtesting_results, min_date, max_date, market_change=0
)
results_explanation = HyperoptTools.format_results_explanation_string(
strat_stats, self.config['stake_currency'])
not_optimized = self.backtesting.strategy.get_params_dict()
trade_count = strat_stats['total_trades']
total_profit = strat_stats['profit_total']
# If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value)
@@ -307,50 +314,20 @@ class Hyperopt:
# path. We do not want to optimize 'hodl' strategies.
loss: float = MAX_LOSS
if trade_count >= self.config['hyperopt_min_trades']:
loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
min_date=min_date.datetime, max_date=max_date.datetime,
loss = self.calculate_loss(results=backtesting_results['results'],
trade_count=trade_count,
min_date=min_date, max_date=max_date,
config=self.config, processed=processed)
return {
'loss': loss,
'params_dict': params_dict,
'params_details': params_details,
'results_metrics': results_metrics,
'params_not_optimized': not_optimized,
'results_metrics': strat_stats,
'results_explanation': results_explanation,
'total_profit': total_profit,
}
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0])
draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0])
losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0])
return {
'trade_count': len(backtesting_results.index),
'wins': wins,
'draws': draws,
'losses': losses,
'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0,
'median_profit': backtesting_results['profit_ratio'].median() * 100.0,
'total_profit': backtesting_results['profit_abs'].sum(),
'profit': backtesting_results['profit_ratio'].sum() * 100.0,
'duration': backtesting_results['trade_duration'].mean(),
}
def _format_results_explanation_string(self, results_metrics: Dict) -> str:
"""
Return the formatted results explanation in a string
"""
stake_cur = self.config['stake_currency']
return (f"{results_metrics['trade_count']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
f"Median profit {results_metrics['median_profit']: 6.2f}%. "
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
f"Avg duration {results_metrics['duration']:5.1f} min."
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
return Optimizer(
dimensions,
@@ -369,24 +346,31 @@ class Hyperopt:
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1
def prepare_hyperopt_data(self) -> None:
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_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_timerange(preprocessed)
processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
self.min_date, self.max_date = get_timerange(processed)
dump(preprocessed, self.data_pickle_file)
logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
dump(processed, self.data_pickle_file)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1
# Initialize spaces ...
self.init_spaces()
self.prepare_hyperopt_data()
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange.close()
@@ -394,15 +378,12 @@ class Hyperopt:
self.backtesting.exchange._api_async = None # type: ignore
# self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
cpus = cpu_count()
logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
config_jobs = self.config.get('hyperopt_jobs', -1)
logger.info(f'Number of parallel jobs set as: {config_jobs}')
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:
@@ -468,25 +449,21 @@ class Hyperopt:
if is_best:
self.current_best_loss = val['loss']
self.epochs.append(val)
self.current_best_epoch = val
# Save results after each best epoch and every 100 epochs
if is_best or current % 100 == 0:
self._save_results()
self._save_result(val)
pbar.update(current)
except KeyboardInterrupt:
print('User interrupted..')
self._save_results()
logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
if self.epochs:
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
best_epoch = sorted_epochs[0]
HyperoptTools.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
if self.current_best_epoch:
HyperoptTools.print_epoch_details(self.current_best_epoch, self.total_epochs,
self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.

89
freqtrade/optimize/hyperopt_auto.py Normal file
View File

@@ -0,0 +1,89 @@
"""
HyperOptAuto class.
This module implements a convenience auto-hyperopt class, which can be used together with strategies
that implement IHyperStrategy interface.
"""
from contextlib import suppress
from typing import Any, Callable, Dict, List
from pandas import DataFrame
with suppress(ImportError):
from skopt.space import Dimension
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class HyperOptAuto(IHyperOpt):
"""
This class delegates functionality to Strategy(IHyperStrategy) and Strategy.HyperOpt classes.
Most of the time Strategy.HyperOpt class would only implement indicator_space and
sell_indicator_space methods, but other hyperopt methods can be overridden as well.
"""
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_buy_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('buy'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_buy_trend(dataframe, metadata)
return populate_buy_trend
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_sell_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('sell'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_sell_trend(dataframe, metadata)
return populate_sell_trend
def _get_func(self, name) -> Callable:
"""
Return a function defined in Strategy.HyperOpt class, or one defined in super() class.
:param name: function name.
:return: a requested function.
"""
hyperopt_cls = getattr(self.strategy, 'HyperOpt', None)
default_func = getattr(super(), name)
if hyperopt_cls:
return getattr(hyperopt_cls, name, default_func)
else:
return default_func
def _generate_indicator_space(self, category):
for attr_name, attr in self.strategy.enumerate_parameters(category):
if attr.optimize:
yield attr.get_space(attr_name)
def _get_indicator_space(self, category, fallback_method_name):
indicator_space = list(self._generate_indicator_space(category))
if len(indicator_space) > 0:
return indicator_space
else:
return self._get_func(fallback_method_name)()
def indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('buy', 'indicator_space')
def sell_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('sell', 'sell_indicator_space')
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
return self._get_func('generate_roi_table')(params)
def roi_space(self) -> List['Dimension']:
return self._get_func('roi_space')()
def stoploss_space(self) -> List['Dimension']:
return self._get_func('stoploss_space')()
def generate_trailing_params(self, params: Dict) -> Dict:
return self._get_func('generate_trailing_params')(params)
def trailing_space(self) -> List['Dimension']:
return self._get_func('trailing_space')()

57
freqtrade/optimize/hyperopt_interface.py
View File

@@ -7,11 +7,12 @@ import math
from abc import ABC
from typing import Any, Callable, Dict, List
from skopt.space import Categorical, Dimension, Integer, Real
from skopt.space import Categorical, Dimension, Integer
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
from freqtrade.optimize.space import SKDecimal
from freqtrade.strategy import IStrategy
@@ -31,7 +32,7 @@ class IHyperOpt(ABC):
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
timeframe -> int: value of the timeframe to use for the strategy
"""
ticker_interval: str # DEPRECATED
timeframe: str
@@ -44,36 +45,31 @@ class IHyperOpt(ABC):
IHyperOpt.ticker_interval = str(config['timeframe']) # DEPRECATED
IHyperOpt.timeframe = str(config['timeframe'])
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
"""
Create a buy strategy generator.
"""
raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy'))
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
"""
Create a sell strategy generator.
"""
raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))
@staticmethod
def indicator_space() -> List[Dimension]:
def indicator_space(self) -> List[Dimension]:
"""
Create an indicator space.
"""
raise OperationalException(_format_exception_message('indicator_space', 'buy'))
@staticmethod
def sell_indicator_space() -> List[Dimension]:
def sell_indicator_space(self) -> List[Dimension]:
"""
Create a sell indicator space.
"""
raise OperationalException(_format_exception_message('sell_indicator_space', 'sell'))
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
"""
Create a ROI table.
@@ -88,8 +84,7 @@ class IHyperOpt(ABC):
return roi_table
@staticmethod
def roi_space() -> List[Dimension]:
def roi_space(self) -> List[Dimension]:
"""
Create a ROI space.
@@ -97,7 +92,7 @@ class IHyperOpt(ABC):
This method implements adaptive roi hyperspace with varied
ranges for parameters which automatically adapts to the
ticker interval used.
timeframe used.
It's used by Freqtrade by default, if no custom roi_space method is defined.
"""
@@ -109,7 +104,7 @@ class IHyperOpt(ABC):
roi_t_alpha = 1.0
roi_p_alpha = 1.0
timeframe_min = timeframe_to_minutes(IHyperOpt.ticker_interval)
timeframe_min = timeframe_to_minutes(self.timeframe)
# We define here limits for the ROI space parameters automagically adapted to the
# timeframe used by the bot:
@@ -119,7 +114,7 @@ class IHyperOpt(ABC):
# * 'roi_p' (limits for the ROI value steps) components are scaled logarithmically.
#
# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
# method for the 5m ticker interval.
# method for the 5m timeframe.
roi_t_scale = timeframe_min / 5
roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)
roi_limits = {
@@ -145,7 +140,7 @@ class IHyperOpt(ABC):
'roi_p2': roi_limits['roi_p2_min'],
'roi_p3': roi_limits['roi_p3_min'],
}
logger.info(f"Min roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")
logger.info(f"Min roi table: {round_dict(self.generate_roi_table(p), 3)}")
p = {
'roi_t1': roi_limits['roi_t1_max'],
'roi_t2': roi_limits['roi_t2_max'],
@@ -154,19 +149,21 @@ class IHyperOpt(ABC):
'roi_p2': roi_limits['roi_p2_max'],
'roi_p3': roi_limits['roi_p3_max'],
}
logger.info(f"Max roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")
logger.info(f"Max roi table: {round_dict(self.generate_roi_table(p), 3)}")
return [
Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1'),
Integer(roi_limits['roi_t2_min'], roi_limits['roi_t2_max'], name='roi_t2'),
Integer(roi_limits['roi_t3_min'], roi_limits['roi_t3_max'], name='roi_t3'),
Real(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], name='roi_p1'),
Real(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], name='roi_p2'),
Real(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], name='roi_p3'),
SKDecimal(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], decimals=3,
name='roi_p1'),
SKDecimal(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], decimals=3,
name='roi_p2'),
SKDecimal(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], decimals=3,
name='roi_p3'),
]
@staticmethod
def stoploss_space() -> List[Dimension]:
def stoploss_space(self) -> List[Dimension]:
"""
Create a stoploss space.
@@ -174,11 +171,10 @@ class IHyperOpt(ABC):
You may override it in your custom Hyperopt class.
"""
return [
Real(-0.35, -0.02, name='stoploss'),
SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
]
@staticmethod
def generate_trailing_params(params: Dict) -> Dict:
def generate_trailing_params(self, params: Dict) -> Dict:
"""
Create dict with trailing stop parameters.
"""
@@ -190,8 +186,7 @@ class IHyperOpt(ABC):
'trailing_only_offset_is_reached': params['trailing_only_offset_is_reached'],
}
@staticmethod
def trailing_space() -> List[Dimension]:
def trailing_space(self) -> List[Dimension]:
"""
Create a trailing stoploss space.
@@ -206,14 +201,14 @@ class IHyperOpt(ABC):
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
Real(0.01, 0.35, name='trailing_stop_positive'),
SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]

189
freqtrade/optimize/hyperopt_tools.py
View File

@@ -1,19 +1,18 @@
import io
import locale
import logging
from collections import OrderedDict
from pathlib import Path
from pprint import pformat
from typing import Dict, List
from typing import Any, Dict, List
import rapidjson
import tabulate
from colorama import Fore, Style
from joblib import load
from pandas import isna, json_normalize
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_dict
from freqtrade.misc import round_coin_value, round_dict
logger = logging.getLogger(__name__)
@@ -21,13 +20,38 @@ logger = logging.getLogger(__name__)
class HyperoptTools():
@staticmethod
def has_space(config: Dict[str, Any], space: str) -> bool:
"""
Tell if the space value is contained in the configuration
"""
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
return any(s in config['spaces'] for s in [space, 'all'])
else:
return any(s in config['spaces'] for s in [space, 'all', 'default'])
@staticmethod
def _read_results_pickle(results_file: Path) -> List:
"""
Read hyperopt results from pickle file
LEGACY method - new files are written as json and cannot be read with this method.
"""
from joblib import load
logger.info(f"Reading pickled epochs from '{results_file}'")
data = load(results_file)
return data
@staticmethod
def _read_results(results_file: Path) -> List:
"""
Read hyperopt results from file
"""
logger.info("Reading epochs from '%s'", results_file)
data = load(results_file)
import rapidjson
logger.info(f"Reading epochs from '{results_file}'")
with results_file.open('r') as f:
data = [rapidjson.loads(line) for line in f]
return data
@staticmethod
@@ -37,7 +61,10 @@ class HyperoptTools():
"""
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
epochs = HyperoptTools._read_results(results_file)
if results_file.suffix == '.pickle':
epochs = HyperoptTools._read_results_pickle(results_file)
else:
epochs = HyperoptTools._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
@@ -53,6 +80,7 @@ class HyperoptTools():
Display details of the hyperopt result
"""
params = results.get('params_details', {})
non_optimized = results.get('params_not_optimized', {})
# Default header string
if header_str is None:
@@ -69,8 +97,10 @@ class HyperoptTools():
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:")
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:")
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
@@ -96,12 +126,12 @@ class HyperoptTools():
result_dict.update(space_params)
@staticmethod
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
if space in params or space in non_optimized:
space_params = HyperoptTools._space_params(params, space, 5)
params_result = f"\n# {header}\n"
result = f"\n# {header}\n"
if space == 'stoploss':
params_result += f"stoploss = {space_params.get('stoploss')}"
result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
@@ -110,28 +140,64 @@ class HyperoptTools():
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing':
for k, v in space_params.items():
params_result += f'{k} = {v}\n'
result += f'{k} = {v}\n'
else:
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
no_params = HyperoptTools._space_params(non_optimized, space, 5)
params_result = params_result.replace("\n", "\n ")
print(params_result)
result += f"{space}_params = {HyperoptTools._pprint(space_params, no_params)}"
result = result.replace("\n", "\n ")
print(result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
d = params[space]
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
d = params.get(space)
if d:
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
return {}
@staticmethod
def _pprint(params, non_optimized, indent: int = 4):
"""
Pretty-print hyperopt results (based on 2 dicts - with add. comment)
"""
p = params.copy()
p.update(non_optimized)
result = '{\n'
for k, param in p.items():
result += " " * indent + f'"{k}": '
result += f'"{param}",' if isinstance(param, str) else f'{param},'
if k in non_optimized:
result += " # value loaded from strategy"
result += "\n"
result += '}'
return result
@staticmethod
def is_best_loss(results, current_best_loss: float) -> bool:
return results['loss'] < current_best_loss
return bool(results['loss'] < current_best_loss)
@staticmethod
def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
"""
Return the formatted results explanation in a string
"""
return (f"{results_metrics['total_trades']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
f"({results_metrics['profit_total'] * 100: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
f"Avg duration {results_metrics['holding_avg']} min."
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
@@ -156,12 +222,27 @@ class HyperoptTools():
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
legacy_mode = True
if 'results_metrics.total_trades' in trials:
legacy_mode = False
# New mode, using backtest result for metrics
trials['results_metrics.winsdrawslosses'] = trials.apply(
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
f"{x['results_metrics.losses']:>4}", axis=1)
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.winsdrawslosses',
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
'results_metrics.profit_total', 'results_metrics.holding_avg',
'loss', 'is_initial_point', 'is_best']]
else:
# Legacy mode
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
@@ -171,26 +252,28 @@ class HyperoptTools():
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
perc_multi = 1 if legacy_mode else 100
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
stake_currency = config['stake_currency']
trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format(
x['Total profit'], config['stake_currency'],
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
).rjust(25+len(config['stake_currency']))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
lambda x: '{} {}'.format(
round_coin_value(x['Total profit'], stake_currency),
'({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
).rjust(25+len(stake_currency))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
axis=1
)
trials = trials.drop(columns=['Total profit'])
@@ -251,11 +334,21 @@ class HyperoptTools():
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.median_profit',
'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
if 'results_metrics.total_trades' in trials:
base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.profit_mean', 'results_metrics.profit_median',
'results_metrics.profit_total',
'Stake currency',
'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
'loss', 'is_initial_point', 'is_best']
perc_multi = 100
else:
perc_multi = 1
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.median_profit',
'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
@@ -272,21 +365,23 @@ class HyperoptTools():
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Median profit'] = trials['Median profit'] * perc_multi
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
lambda x: f'{x:,.8f}' if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
lambda x: f'{x:,.2f}' if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
lambda x: f'{x:,.1f} m' if isinstance(
x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
lambda x: f'{x:,.5f}' if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])

