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Merge branch 'develop' into pr/SmartManoj/6859

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This commit is contained in:
Matthias
2022-06-23 20:43:35 +02:00
parent 2b61aa282a ddc355feb6
commit 8bf0bf10c5
116 changed files with 11323 additions and 9511 deletions
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1
freqtrade/commands/__init__.py
View File

@@ -6,6 +6,7 @@ Contains all start-commands, subcommands and CLI Interface creation.
Note: Be careful with file-scoped imports in these subfiles.
as they are parsed on startup, nothing containing optional modules should be loaded.
"""
from freqtrade.commands.analyze_commands import start_analysis_entries_exits
from freqtrade.commands.arguments import Arguments
from freqtrade.commands.build_config_commands import start_new_config
from freqtrade.commands.data_commands import (start_convert_data, start_convert_trades,

69
freqtrade/commands/analyze_commands.py Executable file
View File

@@ -0,0 +1,69 @@
import logging
from pathlib import Path
from typing import Any, Dict
from freqtrade.configuration import setup_utils_configuration
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def setup_analyze_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
"""
Prepare the configuration for the entry/exit reason analysis module
:param args: Cli args from Arguments()
:param method: Bot running mode
:return: Configuration
"""
config = setup_utils_configuration(args, method)
no_unlimited_runmodes = {
RunMode.BACKTEST: 'backtesting',
}
if method in no_unlimited_runmodes.keys():
from freqtrade.data.btanalysis import get_latest_backtest_filename
if 'exportfilename' in config:
if config['exportfilename'].is_dir():
btfile = Path(get_latest_backtest_filename(config['exportfilename']))
signals_file = f"{config['exportfilename']}/{btfile.stem}_signals.pkl"
else:
if config['exportfilename'].exists():
btfile = Path(config['exportfilename'])
signals_file = f"{btfile.parent}/{btfile.stem}_signals.pkl"
else:
raise OperationalException(f"{config['exportfilename']} does not exist.")
else:
raise OperationalException('exportfilename not in config.')
if (not Path(signals_file).exists()):
raise OperationalException(
(f"Cannot find latest backtest signals file: {signals_file}."
"Run backtesting with `--export signals`.")
)
return config
def start_analysis_entries_exits(args: Dict[str, Any]) -> None:
"""
Start analysis script
:param args: Cli args from Arguments()
:return: None
"""
from freqtrade.data.entryexitanalysis import process_entry_exit_reasons
# Initialize configuration
config = setup_analyze_configuration(args, RunMode.BACKTEST)
logger.info('Starting freqtrade in analysis mode')
process_entry_exit_reasons(config['exportfilename'],
config['exchange']['pair_whitelist'],
config['analysis_groups'],
config['enter_reason_list'],
config['exit_reason_list'],
config['indicator_list']
)

15
freqtrade/commands/arguments.py
View File

@@ -101,6 +101,9 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
"print_json", "hyperoptexportfilename", "hyperopt_show_no_header",
"disableparamexport", "backtest_breakdown"]
ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
"exit_reason_list", "indicator_list"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-data",
"hyperopt-list", "hyperopt-show", "backtest-filter",
@@ -182,8 +185,9 @@ class Arguments:
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.commands import (start_backtesting, start_backtesting_show,
start_convert_data, start_convert_db, start_convert_trades,
from freqtrade.commands import (start_analysis_entries_exits, start_backtesting,
start_backtesting_show, start_convert_data,
start_convert_db, start_convert_trades,
start_create_userdir, start_download_data, start_edge,
start_hyperopt, start_hyperopt_list, start_hyperopt_show,
start_install_ui, start_list_data, start_list_exchanges,
@@ -283,6 +287,13 @@ class Arguments:
backtesting_show_cmd.set_defaults(func=start_backtesting_show)
self._build_args(optionlist=ARGS_BACKTEST_SHOW, parser=backtesting_show_cmd)
# Add backtesting analysis subcommand
analysis_cmd = subparsers.add_parser('backtesting-analysis',
help='Backtest Analysis module.',
parents=[_common_parser])
analysis_cmd.set_defaults(func=start_analysis_entries_exits)
self._build_args(optionlist=ARGS_ANALYZE_ENTRIES_EXITS, parser=analysis_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])

33
freqtrade/commands/cli_options.py
View File

@@ -614,4 +614,37 @@ AVAILABLE_CLI_OPTIONS = {
"that do not contain any parameters."),
action="store_true",
),
"analysis_groups": Arg(
"--analysis-groups",
help=("grouping output - "
"0: simple wins/losses by enter tag, "
"1: by enter_tag, "
"2: by enter_tag and exit_tag, "
"3: by pair and enter_tag, "
"4: by pair, enter_ and exit_tag (this can get quite large)"),
nargs='+',
default=['0', '1', '2'],
choices=['0', '1', '2', '3', '4'],
),
"enter_reason_list": Arg(
"--enter-reason-list",
help=("Comma separated list of entry signals to analyse. Default: all. "
"e.g. 'entry_tag_a,entry_tag_b'"),
nargs='+',
default=['all'],
),
"exit_reason_list": Arg(
"--exit-reason-list",
help=("Comma separated list of exit signals to analyse. Default: all. "
"e.g. 'exit_tag_a,roi,stop_loss,trailing_stop_loss'"),
nargs='+',
default=['all'],
),
"indicator_list": Arg(
"--indicator-list",
help=("Comma separated list of indicators to analyse. "
"e.g. 'close,rsi,bb_lowerband,profit_abs'"),
nargs='+',
default=[],
),
}