448
freqtrade/optimize/optimize_reports.py
View File

@@ -3,7 +3,6 @@ from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List, Union
from arrow import Arrow
from numpy import int64
from pandas import DataFrame
from tabulate import tabulate
@@ -44,7 +43,7 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
Generate floatformat (goes in line with _generate_result_line())
"""
return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
'.2f', 'd', 'd', 'd', 'd']
'.2f', 'd', 's', 's']
def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
@@ -53,7 +52,17 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
"""
return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Wins', 'Draws', 'Losses']
'Win Draw Loss Win%']
def _generate_wins_draws_losses(wins, draws, losses):
if wins > 0 and losses == 0:
wl_ratio = '100'
elif wins == 0:
wl_ratio = '0'
else:
wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
return f'{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}'
def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
@@ -110,6 +119,9 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_b
tabular_data.append(_generate_result_line(result, starting_balance, pair))
# Sort by total profit %:
tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)
# Append Total
tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
return tabular_data
@@ -150,7 +162,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
return tabular_data
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
@@ -162,6 +174,17 @@ def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
tabular_data.append(_generate_result_line(
results['results'], results['config']['dry_run_wallet'], strategy)
)
try:
max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_ratio')
max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_abs')
except ValueError:
max_drawdown_per = 0
max_drawdown_abs = 0
tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
tabular_data[-1]['max_drawdown_abs'] = \
round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
return tabular_data
@@ -191,7 +214,40 @@ def generate_edge_table(results: dict) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
""" Generate overall trade statistics """
if len(results) == 0:
return {
'wins': 0,
'losses': 0,
'draws': 0,
'holding_avg': timedelta(),
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
winning_trades = results.loc[results['profit_ratio'] > 0]
draw_trades = results.loc[results['profit_ratio'] == 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
zero_duration_trades = len(results.loc[(results['trade_duration'] == 0) &
(results['sell_reason'] == 'trailing_stop_loss')])
return {
'wins': len(winning_trades),
'losses': len(losing_trades),
'draws': len(draw_trades),
'holding_avg': (timedelta(minutes=round(results['trade_duration'].mean()))
if not results.empty else timedelta()),
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
'zero_duration_trades': zero_duration_trades,
}
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
""" Generate daily statistics """
if len(results) == 0:
return {
'backtest_best_day': 0,
@@ -201,8 +257,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
'winning_days': 0,
'draw_days': 0,
'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
@@ -214,9 +268,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
winning_trades = results.loc[results['profit_ratio'] > 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
return {
'backtest_best_day': best_rel,
'backtest_worst_day': worst_rel,
@@ -225,16 +276,152 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
}
def generate_strategy_stats(btdata: Dict[str, DataFrame],
strategy: str,
content: Dict[str, Any],
min_date: datetime, max_date: datetime,
market_change: float
) -> Dict[str, Any]:
"""
:param btdata: Backtest data
:param strategy: Strategy name
:param content: Backtest result data in the format:
{'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:param market_change: float indicating the market change
:return: Dictionary containing results per strategy and a stratgy summary.
"""
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
return {}
config = content['config']
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
starting_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results.loc[results['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
trade_stats = generate_trading_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = {
'trades': results.to_dict(orient='records'),
'locks': [lock.to_json() for lock in content['locks']],
'best_pair': best_pair,
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
'profit_total': results['profit_abs'].sum() / starting_balance,
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts': int(min_date.timestamp() * 1000),
'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_end_ts': int(max_date.timestamp() * 1000),
'backtest_days': backtest_days,
'backtest_run_start_ts': content['backtest_start_time'],
'backtest_run_end_ts': content['backtest_end_time'],
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'starting_balance': starting_balance,
'dry_run_wallet': starting_balance,
'final_balance': content['final_balance'],
'rejected_signals': content['rejected_signals'],
'max_open_trades': max_open_trades,
'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
'timerange': config.get('timerange', ''),
'enable_protections': config.get('enable_protections', False),
'strategy_name': strategy,
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'use_custom_stoploss': config.get('use_custom_stoploss', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
**trade_stats
}
try:
max_drawdown, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
results, value_col='profit_abs')
strat_stats.update({
'max_drawdown': max_drawdown,
'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end.strftime(DATETIME_PRINT_FORMAT),
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
'max_drawdown_low': low_val,
'max_drawdown_high': high_val,
})
csum_min, csum_max = calculate_csum(results, starting_balance)
strat_stats.update({
'csum_min': csum_min,
'csum_max': csum_max
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0,
'max_drawdown_high': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
'csum_min': 0,
'csum_max': 0
})
return strat_stats
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
min_date: Arrow, max_date: Arrow
min_date: datetime, max_date: datetime
) -> Dict[str, Any]:
"""
:param btdata: Backtest data
@@ -242,132 +429,17 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
{ Strategy: {'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
:return: Dictionary containing results per strategy and a stratgy summary.
"""
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, content in all_results.items():
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
continue
config = content['config']
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
starting_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results.loc[results['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = {
'trades': results.to_dict(orient='records'),
'locks': [lock.to_json() for lock in content['locks']],
'best_pair': best_pair,
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_abs'].sum() / starting_balance,
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.int_timestamp * 1000,
'backtest_end': max_date.datetime,
'backtest_end_ts': max_date.int_timestamp * 1000,
'backtest_days': backtest_days,
'backtest_run_start_ts': content['backtest_start_time'],
'backtest_run_end_ts': content['backtest_end_time'],
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'starting_balance': starting_balance,
'dry_run_wallet': starting_balance,
'final_balance': content['final_balance'],
'max_open_trades': max_open_trades,
'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
'timerange': config.get('timerange', ''),
'enable_protections': config.get('enable_protections', False),
'strategy_name': strategy,
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'use_custom_stoploss': config.get('use_custom_stoploss', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
}
strat_stats = generate_strategy_stats(btdata, strategy, content,
min_date, max_date, market_change=market_change)
result['strategy'][strategy] = strat_stats
try:
max_drawdown, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
results, value_col='profit_abs')
strat_stats.update({
'max_drawdown': max_drawdown,
'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start,
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end,
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
'max_drawdown_low': low_val,
'max_drawdown_high': high_val,
})
csum_min, csum_max = calculate_csum(results, starting_balance)
strat_stats.update({
'csum_min': csum_min,
'csum_max': csum_max
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0,
'max_drawdown_high': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
'csum_min': 0,
'csum_max': 0
})
strategy_results = generate_strategy_metrics(all_results=all_results)
strategy_results = generate_strategy_comparison(all_results=all_results)
result['strategy_comparison'] = strategy_results
@@ -390,7 +462,8 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
floatfmt = _get_line_floatfmt(stake_currency)
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
] for t in pair_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
@@ -407,9 +480,7 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
headers = [
'Sell Reason',
'Sells',
'Wins',
'Draws',
'Losses',
'Win Draws Loss Win%',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
@@ -417,7 +488,8 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
]
output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
t['sell_reason'], t['trades'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
t['profit_mean_pct'], t['profit_sum_pct'],
round_coin_value(t['profit_total_abs'], stake_currency, False),
t['profit_total_pct'],
@@ -435,11 +507,22 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
"""
floatfmt = _get_line_floatfmt(stake_currency)
headers = _get_line_header('Strategy', stake_currency)
# _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
# therefore we slip this column in only for strategy summary here.
headers.append('Drawdown')
# Align drawdown string on the center two space separator.
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
dd_pad_per = max([len(dd) for dd in drawdown])
drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
for t, dd in zip(strategy_results, drawdown)]
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
] for t in strategy_results]
t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
for t, drawdown in zip(strategy_results, drawdown)]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
@@ -449,9 +532,21 @@ def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
# command stores these results and newer version of freqtrade must be able to handle old
# results with missing new fields.
zero_duration_trades = '--'
if 'zero_duration_trades' in strat_results:
zero_duration_trades_per = \
100.0 / strat_results['total_trades'] * strat_results['zero_duration_trades']
zero_duration_trades = f'{zero_duration_trades_per:.2f}% ' \
f'({strat_results["zero_duration_trades"]})'
metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting from', strat_results['backtest_start']),
('Backtesting to', strat_results['backtest_end']),
('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability
('Total trades', strat_results['total_trades']),
@@ -461,13 +556,12 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2):}%"),
('Trades per day', strat_results['trades_per_day']),
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'],
strat_results['stake_currency'])),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
@@ -485,6 +579,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('Zero Duration Trades', zero_duration_trades),
('Rejected Buy signals', strat_results.get('rejected_signals', 'N/A')),
('', ''), # Empty line to improve readability
('Min balance', round_coin_value(strat_results['csum_min'],
@@ -499,8 +595,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end']),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
]
@@ -519,37 +615,43 @@ def text_table_add_metrics(strat_results: Dict) -> str:
return message
def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str):
"""
Print results for one strategy
"""
# Print results
print(f"Result for strategy {strategy}")
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()
def show_backtest_results(config: Dict, backtest_stats: Dict):
stake_currency = config['stake_currency']
for strategy, results in backtest_stats['strategy'].items():
# Print results
print(f"Result for strategy {strategy}")
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()
show_backtest_result(strategy, results, stake_currency)
if len(backtest_stats['strategy']) > 1:
# Print Strategy summary table

4
freqtrade/optimize/space/__init__.py Normal file
View File

@@ -0,0 +1,4 @@
# flake8: noqa: F401
from skopt.space import Categorical, Dimension, Integer, Real
from .decimalspace import SKDecimal

33
freqtrade/optimize/space/decimalspace.py Normal file
View File

@@ -0,0 +1,33 @@
import numpy as np
from skopt.space import Integer
class SKDecimal(Integer):
def __init__(self, low, high, decimals=3, prior="uniform", base=10, transform=None,
name=None, dtype=np.int64):
self.decimals = decimals
_low = int(low * pow(10, self.decimals))
_high = int(high * pow(10, self.decimals))
# trunc to precision to avoid points out of space
self.low_orig = round(_low * pow(0.1, self.decimals), self.decimals)
self.high_orig = round(_high * pow(0.1, self.decimals), self.decimals)
super().__init__(_low, _high, prior, base, transform, name, dtype)
def __repr__(self):
return "Decimal(low={}, high={}, decimals={}, prior='{}', transform='{}')".format(
self.low_orig, self.high_orig, self.decimals, self.prior, self.transform_)
def __contains__(self, point):
if isinstance(point, list):
point = np.array(point)
return self.low_orig <= point <= self.high_orig
def transform(self, Xt):
aa = [int(x * pow(10, self.decimals)) for x in Xt]
return super().transform(aa)
def inverse_transform(self, Xt):
res = super().inverse_transform(Xt)
return [round(x * pow(0.1, self.decimals), self.decimals) for x in res]

32
freqtrade/persistence/migrations.py
View File

@@ -123,6 +123,27 @@ def migrate_open_orders_to_trades(engine):
""")
def migrate_orders_table(decl_base, inspector, engine, table_back_name: str, cols: List):
# Schema migration necessary
engine.execute(f"alter table orders rename to {table_back_name}")
# drop indexes on backup table
for index in inspector.get_indexes(table_back_name):
engine.execute(f"drop index {index['name']}")
# let SQLAlchemy create the schema as required
decl_base.metadata.create_all(engine)
engine.execute(f"""
insert into orders ( id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
symbol, order_type, side, price, amount, filled, average, remaining, cost, order_date,
order_filled_date, order_update_date)
select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
symbol, order_type, side, price, amount, filled, null average, remaining, cost, order_date,
order_filled_date, order_update_date
from {table_back_name}
""")
def check_migrate(engine, decl_base, previous_tables) -> None:
"""
Checks if migration is necessary and migrates if necessary
@@ -145,6 +166,11 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
logger.info('Moving open orders to Orders table.')
migrate_open_orders_to_trades(engine)
else:
pass
# Empty for now - as there is only one iteration of the orders table so far.
# table_back_name = get_backup_name(tabs, 'orders_bak')
cols_order = inspector.get_columns('orders')
if not has_column(cols_order, 'average'):
tabs = get_table_names_for_table(inspector, 'orders')
# Empty for now - as there is only one iteration of the orders table so far.
table_back_name = get_backup_name(tabs, 'orders_bak')
migrate_orders_table(decl_base, inspector, engine, table_back_name, cols)

269
freqtrade/persistence/models.py
View File

@@ -6,7 +6,6 @@ from datetime import datetime, timezone
from decimal import Decimal
from typing import Any, Dict, List, Optional
import arrow
from sqlalchemy import (Boolean, Column, DateTime, Float, ForeignKey, Integer, String,
create_engine, desc, func, inspect)
from sqlalchemy.exc import NoSuchModuleError
@@ -59,13 +58,10 @@ def init_db(db_url: str, clean_open_orders: bool = False) -> None:
# 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()
# Copy session attributes to order object too
Order.session = Trade.session
Order.query = Order.session.query_property()
PairLock.session = Trade.session
PairLock.query = PairLock.session.query_property()
Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
Trade.query = Trade._session.query_property()
Order.query = Trade._session.query_property()
PairLock.query = Trade._session.query_property()
previous_tables = inspect(engine).get_table_names()
_DECL_BASE.metadata.create_all(engine)
@@ -81,7 +77,7 @@ def cleanup_db() -> None:
Flushes all pending operations to disk.
:return: None
"""
Trade.session.flush()
Trade.query.session.flush()
def clean_dry_run_db() -> None:
@@ -116,16 +112,17 @@ class Order(_DECL_BASE):
trade = relationship("Trade", back_populates="orders")
ft_order_side = Column(String, nullable=False)
ft_pair = Column(String, nullable=False)
ft_order_side = Column(String(25), nullable=False)
ft_pair = Column(String(25), nullable=False)
ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
order_id = Column(String, nullable=False, index=True)
status = Column(String, nullable=True)
symbol = Column(String, nullable=True)
order_type = Column(String, nullable=True)
side = Column(String, nullable=True)
order_id = Column(String(255), nullable=False, index=True)
status = Column(String(255), nullable=True)
symbol = Column(String(25), nullable=True)
order_type = Column(String(50), nullable=True)
side = Column(String(25), nullable=True)
price = Column(Float, nullable=True)
average = Column(Float, nullable=True)
amount = Column(Float, nullable=True)
filled = Column(Float, nullable=True)
remaining = Column(Float, nullable=True)
@@ -154,6 +151,7 @@ class Order(_DECL_BASE):
self.price = order.get('price', self.price)
self.amount = order.get('amount', self.amount)
self.filled = order.get('filled', self.filled)
self.average = order.get('average', self.average)
self.remaining = order.get('remaining', self.remaining)
self.cost = order.get('cost', self.cost)
if 'timestamp' in order and order['timestamp'] is not None:
@@ -163,8 +161,8 @@ class Order(_DECL_BASE):
if self.status in ('closed', 'canceled', 'cancelled'):
self.ft_is_open = False
if order.get('filled', 0) > 0:
self.order_filled_date = arrow.utcnow().datetime
self.order_update_date = arrow.utcnow().datetime
self.order_filled_date = datetime.now(timezone.utc)
self.order_update_date = datetime.now(timezone.utc)
@staticmethod
def update_orders(orders: List['Order'], order: Dict[str, Any]):
@@ -297,15 +295,12 @@ class LocalTrade():
'fee_close_cost': self.fee_close_cost,
'fee_close_currency': self.fee_close_currency,
'open_date_hum': arrow.get(self.open_date).humanize(),
'open_date': self.open_date.strftime(DATETIME_PRINT_FORMAT),
'open_timestamp': int(self.open_date.replace(tzinfo=timezone.utc).timestamp() * 1000),
'open_rate': self.open_rate,
'open_rate_requested': self.open_rate_requested,
'open_trade_value': round(self.open_trade_value, 8),
'close_date_hum': (arrow.get(self.close_date).humanize()
if self.close_date else None),
'close_date': (self.close_date.strftime(DATETIME_PRINT_FORMAT)
if self.close_date else None),
'close_timestamp': int(self.close_date.replace(
@@ -554,6 +549,8 @@ class LocalTrade():
rate=(rate or self.close_rate),
fee=(fee or self.fee_close)
)
if self.open_trade_value == 0.0:
return 0.0
profit_ratio = (close_trade_value / self.open_trade_value) - 1
return float(f"{profit_ratio:.8f}")
@@ -572,23 +569,6 @@ class LocalTrade():
else:
return None
@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_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
@@ -611,7 +591,7 @@ class LocalTrade():
else:
# Not used during backtesting, but might be used by a strategy
sel_trades = [trade for trade in LocalTrade.trades + LocalTrade.trades_open]
sel_trades = list(LocalTrade.trades + LocalTrade.trades_open)
if pair:
sel_trades = [trade for trade in sel_trades if trade.pair == pair]
@@ -641,83 +621,7 @@ class LocalTrade():
"""
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 get_open_trades_without_assigned_fees():
"""
Returns all open trades which don't have open fees set correctly
"""
return Trade.get_trades([Trade.fee_open_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(True),
]).all()
@staticmethod
def get_sold_trades_without_assigned_fees():
"""
Returns all closed trades which don't have fees set correctly
"""
return Trade.get_trades([Trade.fee_close_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(False),
]).all()
@staticmethod
def total_open_trades_stakes() -> float:
"""
Calculates total invested amount in open trades
in stake currency
"""
if Trade.use_db:
total_open_stake_amount = Trade.session.query(
func.sum(Trade.stake_amount)).filter(Trade.is_open.is_(True)).scalar()
else:
total_open_stake_amount = sum(
t.stake_amount for t in Trade.get_trades_proxy(is_open=True))
return total_open_stake_amount or 0
@staticmethod
def get_overall_performance() -> List[Dict[str, Any]]:
"""
Returns List of dicts containing all Trades, including profit and trade count
"""
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.
:returns: Tuple containing (pair, profit_sum)
"""
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
return Trade.get_trades_proxy(is_open=True)
@staticmethod
def stoploss_reinitialization(desired_stoploss):
@@ -754,15 +658,15 @@ class Trade(_DECL_BASE, LocalTrade):
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
exchange = Column(String, nullable=False)
pair = Column(String, nullable=False, index=True)
exchange = Column(String(25), nullable=False)
pair = Column(String(25), nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True)
fee_open_currency = Column(String, nullable=True)
fee_open_currency = Column(String(25), nullable=True)
fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True)
fee_close_currency = Column(String, nullable=True)
fee_close_currency = Column(String(25), nullable=True)
open_rate = Column(Float)
open_rate_requested = Column(Float)
# open_trade_value - calculated via _calc_open_trade_value
@@ -776,7 +680,7 @@ class Trade(_DECL_BASE, LocalTrade):
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
open_order_id = Column(String(255))
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the stop loss
@@ -786,16 +690,16 @@ class Trade(_DECL_BASE, LocalTrade):
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
# stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True)
stoploss_order_id = Column(String(255), nullable=True, index=True)
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
# Lowest price reached
min_rate = Column(Float, nullable=True)
sell_reason = Column(String, nullable=True)
sell_order_status = Column(String, nullable=True)
strategy = Column(String, nullable=True)
sell_reason = Column(String(100), nullable=True)
sell_order_status = Column(String(100), nullable=True)
strategy = Column(String(100), nullable=True)
timeframe = Column(Integer, nullable=True)
def __init__(self, **kwargs):
@@ -805,17 +709,17 @@ class Trade(_DECL_BASE, LocalTrade):
def delete(self) -> None:
for order in self.orders:
Order.session.delete(order)
Order.query.session.delete(order)
Trade.session.delete(self)
Trade.session.flush()
Trade.query.session.delete(self)
Trade.query.session.flush()
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
) -> List['LocalTrade']:
"""
Helper function to query Trades.
Helper function to query Trades.j
Returns a List of trades, filtered on the parameters given.
In live mode, converts the filter to a database query and returns all rows
In Backtest mode, uses filters on Trade.trades to get the result.
@@ -840,6 +744,109 @@ class Trade(_DECL_BASE, LocalTrade):
close_date=close_date
)
@staticmethod
def get_trades(trade_filter=None) -> Query:
"""
Helper function to query Trades using filters.
NOTE: Not supported in Backtesting.
: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 not Trade.use_db:
raise NotImplementedError('`Trade.get_trades()` not supported in backtesting mode.')
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_order_trades():
"""
Returns all open trades
NOTE: Not supported in Backtesting.
"""
return Trade.get_trades(Trade.open_order_id.isnot(None)).all()
@staticmethod
def get_open_trades_without_assigned_fees():
"""
Returns all open trades which don't have open fees set correctly
NOTE: Not supported in Backtesting.
"""
return Trade.get_trades([Trade.fee_open_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(True),
]).all()
@staticmethod
def get_sold_trades_without_assigned_fees():
"""
Returns all closed trades which don't have fees set correctly
NOTE: Not supported in Backtesting.
"""
return Trade.get_trades([Trade.fee_close_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(False),
]).all()
@staticmethod
def total_open_trades_stakes() -> float:
"""
Calculates total invested amount in open trades
in stake currency
"""
if Trade.use_db:
total_open_stake_amount = Trade.query.with_entities(
func.sum(Trade.stake_amount)).filter(Trade.is_open.is_(True)).scalar()
else:
total_open_stake_amount = sum(
t.stake_amount for t in LocalTrade.get_trades_proxy(is_open=True))
return total_open_stake_amount or 0
@staticmethod
def get_overall_performance() -> List[Dict[str, Any]]:
"""
Returns List of dicts containing all Trades, including profit and trade count
NOTE: Not supported in Backtesting.
"""
pair_rates = Trade.query.with_entities(
Trade.pair,
func.sum(Trade.close_profit).label('profit_sum'),
func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
func.count(Trade.pair).label('count')
).filter(Trade.is_open.is_(False))\
.group_by(Trade.pair) \
.order_by(desc('profit_sum_abs')) \
.all()
return [
{
'pair': pair,
'profit': profit,
'profit_abs': profit_abs,
'count': count
}
for pair, profit, profit_abs, count in pair_rates
]
@staticmethod
def get_best_pair():
"""
Get best pair with closed trade.
NOTE: Not supported in Backtesting.
:returns: Tuple containing (pair, profit_sum)
"""
best_pair = Trade.query.with_entities(
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
class PairLock(_DECL_BASE):
"""
@@ -849,8 +856,8 @@ class PairLock(_DECL_BASE):
id = Column(Integer, primary_key=True)
pair = Column(String, nullable=False, index=True)
reason = Column(String, nullable=True)
pair = Column(String(25), nullable=False, index=True)
reason = Column(String(255), nullable=True)
# Time the pair was locked (start time)
lock_time = Column(DateTime, nullable=False)
# Time until the pair is locked (end time)

6
freqtrade/persistence/pairlock_middleware.py
View File

@@ -48,8 +48,8 @@ class PairLocks():
active=True
)
if PairLocks.use_db:
PairLock.session.add(lock)
PairLock.session.flush()
PairLock.query.session.add(lock)
PairLock.query.session.flush()
else:
PairLocks.locks.append(lock)
@@ -99,7 +99,7 @@ class PairLocks():
for lock in locks:
lock.active = False
if PairLocks.use_db:
PairLock.session.flush()
PairLock.query.session.flush()
@staticmethod
def is_global_lock(now: Optional[datetime] = None) -> bool:

19
freqtrade/plot/plotting.py
View File

@@ -77,7 +77,8 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
)
except ValueError as e:
raise OperationalException(e) from e
trades = trim_dataframe(trades, timerange, 'open_date')
if not trades.empty:
trades = trim_dataframe(trades, timerange, 'open_date')
return {"ohlcv": data,
"trades": trades,
@@ -441,7 +442,7 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
trades: pd.DataFrame, timeframe: str) -> go.Figure:
trades: pd.DataFrame, timeframe: str, stake_currency: str) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
df_comb = combine_dataframes_with_mean(data, "close")
@@ -466,8 +467,8 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
subplot_titles=["AVG Close Price", "Combined Profit", "Profit per pair"])
fig['layout'].update(title="Freqtrade Profit plot")
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title='Profit')
fig['layout']['yaxis3'].update(title='Profit')
fig['layout']['yaxis2'].update(title=f'Profit {stake_currency}')
fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
fig['layout']['xaxis']['rangeslider'].update(visible=False)
fig.add_trace(avgclose, 1, 1)
@@ -540,8 +541,11 @@ def load_and_plot_trades(config: Dict[str, Any]):
df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
df_analyzed = trim_dataframe(df_analyzed, timerange)
trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
if not trades.empty:
trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
else:
trades_pair = trades
fig = generate_candlestick_graph(
pair=pair,
@@ -581,6 +585,7 @@ def plot_profit(config: Dict[str, Any]) -> None:
# 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['ohlcv'],
trades, config.get('timeframe', '5m'))
trades, config.get('timeframe', '5m'),
config.get('stake_currency', ''))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / 'plot', auto_open=True)

6
freqtrade/plugins/pairlist/AgeFilter.py
View File

@@ -71,14 +71,14 @@ class AgeFilter(IPairList):
daily_candles = candles[(p, '1d')] if (p, '1d') in candles else None
if not self._validate_pair_loc(p, daily_candles):
pairlist.remove(p)
logger.info(f"Validated {len(pairlist)} pairs.")
self.log_once(f"Validated {len(pairlist)} pairs.", logger.info)
return pairlist
def _validate_pair_loc(self, pair: str, daily_candles: Optional[DataFrame]) -> bool:
"""
Validate age for the ticker
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache
@@ -86,7 +86,7 @@ class AgeFilter(IPairList):
return True
if daily_candles is not None:
if len(daily_candles) > self._min_days_listed:
if len(daily_candles) >= self._min_days_listed:
# We have fetched at least the minimum required number of daily candles
# Add to cache, store the time we last checked this symbol
self._symbolsChecked[pair] = int(arrow.utcnow().float_timestamp) * 1000

13
freqtrade/plugins/pairlist/IPairList.py
View File

@@ -7,7 +7,7 @@ from copy import deepcopy
from typing import Any, Dict, List
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active
from freqtrade.exchange import Exchange, market_is_active
from freqtrade.mixins import LoggingMixin
@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
class IPairList(LoggingMixin, ABC):
def __init__(self, exchange, pairlistmanager,
def __init__(self, exchange: Exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
"""
@@ -28,7 +28,7 @@ class IPairList(LoggingMixin, ABC):
"""
self._enabled = True
self._exchange = exchange
self._exchange: Exchange = exchange
self._pairlistmanager = pairlistmanager
self._config = config
self._pairlistconfig = pairlistconfig
@@ -68,12 +68,12 @@ class IPairList(LoggingMixin, ABC):
filter_pairlist() method.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
raise NotImplementedError()
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist.
@@ -84,8 +84,7 @@ class IPairList(LoggingMixin, ABC):
it will raise the exception if a Pairlist Handler is used at the first
position in the chain.
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: List of pairs
"""
raise OperationalException("This Pairlist Handler should not be used "

14
freqtrade/plugins/pairlist/PerformanceFilter.py
View File

@@ -2,7 +2,7 @@
Performance pair list filter
"""
import logging
from typing import Any, Dict, List
from typing import Dict, List
import pandas as pd
@@ -15,11 +15,6 @@ logger = logging.getLogger(__name__)
class PerformanceFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
@property
def needstickers(self) -> bool:
"""
@@ -44,7 +39,12 @@ class PerformanceFilter(IPairList):
:return: new allowlist
"""
# Get the trading performance for pairs from database
performance = pd.DataFrame(Trade.get_overall_performance())
try:
performance = pd.DataFrame(Trade.get_overall_performance())
except AttributeError:
# Performancefilter does not work in backtesting.
self.log_once("PerformanceFilter is not available in this mode.", logger.warning)
return pairlist
# Skip performance-based sorting if no performance data is available
if len(performance) == 0:

2
freqtrade/plugins/pairlist/PrecisionFilter.py
View File

@@ -48,7 +48,7 @@ class PrecisionFilter(IPairList):
Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
low value pairs.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
stop_price = ticker['ask'] * self._stoploss

38
freqtrade/plugins/pairlist/PriceFilter.py
View File

@@ -27,9 +27,13 @@ class PriceFilter(IPairList):
self._max_price = pairlistconfig.get('max_price', 0)
if self._max_price < 0:
raise OperationalException("PriceFilter requires max_price to be >= 0")
self._max_value = pairlistconfig.get('max_value', 0)
if self._max_value < 0:
raise OperationalException("PriceFilter requires max_value to be >= 0")
self._enabled = ((self._low_price_ratio > 0) or
(self._min_price > 0) or
(self._max_price > 0))
(self._max_price > 0) or
(self._max_value > 0))
@property
def needstickers(self) -> bool:
@@ -51,6 +55,8 @@ class PriceFilter(IPairList):
active_price_filters.append(f"below {self._min_price:.8f}")
if self._max_price != 0:
active_price_filters.append(f"above {self._max_price:.8f}")
if self._max_value != 0:
active_price_filters.append(f"Value above {self._max_value:.8f}")
if len(active_price_filters):
return f"{self.name} - Filtering pairs priced {' or '.join(active_price_filters)}."
@@ -61,7 +67,7 @@ class PriceFilter(IPairList):
"""
Check if if one price-step (pip) is > than a certain barrier.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
if ticker.get('last', None) is None or ticker.get('last') == 0:
@@ -79,6 +85,32 @@ class PriceFilter(IPairList):
f"because 1 unit is {changeperc * 100:.3f}%", logger.info)
return False
# Perform low_amount check
if self._max_value != 0:
price = ticker['last']
market = self._exchange.markets[pair]
limits = market['limits']
if ('amount' in limits and 'min' in limits['amount']
and limits['amount']['min'] is not None):
min_amount = limits['amount']['min']
min_precision = market['precision']['amount']
min_value = min_amount * price
if self._exchange.precisionMode == 4:
# tick size
next_value = (min_amount + min_precision) * price
else:
# Decimal places
min_precision = pow(0.1, min_precision)
next_value = (min_amount + min_precision) * price
diff = next_value - min_value
if diff > self._max_value:
self.log_once(f"Removed {pair} from whitelist, "
f"because min value change of {diff} > {self._max_value}.",
logger.info)
return False
# Perform min_price check.
if self._min_price != 0:
if ticker['last'] < self._min_price:
@@ -89,7 +121,7 @@ class PriceFilter(IPairList):
# Perform max_price check.
if self._max_price != 0:
if ticker['last'] > self._max_price:
self.log_once(f"Removed {ticker['symbol']} from whitelist, "
self.log_once(f"Removed {pair} from whitelist, "
f"because last price > {self._max_price:.8f}", logger.info)
return False

2
freqtrade/plugins/pairlist/SpreadFilter.py
View File

@@ -40,7 +40,7 @@ class SpreadFilter(IPairList):
"""
Validate spread for the ticker
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
if 'bid' in ticker and 'ask' in ticker and ticker['ask']:

5
freqtrade/plugins/pairlist/StaticPairList.py
View File

@@ -42,11 +42,10 @@ class StaticPairList(IPairList):
"""
return f"{self.name}"
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: List of pairs
"""
if self._allow_inactive:

121
freqtrade/plugins/pairlist/VolatilityFilter.py Normal file
View File

@@ -0,0 +1,121 @@
"""
Volatility pairlist filter
"""
import logging
import sys
from copy import deepcopy
from typing import Any, Dict, List, Optional
import arrow
import numpy as np
from cachetools.ttl import TTLCache
from pandas import DataFrame
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.plugins.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class VolatilityFilter(IPairList):
'''
Filters pairs by volatility
'''
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._days = pairlistconfig.get('lookback_days', 10)
self._min_volatility = pairlistconfig.get('min_volatility', 0)
self._max_volatility = pairlistconfig.get('max_volatility', sys.maxsize)
self._refresh_period = pairlistconfig.get('refresh_period', 1440)
self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
if self._days < 1:
raise OperationalException("VolatilityFilter requires lookback_days to be >= 1")
if self._days > exchange.ohlcv_candle_limit('1d'):
raise OperationalException("VolatilityFilter requires lookback_days to not "
"exceed exchange max request size "
f"({exchange.ohlcv_candle_limit('1d')})")
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requires 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
"""
return (f"{self.name} - Filtering pairs with volatility range "
f"{self._min_volatility}-{self._max_volatility} "
f" the last {self._days} {plural(self._days, 'day')}.")
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Validate trading range
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new allowlist
"""
needed_pairs = [(p, '1d') for p in pairlist if p not in self._pair_cache]
since_ms = int(arrow.utcnow()
.floor('day')
.shift(days=-self._days - 1)
.float_timestamp) * 1000
# Get all candles
candles = {}
if needed_pairs:
candles = self._exchange.refresh_latest_ohlcv(needed_pairs, since_ms=since_ms,
cache=False)
if self._enabled:
for p in deepcopy(pairlist):
daily_candles = candles[(p, '1d')] if (p, '1d') in candles else None
if not self._validate_pair_loc(p, daily_candles):
pairlist.remove(p)
return pairlist
def _validate_pair_loc(self, pair: str, daily_candles: Optional[DataFrame]) -> bool:
"""
Validate trading range
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache
cached_res = self._pair_cache.get(pair, None)
if cached_res is not None:
return cached_res
result = False
if daily_candles is not None and not daily_candles.empty:
returns = (np.log(daily_candles.close / daily_candles.close.shift(-1)))
returns.fillna(0, inplace=True)
volatility_series = returns.rolling(window=self._days).std()*np.sqrt(self._days)
volatility_avg = volatility_series.mean()
if self._min_volatility <= volatility_avg <= self._max_volatility:
result = True
else:
self.log_once(f"Removed {pair} from whitelist, because volatility "
f"over {self._days} {plural(self._days, 'day')} "
f"is: {volatility_avg:.3f} "
f"which is not in the configured range of "
f"{self._min_volatility}-{self._max_volatility}.",
logger.info)
result = False
self._pair_cache[pair] = result
return result

24
freqtrade/plugins/pairlist/VolumePairList.py
View File

@@ -4,9 +4,10 @@ Volume PairList provider
Provides dynamic pair list based on trade volumes
"""
import logging
from datetime import datetime
from typing import Any, Dict, List
from cachetools.ttl import TTLCache
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.IPairList import IPairList
@@ -33,7 +34,8 @@ class VolumePairList(IPairList):
self._number_pairs = self._pairlistconfig['number_assets']
self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
self._min_value = self._pairlistconfig.get('min_value', 0)
self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
self._refresh_period = self._pairlistconfig.get('refresh_period', 1800)
self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
if not self._exchange.exchange_has('fetchTickers'):
raise OperationalException(
@@ -63,17 +65,19 @@ class VolumePairList(IPairList):
"""
return f"{self.name} - top {self._pairlistconfig['number_assets']} volume pairs."
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: List of pairs
"""
# Generate dynamic whitelist
# Must always run if this pairlist is not the first in the list.
if self._last_refresh + self.refresh_period < datetime.now().timestamp():
self._last_refresh = int(datetime.now().timestamp())
pairlist = self._pair_cache.get('pairlist')
if pairlist:
# Item found - no refresh necessary
return pairlist
else:
# Use fresh pairlist
# Check if pair quote currency equals to the stake currency.
@@ -82,9 +86,9 @@ class VolumePairList(IPairList):
if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
and v[self._sort_key] is not None)]
pairlist = [s['symbol'] for s in filtered_tickers]
else:
# Use the cached pairlist if it's not time yet to refresh
pairlist = cached_pairlist
pairlist = self.filter_pairlist(pairlist, tickers)
self._pair_cache['pairlist'] = pairlist
return pairlist

7
freqtrade/plugins/pairlist/rangestabilityfilter.py
View File

@@ -83,12 +83,13 @@ class RangeStabilityFilter(IPairList):
"""
Validate trading range
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache
if pair in self._pair_cache:
return self._pair_cache[pair]
cached_res = self._pair_cache.get(pair, None)
if cached_res is not None:
return cached_res
result = False
if daily_candles is not None and not daily_candles.empty:

20
freqtrade/plugins/pairlistmanager.py
View File

@@ -3,7 +3,7 @@ PairList manager class
"""
import logging
from copy import deepcopy
from typing import Any, Dict, List
from typing import Dict, List
from cachetools import TTLCache, cached
@@ -79,11 +79,8 @@ class PairListManager():
if self._tickers_needed:
tickers = self._get_cached_tickers()
# Adjust whitelist if filters are using tickers
pairlist = self._prepare_whitelist(self._whitelist.copy(), tickers)
# Generate the pairlist with first Pairlist Handler in the chain
pairlist = self._pairlist_handlers[0].gen_pairlist(self._whitelist, tickers)
pairlist = self._pairlist_handlers[0].gen_pairlist(tickers)
# Process all Pairlist Handlers in the chain
for pairlist_handler in self._pairlist_handlers:
@@ -95,19 +92,6 @@ class PairListManager():
self._whitelist = pairlist
def _prepare_whitelist(self, pairlist: List[str], tickers: Dict[str, Any]) -> List[str]:
"""
Prepare sanitized pairlist for Pairlist Handlers that use tickers data - remove
pairs that do not have ticker available
"""
if self._tickers_needed:
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
if p not in tickers:
pairlist.remove(p)
return pairlist
def verify_blacklist(self, pairlist: List[str], logmethod) -> List[str]:
"""
Verify and remove items from pairlist - returning a filtered pairlist.

4
freqtrade/plugins/protections/cooldown_period.py
View File

@@ -1,7 +1,6 @@
import logging
from datetime import datetime, timedelta
from typing import Any, Dict
from freqtrade.persistence import Trade
from freqtrade.plugins.protections import IProtection, ProtectionReturn
@@ -15,9 +14,6 @@ class CooldownPeriod(IProtection):
has_global_stop: bool = False
has_local_stop: bool = True
def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
super().__init__(config, protection_config)
def _reason(self) -> str:
"""
LockReason to use

2
freqtrade/plugins/protections/max_drawdown_protection.py
View File

@@ -61,7 +61,7 @@ class MaxDrawdown(IProtection):
if drawdown > self._max_allowed_drawdown:
self.log_once(
f"Trading stopped due to Max Drawdown {drawdown:.2f} < {self._max_allowed_drawdown}"
f"Trading stopped due to Max Drawdown {drawdown:.2f} > {self._max_allowed_drawdown}"
f" within {self.lookback_period_str}.", logger.info)
until = self.calculate_lock_end(trades, self._stop_duration)

2
freqtrade/resolvers/iresolver.py
View File

@@ -61,7 +61,7 @@ class IResolver:
module = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
except (ModuleNotFoundError, SyntaxError, ImportError) as err:
except (ModuleNotFoundError, SyntaxError, ImportError, NameError) as err:
# Catch errors in case a specific module is not installed
logger.warning(f"Could not import {module_path} due to '{err}'")
if enum_failed:

6
freqtrade/resolvers/strategy_resolver.py
View File

@@ -196,9 +196,9 @@ class StrategyResolver(IResolver):
strategy._populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
strategy._sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
if any([x == 2 for x in [strategy._populate_fun_len,
strategy._buy_fun_len,
strategy._sell_fun_len]]):
if any(x == 2 for x in [strategy._populate_fun_len,
strategy._buy_fun_len,
strategy._sell_fun_len]):
strategy.INTERFACE_VERSION = 1
return strategy

8
freqtrade/rpc/api_server/api_schemas.py
View File

@@ -57,6 +57,7 @@ class Count(BaseModel):
class PerformanceEntry(BaseModel):
pair: str
profit: float
profit_abs: float
count: int
@@ -151,13 +152,11 @@ class TradeSchema(BaseModel):
fee_close: Optional[float]
fee_close_cost: Optional[float]
fee_close_currency: Optional[str]
open_date_hum: str
open_date: str
open_timestamp: int
open_rate: float
open_rate_requested: Optional[float]
open_trade_value: float
close_date_hum: Optional[str]
close_date: Optional[str]
close_timestamp: Optional[int]
close_rate: Optional[float]
@@ -168,6 +167,7 @@ class TradeSchema(BaseModel):
profit_ratio: Optional[float]
profit_pct: Optional[float]
profit_abs: Optional[float]
profit_fiat: Optional[float]
sell_reason: Optional[str]
sell_order_status: Optional[str]
stop_loss_abs: Optional[float]
@@ -190,7 +190,6 @@ class OpenTradeSchema(TradeSchema):
stoploss_current_dist_ratio: Optional[float]
stoploss_entry_dist: Optional[float]
stoploss_entry_dist_ratio: Optional[float]
base_currency: str
current_profit: float
current_profit_abs: float
current_profit_pct: float
@@ -201,6 +200,7 @@ class OpenTradeSchema(TradeSchema):
class TradeResponse(BaseModel):
trades: List[TradeSchema]
trades_count: int
total_trades: int
class ForceBuyResponse(BaseModel):
@@ -269,7 +269,7 @@ class DeleteTrade(BaseModel):
class PlotConfig_(BaseModel):
main_plot: Dict[str, Any]
subplots: Optional[Dict[str, Any]]
subplots: Dict[str, Any]
class PlotConfig(BaseModel):

19
freqtrade/rpc/api_server/api_v1.py
View File

@@ -17,8 +17,7 @@ from freqtrade.rpc.api_server.api_schemas import (AvailablePairs, Balances, Blac
OpenTradeSchema, PairHistory, PerformanceEntry,
Ping, PlotConfig, Profit, ResultMsg, ShowConfig,
Stats, StatusMsg, StrategyListResponse,
StrategyResponse, TradeResponse, Version,
WhitelistResponse)
StrategyResponse, Version, WhitelistResponse)
from freqtrade.rpc.api_server.deps import get_config, get_rpc, get_rpc_optional
from freqtrade.rpc.rpc import RPCException
@@ -83,9 +82,19 @@ def status(rpc: RPC = Depends(get_rpc)):
return []
@router.get('/trades', response_model=TradeResponse, tags=['info', 'trading'])
def trades(limit: int = 0, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_trade_history(limit)
# Using the responsemodel here will cause a ~100% increase in response time (from 1s to 2s)
# on big databases. Correct response model: response_model=TradeResponse,
@router.get('/trades', tags=['info', 'trading'])
def trades(limit: int = 500, offset: int = 0, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_trade_history(limit, offset=offset, order_by_id=True)
@router.get('/trade/{tradeid}', response_model=OpenTradeSchema, tags=['info', 'trading'])
def trade(tradeid: int = 0, rpc: RPC = Depends(get_rpc)):
try:
return rpc._rpc_trade_status([tradeid])[0]
except (RPCException, KeyError):
raise HTTPException(status_code=404, detail='Trade not found.')
@router.delete('/trades/{tradeid}', response_model=DeleteTrade, tags=['info', 'trading'])

5
freqtrade/rpc/api_server/web_ui.py
View File

@@ -13,6 +13,11 @@ async def favicon():
return FileResponse(str(Path(__file__).parent / 'ui/favicon.ico'))
@router_ui.get('/fallback_file.html', include_in_schema=False)
async def fallback():
return FileResponse(str(Path(__file__).parent / 'ui/fallback_file.html'))
@router_ui.get('/{rest_of_path:path}', include_in_schema=False)
async def index_html(rest_of_path: str):
"""

130
freqtrade/rpc/fiat_convert.py
View File

@@ -3,11 +3,13 @@ Module that define classes to convert Crypto-currency to FIAT
e.g BTC to USD
"""
import datetime
import logging
import time
from typing import Dict, List
from typing import Dict
from cachetools.ttl import TTLCache
from pycoingecko import CoinGeckoAPI
from requests.exceptions import RequestException
from freqtrade.constants import SUPPORTED_FIAT
@@ -15,51 +17,6 @@ from freqtrade.constants import SUPPORTED_FIAT
logger = logging.getLogger(__name__)
class CryptoFiat:
"""
Object to describe what is the price of Crypto-currency in a FIAT
"""
# Constants
CACHE_DURATION = 6 * 60 * 60 # 6 hours
def __init__(self, crypto_symbol: str, fiat_symbol: str, price: float) -> None:
"""
Create an object that will contains the price for a crypto-currency in fiat
:param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
:param fiat_symbol: FIAT currency you want to convert to (e.g USD)
:param price: Price in FIAT
"""
# Public attributes
self.crypto_symbol = None
self.fiat_symbol = None
self.price = 0.0
# Private attributes
self._expiration = 0.0
self.crypto_symbol = crypto_symbol.lower()
self.fiat_symbol = fiat_symbol.lower()
self.set_price(price=price)
def set_price(self, price: float) -> None:
"""
Set the price of the Crypto-currency in FIAT and set the expiration time
:param price: Price of the current Crypto currency in the fiat
:return: None
"""
self.price = price
self._expiration = time.time() + self.CACHE_DURATION
def is_expired(self) -> bool:
"""
Return if the current price is still valid or needs to be refreshed
:return: bool, true the price is expired and needs to be refreshed, false the price is
still valid
"""
return self._expiration - time.time() <= 0
class CryptoToFiatConverter:
"""
Main class to initiate Crypto to FIAT.
@@ -70,6 +27,7 @@ class CryptoToFiatConverter:
_coingekko: CoinGeckoAPI = None
_cryptomap: Dict = {}
_backoff: float = 0.0
def __new__(cls):
"""
@@ -84,14 +42,29 @@ class CryptoToFiatConverter:
return CryptoToFiatConverter.__instance
def __init__(self) -> None:
self._pairs: List[CryptoFiat] = []
# Timeout: 6h
self._pair_price: TTLCache = TTLCache(maxsize=500, ttl=6 * 60 * 60)
self._load_cryptomap()
def _load_cryptomap(self) -> None:
try:
coinlistings = self._coingekko.get_coins_list()
# Create mapping table from synbol to coingekko_id
# Create mapping table from symbol to coingekko_id
self._cryptomap = {x['symbol']: x['id'] for x in coinlistings}
except RequestException as request_exception:
if "429" in str(request_exception):
logger.warning(
"Too many requests for Coingecko API, backing off and trying again later.")
# Set backoff timestamp to 60 seconds in the future
self._backoff = datetime.datetime.now().timestamp() + 60
return
# If the request is not a 429 error we want to raise the normal error
logger.error(
"Could not load FIAT Cryptocurrency map for the following problem: {}".format(
request_exception
)
)
except (Exception) as exception:
logger.error(
f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
@@ -118,49 +91,31 @@ class CryptoToFiatConverter:
"""
crypto_symbol = crypto_symbol.lower()
fiat_symbol = fiat_symbol.lower()
inverse = False
if crypto_symbol == 'usd':
# usd corresponds to "uniswap-state-dollar" for coingecko.
# We'll therefore need to "swap" the currencies
logger.info(f"reversing Rates {crypto_symbol}, {fiat_symbol}")
crypto_symbol = fiat_symbol
fiat_symbol = 'usd'
inverse = True
symbol = f"{crypto_symbol}/{fiat_symbol}"
# Check if the fiat convertion you want is supported
if not self._is_supported_fiat(fiat=fiat_symbol):
raise ValueError(f'The fiat {fiat_symbol} is not supported.')
# Get the pair that interest us and return the price in fiat
for pair in self._pairs:
if pair.crypto_symbol == crypto_symbol and pair.fiat_symbol == fiat_symbol:
# If the price is expired we refresh it, avoid to call the API all the time
if pair.is_expired():
pair.set_price(
price=self._find_price(
crypto_symbol=pair.crypto_symbol,
fiat_symbol=pair.fiat_symbol
)
)
price = self._pair_price.get(symbol, None)
# return the last price we have for this pair
return pair.price
# The pair does not exist, so we create it and return the price
return self._add_pair(
crypto_symbol=crypto_symbol,
fiat_symbol=fiat_symbol,
price=self._find_price(
if not price:
price = self._find_price(
crypto_symbol=crypto_symbol,
fiat_symbol=fiat_symbol
)
)
def _add_pair(self, crypto_symbol: str, fiat_symbol: str, price: float) -> float:
"""
:param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
:param fiat_symbol: FIAT currency you want to convert to (e.g USD)
:return: price in FIAT
"""
self._pairs.append(
CryptoFiat(
crypto_symbol=crypto_symbol,
fiat_symbol=fiat_symbol,
price=price
)
)
if inverse and price != 0.0:
price = 1 / price
self._pair_price[symbol] = price
return price
@@ -188,6 +143,15 @@ class CryptoToFiatConverter:
if crypto_symbol == fiat_symbol:
return 1.0
if self._cryptomap == {}:
if self._backoff <= datetime.datetime.now().timestamp():
self._load_cryptomap()
# return 0.0 if we still dont have data to check, no reason to proceed
if self._cryptomap == {}:
return 0.0
else:
return 0.0
if crypto_symbol not in self._cryptomap:
# return 0 for unsupported stake currencies (fiat-convert should not break the bot)
logger.warning("unsupported crypto-symbol %s - returning 0.0", crypto_symbol)

74
freqtrade/rpc/rpc.py
View File

@@ -24,20 +24,22 @@ from freqtrade.persistence.models import PairLock
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
from freqtrade.state import State
from freqtrade.strategy.interface import SellType
from freqtrade.strategy.interface import SellCheckTuple, SellType
logger = logging.getLogger(__name__)
class RPCMessageType(Enum):
STATUS_NOTIFICATION = 'status'
WARNING_NOTIFICATION = 'warning'
STARTUP_NOTIFICATION = 'startup'
BUY_NOTIFICATION = 'buy'
BUY_CANCEL_NOTIFICATION = 'buy_cancel'
SELL_NOTIFICATION = 'sell'
SELL_CANCEL_NOTIFICATION = 'sell_cancel'
STATUS = 'status'
WARNING = 'warning'
STARTUP = 'startup'
BUY = 'buy'
BUY_FILL = 'buy_fill'
BUY_CANCEL = 'buy_cancel'
SELL = 'sell'
SELL_FILL = 'sell_fill'
SELL_CANCEL = 'sell_cancel'
def __repr__(self):
return self.value
@@ -167,12 +169,24 @@ class RPC:
if trade.open_order_id:
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
# calculate profit and send message to user
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except (ExchangeError, PricingError):
current_rate = NAN
if trade.is_open:
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except (ExchangeError, PricingError):
current_rate = NAN
else:
current_rate = trade.close_rate
current_profit = trade.calc_profit_ratio(current_rate)
current_profit_abs = trade.calc_profit(current_rate)
current_profit_fiat: Optional[float] = None
# Calculate fiat profit
if self._fiat_converter:
current_profit_fiat = self._fiat_converter.convert_amount(
current_profit_abs,
self._freqtrade.config['stake_currency'],
self._freqtrade.config['fiat_display_currency']
)
# Calculate guaranteed profit (in case of trailing stop)
stoploss_entry_dist = trade.calc_profit(trade.stop_loss)
stoploss_entry_dist_ratio = trade.calc_profit_ratio(trade.stop_loss)
@@ -191,6 +205,7 @@ class RPC:
profit_ratio=current_profit,
profit_pct=round(current_profit * 100, 2),
profit_abs=current_profit_abs,
profit_fiat=current_profit_fiat,
stoploss_current_dist=stoploss_current_dist,
stoploss_current_dist_ratio=round(stoploss_current_dist_ratio, 8),
@@ -205,12 +220,13 @@ class RPC:
return results
def _rpc_status_table(self, stake_currency: str,
fiat_display_currency: str) -> Tuple[List, List]:
fiat_display_currency: str) -> Tuple[List, List, float]:
trades = Trade.get_open_trades()
if not trades:
raise RPCException('no active trade')
else:
trades_list = []
fiat_profit_sum = NAN
for trade in trades:
# calculate profit and send message to user
try:
@@ -228,6 +244,8 @@ class RPC:
)
if fiat_profit and not isnan(fiat_profit):
profit_str += f" ({fiat_profit:.2f})"
fiat_profit_sum = fiat_profit if isnan(fiat_profit_sum) \
else fiat_profit_sum + fiat_profit
trades_list.append([
trade.id,
trade.pair + ('*' if (trade.open_order_id is not None
@@ -241,7 +259,7 @@ class RPC:
profitcol += " (" + fiat_display_currency + ")"
columns = ['ID', 'Pair', 'Since', profitcol]
return trades_list, columns
return trades_list, columns, fiat_profit_sum
def _rpc_daily_profit(
self, timescale: int,
@@ -285,11 +303,12 @@ class RPC:
'data': data
}
def _rpc_trade_history(self, limit: int) -> Dict:
def _rpc_trade_history(self, limit: int, offset: int = 0, order_by_id: bool = False) -> Dict:
""" Returns the X last trades """
if limit > 0:
order_by = Trade.id if order_by_id else Trade.close_date.desc()
if limit:
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(
Trade.close_date.desc()).limit(limit)
order_by).limit(limit).offset(offset)
else:
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(
Trade.close_date.desc()).all()
@@ -298,7 +317,8 @@ class RPC:
return {
"trades": output,
"trades_count": len(output)
"trades_count": len(output),
"total_trades": Trade.get_trades([Trade.is_open.is_(False)]).count(),
}
def _rpc_stats(self) -> Dict[str, Any]:
@@ -432,7 +452,7 @@ class RPC:
output = []
total = 0.0
try:
tickers = self._freqtrade.exchange.get_tickers()
tickers = self._freqtrade.exchange.get_tickers(cached=True)
except (ExchangeError):
raise RPCException('Error getting current tickers.')
@@ -537,7 +557,8 @@ class RPC:
if not fully_canceled:
# Get current rate and execute sell
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
self._freqtrade.execute_sell(trade, current_rate, SellType.FORCE_SELL)
sell_reason = SellCheckTuple(sell_type=SellType.FORCE_SELL)
self._freqtrade.execute_sell(trade, current_rate, sell_reason)
# ---- EOF def _exec_forcesell ----
if self._freqtrade.state != State.RUNNING:
@@ -548,7 +569,7 @@ class RPC:
# Execute sell for all open orders
for trade in Trade.get_open_trades():
_exec_forcesell(trade)
Trade.session.flush()
Trade.query.session.flush()
self._freqtrade.wallets.update()
return {'result': 'Created sell orders for all open trades.'}
@@ -561,7 +582,7 @@ class RPC:
raise RPCException('invalid argument')
_exec_forcesell(trade)
Trade.session.flush()
Trade.query.session.flush()
self._freqtrade.wallets.update()
return {'result': f'Created sell order for trade {trade_id}.'}
@@ -590,8 +611,7 @@ class RPC:
raise RPCException(f'position for {pair} already open - id: {trade.id}')
# gen stake amount
stakeamount = self._freqtrade.wallets.get_trade_stake_amount(
pair, self._freqtrade.get_free_open_trades())
stakeamount = self._freqtrade.wallets.get_trade_stake_amount(pair)
# execute buy
if self._freqtrade.execute_buy(pair, stakeamount, price, forcebuy=True):
@@ -686,7 +706,7 @@ class RPC:
lock.lock_end_time = datetime.now(timezone.utc)
# session is always the same
PairLock.session.flush()
PairLock.query.session.flush()
return self._rpc_locks()
@@ -828,5 +848,7 @@ class RPC:
df_analyzed, arrow.Arrow.utcnow().datetime)
def _rpc_plot_config(self) -> Dict[str, Any]:
if (self._freqtrade.strategy.plot_config and
'subplots' not in self._freqtrade.strategy.plot_config):
self._freqtrade.strategy.plot_config['subplots'] = {}
return self._freqtrade.strategy.plot_config

8
freqtrade/rpc/rpc_manager.py
View File

@@ -67,7 +67,7 @@ class RPCManager:
def startup_messages(self, config: Dict[str, Any], pairlist, protections) -> None:
if config['dry_run']:
self.send_msg({
'type': RPCMessageType.WARNING_NOTIFICATION,
'type': RPCMessageType.WARNING,
'status': 'Dry run is enabled. All trades are simulated.'
})
stake_currency = config['stake_currency']
@@ -79,7 +79,7 @@ class RPCManager:
exchange_name = config['exchange']['name']
strategy_name = config.get('strategy', '')
self.send_msg({
'type': RPCMessageType.STARTUP_NOTIFICATION,
'type': RPCMessageType.STARTUP,
'status': f'*Exchange:* `{exchange_name}`\n'
f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
f'*Minimum ROI:* `{minimal_roi}`\n'
@@ -88,13 +88,13 @@ class RPCManager:
f'*Strategy:* `{strategy_name}`'
})
self.send_msg({
'type': RPCMessageType.STARTUP_NOTIFICATION,
'type': RPCMessageType.STARTUP,
'status': f'Searching for {stake_currency} pairs to buy and sell '
f'based on {pairlist.short_desc()}'
})
if len(protections.name_list) > 0:
prots = '\n'.join([p for prot in protections.short_desc() for k, p in prot.items()])
self.send_msg({
'type': RPCMessageType.STARTUP_NOTIFICATION,
'type': RPCMessageType.STARTUP,
'status': f'Using Protections: \n{prots}'
})

206
freqtrade/rpc/telegram.py
View File

@@ -8,6 +8,7 @@ import logging
from datetime import timedelta
from html import escape
from itertools import chain
from math import isnan
from typing import Any, Callable, Dict, List, Optional, Union, cast
import arrow
@@ -21,7 +22,7 @@ from telegram.utils.helpers import escape_markdown
from freqtrade.__init__ import __version__
from freqtrade.constants import DUST_PER_COIN
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_coin_value
from freqtrade.misc import chunks, round_coin_value
from freqtrade.rpc import RPC, RPCException, RPCHandler, RPCMessageType
@@ -160,10 +161,10 @@ class Telegram(RPCHandler):
for handle in handles:
self._updater.dispatcher.add_handler(handle)
self._updater.start_polling(
clean=True,
bootstrap_retries=-1,
timeout=30,
read_latency=60,
drop_pending_updates=True,
)
logger.info(
'rpc.telegram is listening for following commands: %s',
@@ -182,6 +183,53 @@ class Telegram(RPCHandler):
"""
self._updater.stop()
def _format_buy_msg(self, msg: Dict[str, Any]) -> str:
if self._rpc._fiat_converter:
msg['stake_amount_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['stake_amount'], msg['stake_currency'], msg['fiat_currency'])
else:
msg['stake_amount_fiat'] = 0
message = (f"\N{LARGE BLUE CIRCLE} *{msg['exchange']}:* Buying {msg['pair']}"
f" (#{msg['trade_id']})\n"
f"*Amount:* `{msg['amount']:.8f}`\n"
f"*Open Rate:* `{msg['limit']:.8f}`\n"
f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
f"*Total:* `({round_coin_value(msg['stake_amount'], msg['stake_currency'])}")
if msg.get('fiat_currency', None):
message += f", {round_coin_value(msg['stake_amount_fiat'], msg['fiat_currency'])}"
message += ")`"
return message
def _format_sell_msg(self, msg: Dict[str, Any]) -> str:
msg['amount'] = round(msg['amount'], 8)
msg['profit_percent'] = round(msg['profit_ratio'] * 100, 2)
msg['duration'] = msg['close_date'].replace(
microsecond=0) - msg['open_date'].replace(microsecond=0)
msg['duration_min'] = msg['duration'].total_seconds() / 60
msg['emoji'] = self._get_sell_emoji(msg)
message = ("{emoji} *{exchange}:* Selling {pair} (#{trade_id})\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Close Rate:* `{limit:.8f}`\n"
"*Sell Reason:* `{sell_reason}`\n"
"*Duration:* `{duration} ({duration_min:.1f} min)`\n"
"*Profit:* `{profit_percent:.2f}%`").format(**msg)
# Check if all sell properties are available.
# This might not be the case if the message origin is triggered by /forcesell
if (all(prop in msg for prop in ['gain', 'fiat_currency', 'stake_currency'])
and self._rpc._fiat_converter):
msg['profit_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['profit_amount'], msg['stake_currency'], msg['fiat_currency'])
message += (' `({gain}: {profit_amount:.8f} {stake_currency}'
' / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
return message
def send_msg(self, msg: Dict[str, Any]) -> None:
""" Send a message to telegram channel """
@@ -192,67 +240,33 @@ class Telegram(RPCHandler):
# Notification disabled
return
if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
if self._rpc._fiat_converter:
msg['stake_amount_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['stake_amount'], msg['stake_currency'], msg['fiat_currency'])
else:
msg['stake_amount_fiat'] = 0
if msg['type'] == RPCMessageType.BUY:
message = self._format_buy_msg(msg)
message = (f"\N{LARGE BLUE CIRCLE} *{msg['exchange']}:* Buying {msg['pair']}"
f" (#{msg['trade_id']})\n"
f"*Amount:* `{msg['amount']:.8f}`\n"
f"*Open Rate:* `{msg['limit']:.8f}`\n"
f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
f"*Total:* `({round_coin_value(msg['stake_amount'], msg['stake_currency'])}")
if msg.get('fiat_currency', None):
message += f", {round_coin_value(msg['stake_amount_fiat'], msg['fiat_currency'])}"
message += ")`"
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
elif msg['type'] in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
msg['message_side'] = 'buy' if msg['type'] == RPCMessageType.BUY_CANCEL else 'sell'
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling open buy Order for {pair} (#{trade_id}). "
"Cancelling open {message_side} Order for {pair} (#{trade_id}). "
"Reason: {reason}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
msg['amount'] = round(msg['amount'], 8)
msg['profit_percent'] = round(msg['profit_ratio'] * 100, 2)
msg['duration'] = msg['close_date'].replace(
microsecond=0) - msg['open_date'].replace(microsecond=0)
msg['duration_min'] = msg['duration'].total_seconds() / 60
elif msg['type'] == RPCMessageType.BUY_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Buy order for {pair} (#{trade_id}) filled "
"for {open_rate}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Sell order for {pair} (#{trade_id}) filled "
"for {close_rate}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL:
message = self._format_sell_msg(msg)
msg['emoji'] = self._get_sell_emoji(msg)
message = ("{emoji} *{exchange}:* Selling {pair} (#{trade_id})\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Close Rate:* `{limit:.8f}`\n"
"*Sell Reason:* `{sell_reason}`\n"
"*Duration:* `{duration} ({duration_min:.1f} min)`\n"
"*Profit:* `{profit_percent:.2f}%`").format(**msg)
# Check if all sell properties are available.
# This might not be the case if the message origin is triggered by /forcesell
if (all(prop in msg for prop in ['gain', 'fiat_currency', 'stake_currency'])
and self._rpc._fiat_converter):
msg['profit_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['profit_amount'], msg['stake_currency'], msg['fiat_currency'])
message += (' `({gain}: {profit_amount:.8f} {stake_currency}'
' / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
message = ("\N{WARNING SIGN} *{exchange}:* Cancelling Open Sell Order "
"for {pair} (#{trade_id}). Reason: {reason}").format(**msg)
elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
elif msg['type'] == RPCMessageType.STATUS:
message = '*Status:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.WARNING_NOTIFICATION:
elif msg['type'] == RPCMessageType.WARNING:
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.STARTUP_NOTIFICATION:
elif msg['type'] == RPCMessageType.STARTUP:
message = '{status}'.format(**msg)
else:
@@ -300,6 +314,7 @@ class Telegram(RPCHandler):
messages = []
for r in results:
r['open_date_hum'] = arrow.get(r['open_date']).humanize()
lines = [
"*Trade ID:* `{trade_id}` `(since {open_date_hum})`",
"*Current Pair:* {pair}",
@@ -346,19 +361,31 @@ class Telegram(RPCHandler):
:return: None
"""
try:
statlist, head = self._rpc._rpc_status_table(
self._config['stake_currency'], self._config.get('fiat_display_currency', ''))
fiat_currency = self._config.get('fiat_display_currency', '')
statlist, head, fiat_profit_sum = self._rpc._rpc_status_table(
self._config['stake_currency'], fiat_currency)
show_total = not isnan(fiat_profit_sum) and len(statlist) > 1
max_trades_per_msg = 50
"""
Calculate the number of messages of 50 trades per message
0.99 is used to make sure that there are no extra (empty) messages
As an example with 50 trades, there will be int(50/50 + 0.99) = 1 message
"""
for i in range(0, max(int(len(statlist) / max_trades_per_msg + 0.99), 1)):
message = tabulate(statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg],
messages_count = max(int(len(statlist) / max_trades_per_msg + 0.99), 1)
for i in range(0, messages_count):
trades = statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg]
if show_total and i == messages_count - 1:
# append total line
trades.append(["Total", "", "", f"{fiat_profit_sum:.2f} {fiat_currency}"])
message = tabulate(trades,
headers=head,
tablefmt='simple')
if show_total and i == messages_count - 1:
# insert separators line between Total
lines = message.split("\n")
message = "\n".join(lines[:-1] + [lines[1]] + [lines[-1]])
self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@@ -723,14 +750,21 @@ class Telegram(RPCHandler):
"""
try:
trades = self._rpc._rpc_performance()
stats = '\n'.join('{index}.\t<code>{pair}\t{profit:.2f}% ({count})</code>'.format(
index=i + 1,
pair=trade['pair'],
profit=trade['profit'],
count=trade['count']
) for i, trade in enumerate(trades))
message = '<b>Performance:</b>\n{}'.format(stats)
self._send_msg(message, parse_mode=ParseMode.HTML)
output = "<b>Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['pair']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit']:.2f}%) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
self._send_msg(output, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@@ -760,17 +794,21 @@ class Telegram(RPCHandler):
Handler for /locks.
Returns the currently active locks
"""
locks = self._rpc._rpc_locks()
message = tabulate([[
lock['id'],
lock['pair'],
lock['lock_end_time'],
lock['reason']] for lock in locks['locks']],
headers=['ID', 'Pair', 'Until', 'Reason'],
tablefmt='simple')
message = f"<pre>{escape(message)}</pre>"
logger.debug(message)
self._send_msg(message, parse_mode=ParseMode.HTML)
rpc_locks = self._rpc._rpc_locks()
if not rpc_locks['locks']:
self._send_msg('No active locks.', parse_mode=ParseMode.HTML)
for locks in chunks(rpc_locks['locks'], 25):
message = tabulate([[
lock['id'],
lock['pair'],
lock['lock_end_time'],
lock['reason']] for lock in locks],
headers=['ID', 'Pair', 'Until', 'Reason'],
tablefmt='simple')
message = f"<pre>{escape(message)}</pre>"
logger.debug(message)
self._send_msg(message, parse_mode=ParseMode.HTML)
@authorized_only
def _delete_locks(self, update: Update, context: CallbackContext) -> None:
@@ -870,9 +908,17 @@ class Telegram(RPCHandler):
"""
try:
edge_pairs = self._rpc._rpc_edge()
edge_pairs_tab = tabulate(edge_pairs, headers='keys', tablefmt='simple')
message = f'<b>Edge only validated following pairs:</b>\n<pre>{edge_pairs_tab}</pre>'
self._send_msg(message, parse_mode=ParseMode.HTML)
if not edge_pairs:
message = '<b>Edge only validated following pairs:</b>'
self._send_msg(message, parse_mode=ParseMode.HTML)
for chunk in chunks(edge_pairs, 25):
edge_pairs_tab = tabulate(chunk, headers='keys', tablefmt='simple')
message = (f'<b>Edge only validated following pairs:</b>\n'
f'<pre>{edge_pairs_tab}</pre>')
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))

18
freqtrade/rpc/webhook.py
View File

@@ -45,17 +45,21 @@ class Webhook(RPCHandler):
""" Send a message to telegram channel """
try:
if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
if msg['type'] == RPCMessageType.BUY:
valuedict = self._config['webhook'].get('webhookbuy', None)
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
elif msg['type'] == RPCMessageType.BUY_CANCEL:
valuedict = self._config['webhook'].get('webhookbuycancel', None)
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
elif msg['type'] == RPCMessageType.BUY_FILL:
valuedict = self._config['webhook'].get('webhookbuyfill', None)
elif msg['type'] == RPCMessageType.SELL:
valuedict = self._config['webhook'].get('webhooksell', None)
elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
elif msg['type'] == RPCMessageType.SELL_FILL:
valuedict = self._config['webhook'].get('webhooksellfill', None)
elif msg['type'] == RPCMessageType.SELL_CANCEL:
valuedict = self._config['webhook'].get('webhooksellcancel', None)
elif msg['type'] in (RPCMessageType.STATUS_NOTIFICATION,
RPCMessageType.STARTUP_NOTIFICATION,
RPCMessageType.WARNING_NOTIFICATION):
elif msg['type'] in (RPCMessageType.STATUS,
RPCMessageType.STARTUP,
RPCMessageType.WARNING):
valuedict = self._config['webhook'].get('webhookstatus', None)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))

2
freqtrade/strategy/__init__.py
View File

@@ -1,5 +1,7 @@
# flake8: noqa: F401
from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.strategy.hyper import (CategoricalParameter, DecimalParameter, IntParameter,
RealParameter)
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_helper import merge_informative_pair, stoploss_from_open

335
freqtrade/strategy/hyper.py Normal file
View File

@@ -0,0 +1,335 @@
"""
IHyperStrategy interface, hyperoptable Parameter class.
This module defines a base class for auto-hyperoptable strategies.
"""
import logging
from abc import ABC, abstractmethod
from contextlib import suppress
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
from freqtrade.optimize.hyperopt_tools import HyperoptTools
with suppress(ImportError):
from skopt.space import Integer, Real, Categorical
from freqtrade.optimize.space import SKDecimal
from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
class BaseParameter(ABC):
"""
Defines a parameter that can be optimized by hyperopt.
"""
category: Optional[str]
default: Any
value: Any
in_space: bool = False
name: str
def __init__(self, *, default: Any, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable parameter.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.(Integer|Real|Categorical).
"""
if 'name' in kwargs:
raise OperationalException(
'Name is determined by parameter field name and can not be specified manually.')
self.category = space
self._space_params = kwargs
self.value = default
self.optimize = optimize
self.load = load
def __repr__(self):
return f'{self.__class__.__name__}({self.value})'
@abstractmethod
def get_space(self, name: str) -> Union['Integer', 'Real', 'SKDecimal', 'Categorical']:
"""
Get-space - will be used by Hyperopt to get the hyperopt Space
"""
class NumericParameter(BaseParameter):
""" Internal parameter used for Numeric purposes """
float_or_int = Union[int, float]
default: float_or_int
value: float_or_int
def __init__(self, low: Union[float_or_int, Sequence[float_or_int]],
high: Optional[float_or_int] = None, *, default: float_or_int,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable numeric parameter.
Cannot be instantiated, but provides the validation for other numeric parameters
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none of entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.*.
"""
if high is not None and isinstance(low, Sequence):
raise OperationalException(f'{self.__class__.__name__} space invalid.')
if high is None or isinstance(low, Sequence):
if not isinstance(low, Sequence) or len(low) != 2:
raise OperationalException(f'{self.__class__.__name__} space must be [low, high]')
self.low, self.high = low
else:
self.low = low
self.high = high
super().__init__(default=default, space=space, optimize=optimize,
load=load, **kwargs)
class IntParameter(NumericParameter):
default: int
value: int
def __init__(self, low: Union[int, Sequence[int]], high: Optional[int] = None, *, default: int,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable integer parameter.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none of entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Integer.
"""
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Integer':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Integer(low=self.low, high=self.high, name=name, **self._space_params)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List from low to high (inclusive) in Hyperopt mode.
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
# Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
return range(self.low, self.high + 1)
else:
return range(self.value, self.value + 1)
class RealParameter(NumericParameter):
default: float
value: float
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
default: float, space: Optional[str] = None, optimize: bool = True,
load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable floating point parameter with unlimited precision.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none if entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Real.
"""
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Real':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Real(low=self.low, high=self.high, name=name, **self._space_params)
class DecimalParameter(NumericParameter):
default: float
value: float
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
default: float, decimals: int = 3, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable decimal parameter with a limited precision.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none if entire range is passed first parameter.
:param default: A default value.
:param decimals: A number of decimals after floating point to be included in testing.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Integer.
"""
self._decimals = decimals
default = round(default, self._decimals)
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'SKDecimal':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return SKDecimal(low=self.low, high=self.high, decimals=self._decimals, name=name,
**self._space_params)
class CategoricalParameter(BaseParameter):
default: Any
value: Any
opt_range: Sequence[Any]
def __init__(self, categories: Sequence[Any], *, default: Optional[Any] = None,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable parameter.
:param categories: Optimization space, [a, b, ...].
:param default: A default value. If not specified, first item from specified space will be
used.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Categorical.
"""
if len(categories) < 2:
raise OperationalException(
'CategoricalParameter space must be [a, b, ...] (at least two parameters)')
self.opt_range = categories
super().__init__(default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Categorical':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Categorical(self.opt_range, name=name, **self._space_params)
class HyperStrategyMixin(object):
"""
A helper base class which allows HyperOptAuto class to reuse implementations of of buy/sell
strategy logic.
"""
def __init__(self, config: Dict[str, Any], *args, **kwargs):
"""
Initialize hyperoptable strategy mixin.
"""
self.config = config
self.ft_buy_params: List[BaseParameter] = []
self.ft_sell_params: List[BaseParameter] = []
self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
"""
Find all optimizeable parameters and return (name, attr) iterator.
:param category:
:return:
"""
if category not in ('buy', 'sell', None):
raise OperationalException('Category must be one of: "buy", "sell", None.')
if category is None:
params = self.ft_buy_params + self.ft_sell_params
else:
params = getattr(self, f"ft_{category}_params")
for par in params:
yield par.name, par
def _detect_parameters(self, category: str) -> Iterator[Tuple[str, BaseParameter]]:
""" Detect all parameters for 'category' """
for attr_name in dir(self):
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
attr = getattr(self, attr_name)
if issubclass(attr.__class__, BaseParameter):
if (attr_name.startswith(category + '_')
and attr.category is not None and attr.category != category):
raise OperationalException(
f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
if (category == attr.category or
(attr_name.startswith(category + '_') and attr.category is None)):
yield attr_name, attr
def _load_hyper_params(self, hyperopt: bool = False) -> None:
"""
Load Hyperoptable parameters
"""
self._load_params(getattr(self, 'buy_params', None), 'buy', hyperopt)
self._load_params(getattr(self, 'sell_params', None), 'sell', hyperopt)
def _load_params(self, params: dict, space: str, hyperopt: bool = False) -> None:
"""
Set optimizeable parameter values.
:param params: Dictionary with new parameter values.
"""
if not params:
logger.info(f"No params for {space} found, using default values.")
param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
for attr_name, attr in self._detect_parameters(space):
attr.name = attr_name
attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
if not attr.category:
attr.category = space
param_container.append(attr)
if params and attr_name in params:
if attr.load:
attr.value = params[attr_name]
logger.info(f'Strategy Parameter: {attr_name} = {attr.value}')
else:
logger.warning(f'Parameter "{attr_name}" exists, but is disabled. '
f'Default value "{attr.value}" used.')
else:
logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}')
def get_params_dict(self):
"""
Returns list of Parameters that are not part of the current optimize job
"""
params = {
'buy': {},
'sell': {}
}
for name, p in self.enumerate_parameters():
if not p.optimize or not p.in_space:
params[p.category][name] = p.value
return params

106
freqtrade/strategy/interface.py
View File

@@ -7,7 +7,7 @@ import warnings
from abc import ABC, abstractmethod
from datetime import datetime, timedelta, timezone
from enum import Enum
from typing import Dict, List, NamedTuple, Optional, Tuple
from typing import Dict, List, Optional, Tuple, Union
import arrow
from pandas import DataFrame
@@ -18,11 +18,13 @@ from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.persistence import PairLocks, Trade
from freqtrade.strategy.hyper import HyperStrategyMixin
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
CUSTOM_SELL_MAX_LENGTH = 64
class SignalType(Enum):
@@ -44,6 +46,7 @@ class SellType(Enum):
SELL_SIGNAL = "sell_signal"
FORCE_SELL = "force_sell"
EMERGENCY_SELL = "emergency_sell"
CUSTOM_SELL = "custom_sell"
NONE = ""
def __str__(self):
@@ -51,15 +54,23 @@ class SellType(Enum):
return self.value
class SellCheckTuple(NamedTuple):
class SellCheckTuple(object):
"""
NamedTuple for Sell type + reason
"""
sell_flag: bool
sell_type: SellType
sell_reason: str = ''
def __init__(self, sell_type: SellType, sell_reason: str = ''):
self.sell_type = sell_type
self.sell_reason = sell_reason or sell_type.value
@property
def sell_flag(self):
return self.sell_type != SellType.NONE
class IStrategy(ABC):
class IStrategy(ABC, HyperStrategyMixin):
"""
Interface for freqtrade strategies
Defines the mandatory structure must follow any custom strategies
@@ -140,6 +151,7 @@ class IStrategy(ABC):
self.config = config
# Dict to determine if analysis is necessary
self._last_candle_seen_per_pair: Dict[str, datetime] = {}
super().__init__(config)
@abstractmethod
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -149,6 +161,7 @@ class IStrategy(ABC):
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
return dataframe
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -158,6 +171,7 @@ class IStrategy(ABC):
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
return dataframe
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -167,6 +181,7 @@ class IStrategy(ABC):
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
return dataframe
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
"""
@@ -214,7 +229,7 @@ class IStrategy(ABC):
pass
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool:
time_in_force: str, current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -229,6 +244,7 @@ class IStrategy(ABC):
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
@@ -236,7 +252,8 @@ class IStrategy(ABC):
return True
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -255,6 +272,7 @@ class IStrategy(ABC):
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
@@ -283,6 +301,30 @@ class IStrategy(ABC):
"""
return self.stoploss
def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
"""
Custom sell signal logic indicating that specified position should be sold. Returning a
string or True from this method is equal to setting sell signal on a candle at specified
time. This method is not called when sell signal is set.
This method should be overridden to create sell signals that depend on trade parameters. For
example you could implement a stoploss relative to candle when trade was opened, or a custom
1:2 risk-reward ROI.
Custom sell reason max length is 64. Exceeding this limit will raise OperationalException.
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return: To execute sell, return a string with custom sell reason or True. Otherwise return
None or False.
"""
return None
def informative_pairs(self) -> ListPairsWithTimeframes:
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
@@ -529,12 +571,33 @@ class IStrategy(ABC):
and self.min_roi_reached(trade=trade, current_profit=current_profit,
current_time=date))
sell_signal = SellType.NONE
custom_reason = ''
# use provided rate in backtesting, not high/low.
current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate)
if (ask_strategy.get('sell_profit_only', False)
and current_profit <= ask_strategy.get('sell_profit_offset', 0)):
# sell_profit_only and profit doesn't reach the offset - ignore sell signal
sell_signal = False
else:
sell_signal = sell and not buy and ask_strategy.get('use_sell_signal', True)
pass
elif ask_strategy.get('use_sell_signal', True) and not buy:
if sell:
sell_signal = SellType.SELL_SIGNAL
else:
custom_reason = strategy_safe_wrapper(self.custom_sell, default_retval=False)(
pair=trade.pair, trade=trade, current_time=date, current_rate=current_rate,
current_profit=current_profit)
if custom_reason:
sell_signal = SellType.CUSTOM_SELL
if isinstance(custom_reason, str):
if len(custom_reason) > CUSTOM_SELL_MAX_LENGTH:
logger.warning(f'Custom sell reason returned from custom_sell is too '
f'long and was trimmed to {CUSTOM_SELL_MAX_LENGTH} '
f'characters.')
custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
else:
custom_reason = None
# TODO: return here if sell-signal should be favored over ROI
# Start evaluations
@@ -543,24 +606,23 @@ class IStrategy(ABC):
# Sell-signal
# Stoploss
if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
logger.debug(f"{trade.pair} - Required profit reached. sell_flag=True, "
f"sell_type=SellType.ROI")
return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
logger.debug(f"{trade.pair} - Required profit reached. sell_type=SellType.ROI")
return SellCheckTuple(sell_type=SellType.ROI)
if sell_signal:
logger.debug(f"{trade.pair} - Sell signal received. sell_flag=True, "
f"sell_type=SellType.SELL_SIGNAL")
return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
if sell_signal != SellType.NONE:
logger.debug(f"{trade.pair} - Sell signal received. "
f"sell_type=SellType.{sell_signal.name}" +
(f", custom_reason={custom_reason}" if custom_reason else ""))
return SellCheckTuple(sell_type=sell_signal, sell_reason=custom_reason)
if stoplossflag.sell_flag:
logger.debug(f"{trade.pair} - Stoploss hit. sell_flag=True, "
f"sell_type={stoplossflag.sell_type}")
logger.debug(f"{trade.pair} - Stoploss hit. sell_type={stoplossflag.sell_type}")
return stoplossflag
# This one is noisy, commented out...
# logger.debug(f"{trade.pair} - No sell signal. sell_flag=False")
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
# logger.debug(f"{trade.pair} - No sell signal.")
return SellCheckTuple(sell_type=SellType.NONE)
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
@@ -624,9 +686,9 @@ class IStrategy(ABC):
logger.debug(f"{trade.pair} - Trailing stop saved "
f"{trade.stop_loss - trade.initial_stop_loss:.6f}")
return SellCheckTuple(sell_flag=True, sell_type=sell_type)
return SellCheckTuple(sell_type=sell_type)
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
return SellCheckTuple(sell_type=SellType.NONE)
def min_roi_reached_entry(self, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:
"""

13
freqtrade/templates/base_config.json.j2
View File

@@ -9,7 +9,8 @@
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30
"sell": 30,
"unit": "minutes"
},
"bid_strategy": {
"price_side": "bid",
@@ -54,15 +55,15 @@
"chat_id": "{{ telegram_chat_id }}"
},
"api_server": {
"enabled": false,
"listen_ip_address": "127.0.0.1",
"enabled": {{ api_server | lower }},
"listen_ip_address": "{{ api_server_listen_addr | default("127.0.0.1", true) }}",
"listen_port": 8080,
"verbosity": "error",
"enable_openapi": false,
"jwt_secret_key": "somethingrandom",
"jwt_secret_key": "{{ api_server_jwt_key }}",
"CORS_origins": [],
"username": "",
"password": ""
"username": "{{ api_server_username }}",
"password": "{{ api_server_password }}"
},
"bot_name": "freqtrade",
"initial_state": "running",

4
freqtrade/templates/base_strategy.py.j2
View File

@@ -1,4 +1,5 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np # noqa
@@ -6,6 +7,7 @@ import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
# --------------------------------
# Add your lib to import here
@@ -16,7 +18,7 @@ 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
More information in https://www.freqtrade.io/en/latest/strategy-customization/
You can:
:return: a Dataframe with all mandatory indicators for the strategies

14
freqtrade/templates/sample_hyperopt_advanced.py
View File

@@ -7,7 +7,7 @@ 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.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa
from freqtrade.optimize.hyperopt_interface import IHyperOpt
@@ -223,9 +223,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
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'),
SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
]
@staticmethod
@@ -237,7 +237,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
'stoploss' optimization hyperspace.
"""
return [
Real(-0.35, -0.02, name='stoploss'),
SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
]
@staticmethod
@@ -256,14 +256,14 @@ class AdvancedSampleHyperOpt(IHyperOpt):
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
Real(0.01, 0.35, name='trailing_stop_positive'),
SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]

16
freqtrade/templates/sample_strategy.py
View File

@@ -1,4 +1,5 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
@@ -6,6 +7,7 @@ import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
# --------------------------------
# Add your lib to import here
@@ -53,7 +55,11 @@ class SampleStrategy(IStrategy):
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal ticker interval for the strategy.
# Hyperoptable parameters
buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
# Optimal timeframe for the strategy.
timeframe = '5m'
# Run "populate_indicators()" only for new candle.
@@ -340,7 +346,8 @@ class SampleStrategy(IStrategy):
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
# Signal: RSI crosses above 30
(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
(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
@@ -354,11 +361,12 @@ class SampleStrategy(IStrategy):
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
:return: DataFrame with sell column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
# Signal: RSI crosses above 70
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &
(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

22
freqtrade/templates/strategy_analysis_example.ipynb
View File

@@ -282,6 +282,28 @@
"graph.show(renderer=\"browser\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot average profit per trade as distribution graph"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import plotly.figure_factory as ff\n",
"\n",
"hist_data = [trades.profit_ratio]\n",
"group_labels = ['profit_ratio'] # name of the dataset\n",
"\n",
"fig = ff.create_distplot(hist_data, group_labels,bin_size=0.01)\n",
"fig.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},

13
freqtrade/templates/subtemplates/strategy_methods_advanced.j2
View File

@@ -14,8 +14,9 @@ def bot_loop_start(self, **kwargs) -> None:
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs) -> float:
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime',
current_rate: float, current_profit: float, dataframe: DataFrame,
**kwargs) -> float:
"""
Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
e.g. returning -0.05 would create a stoploss 5% below current_rate.
@@ -31,13 +32,14 @@ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime', c
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param dataframe: Analyzed dataframe for this pair. Can contain future data in backtesting.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New stoploss value, relative to the currentrate
"""
return self.stoploss
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool:
time_in_force: str, current_time: 'datetime', **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -52,6 +54,7 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
@@ -59,7 +62,8 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
return True
def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
rate: float, time_in_force: str, sell_reason: str,
current_time: 'datetime', **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -78,6 +82,7 @@ def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount:
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process

43
freqtrade/wallets.py
View File

@@ -99,12 +99,13 @@ class Wallets:
balances = self._exchange.get_balances()
for currency in balances:
self._wallets[currency] = Wallet(
currency,
balances[currency].get('free', None),
balances[currency].get('used', None),
balances[currency].get('total', None)
)
if isinstance(balances[currency], dict):
self._wallets[currency] = Wallet(
currency,
balances[currency].get('free', None),
balances[currency].get('used', None),
balances[currency].get('total', None)
)
# Remove currencies no longer in get_balances output
for currency in deepcopy(self._wallets):
if currency not in balances:
@@ -130,14 +131,13 @@ class Wallets:
def get_all_balances(self) -> Dict[str, Any]:
return self._wallets
def _get_available_stake_amount(self) -> float:
def _get_available_stake_amount(self, val_tied_up: float) -> float:
"""
Return the total currently available balance in stake currency,
respecting tradable_balance_ratio.
Calculated as
(<open_trade stakes> + free amount ) * tradable_balance_ratio - <open_trade stakes>
(<open_trade stakes> + free amount) * tradable_balance_ratio - <open_trade stakes>
"""
val_tied_up = Trade.total_open_trades_stakes()
# Ensure <tradable_balance_ratio>% is used from the overall balance
# Otherwise we'd risk lowering stakes with each open trade.
@@ -146,26 +146,26 @@ class Wallets:
self._config['tradable_balance_ratio']) - val_tied_up
return available_amount
def _calculate_unlimited_stake_amount(self, free_open_trades: int) -> float:
def _calculate_unlimited_stake_amount(self, available_amount: float,
val_tied_up: float) -> float:
"""
Calculate stake amount for "unlimited" stake amount
:return: 0 if max number of trades reached, else stake_amount to use.
"""
if not free_open_trades:
if self._config['max_open_trades'] == 0:
return 0
available_amount = self._get_available_stake_amount()
possible_stake = (available_amount + val_tied_up) / self._config['max_open_trades']
# Theoretical amount can be above available amount - therefore limit to available amount!
return min(possible_stake, available_amount)
return available_amount / free_open_trades
def _check_available_stake_amount(self, stake_amount: float) -> float:
def _check_available_stake_amount(self, stake_amount: float, available_amount: float) -> float:
"""
Check if stake amount can be fulfilled with the available balance
for the stake currency
:return: float: Stake amount
:raise: DependencyException if balance is lower than stake-amount
"""
available_amount = self._get_available_stake_amount()
if self._config['amend_last_stake_amount']:
# Remaining amount needs to be at least stake_amount * last_stake_amount_min_ratio
@@ -183,7 +183,7 @@ class Wallets:
return stake_amount
def get_trade_stake_amount(self, pair: str, free_open_trades: int, edge=None) -> float:
def get_trade_stake_amount(self, pair: str, edge=None) -> float:
"""
Calculate stake amount for the trade
:return: float: Stake amount
@@ -192,17 +192,20 @@ class Wallets:
stake_amount: float
# Ensure wallets are uptodate.
self.update()
val_tied_up = Trade.total_open_trades_stakes()
available_amount = self._get_available_stake_amount(val_tied_up)
if edge:
stake_amount = edge.stake_amount(
pair,
self.get_free(self._config['stake_currency']),
self.get_total(self._config['stake_currency']),
Trade.total_open_trades_stakes()
val_tied_up
)
else:
stake_amount = self._config['stake_amount']
if stake_amount == UNLIMITED_STAKE_AMOUNT:
stake_amount = self._calculate_unlimited_stake_amount(free_open_trades)
stake_amount = self._calculate_unlimited_stake_amount(
available_amount, val_tied_up)
return self._check_available_stake_amount(stake_amount)
return self._check_available_stake_amount(stake_amount, available_amount)