4
freqtrade/commands/db_commands.py
View File

@@ -19,9 +19,9 @@ def start_convert_db(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
init_db(config['db_url'], False)
init_db(config['db_url'])
session_target = Trade._session
init_db(config['db_url_from'], False)
init_db(config['db_url_from'])
logger.info("Starting db migration.")
trade_count = 0

2
freqtrade/commands/list_commands.py
View File

@@ -212,7 +212,7 @@ def start_show_trades(args: Dict[str, Any]) -> None:
raise OperationalException("--db-url is required for this command.")
logger.info(f'Using DB: "{parse_db_uri_for_logging(config["db_url"])}"')
init_db(config['db_url'], clean_open_orders=False)
init_db(config['db_url'])
tfilter = []
if config.get('trade_ids'):

2
freqtrade/configuration/check_exchange.py
View File

@@ -27,7 +27,7 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
return True
logger.info("Checking exchange...")
exchange = config.get('exchange', {}).get('name').lower()
exchange = config.get('exchange', {}).get('name', '').lower()
if not exchange:
raise OperationalException(
f'This command requires a configured exchange. You should either use '

20
freqtrade/configuration/configuration.py
View File

@@ -95,6 +95,8 @@ class Configuration:
self._process_data_options(config)
self._process_analyze_options(config)
# Check if the exchange set by the user is supported
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
@@ -433,6 +435,19 @@ class Configuration:
self._args_to_config(config, argname='candle_types',
logstring='Detected --candle-types: {}')
def _process_analyze_options(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='analysis_groups',
logstring='Analysis reason groups: {}')
self._args_to_config(config, argname='enter_reason_list',
logstring='Analysis enter tag list: {}')
self._args_to_config(config, argname='exit_reason_list',
logstring='Analysis exit tag list: {}')
self._args_to_config(config, argname='indicator_list',
logstring='Analysis indicator list: {}')
def _process_runmode(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='dry_run',
@@ -490,7 +505,8 @@ class Configuration:
if not pairs_file.exists():
raise OperationalException(f'No pairs file found with path "{pairs_file}".')
config['pairs'] = load_file(pairs_file)
config['pairs'].sort()
if isinstance(config['pairs'], list):
config['pairs'].sort()
return
if 'config' in self.args and self.args['config']:
@@ -501,5 +517,5 @@ class Configuration:
pairs_file = config['datadir'] / 'pairs.json'
if pairs_file.exists():
config['pairs'] = load_file(pairs_file)
if 'pairs' in config:
if 'pairs' in config and isinstance(config['pairs'], list):
config['pairs'].sort()

2
freqtrade/configuration/deprecated_settings.py
View File

@@ -113,7 +113,7 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
process_removed_setting(config, 'experimental', 'ignore_roi_if_buy_signal',
None, 'ignore_roi_if_entry_signal')
process_removed_setting(config, 'ask_strategy', 'use_sell_signal', None, 'exit_sell_signal')
process_removed_setting(config, 'ask_strategy', 'use_sell_signal', None, 'use_exit_signal')
process_removed_setting(config, 'ask_strategy', 'sell_profit_only', None, 'exit_profit_only')
process_removed_setting(config, 'ask_strategy', 'sell_profit_offset',
None, 'exit_profit_offset')

2
freqtrade/configuration/directory_operations.py
View File

@@ -15,7 +15,7 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Pat
folder = Path(datadir) if datadir else Path(f"{config['user_data_dir']}/data")
if not datadir:
# set datadir
exchange_name = config.get('exchange', {}).get('name').lower()
exchange_name = config.get('exchange', {}).get('name', '').lower()
folder = folder.joinpath(exchange_name)
if not folder.is_dir():

45
freqtrade/constants.py
View File

@@ -302,12 +302,12 @@ CONF_SCHEMA = {
'exit_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
'default': 'on'
},
'protection_trigger': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
'default': 'on'
},
'protection_trigger_global': {
'type': 'string',
@@ -336,6 +336,47 @@ CONF_SCHEMA = {
'webhookstatus': {'type': 'object'},
},
},
'discord': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'webhook_url': {'type': 'string'},
"exit_fill": {
'type': 'array', 'items': {'type': 'object'},
'default': [
{"Trade ID": "{trade_id}"},
{"Exchange": "{exchange}"},
{"Pair": "{pair}"},
{"Direction": "{direction}"},
{"Open rate": "{open_rate}"},
{"Close rate": "{close_rate}"},
{"Amount": "{amount}"},
{"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
{"Close date": "{close_date:%Y-%m-%d %H:%M:%S}"},
{"Profit": "{profit_amount} {stake_currency}"},
{"Profitability": "{profit_ratio:.2%}"},
{"Enter tag": "{enter_tag}"},
{"Exit Reason": "{exit_reason}"},
{"Strategy": "{strategy}"},
{"Timeframe": "{timeframe}"},
]
},
"entry_fill": {
'type': 'array', 'items': {'type': 'object'},
'default': [
{"Trade ID": "{trade_id}"},
{"Exchange": "{exchange}"},
{"Pair": "{pair}"},
{"Direction": "{direction}"},
{"Open rate": "{open_rate}"},
{"Amount": "{amount}"},
{"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
{"Enter tag": "{enter_tag}"},
{"Strategy": "{strategy} {timeframe}"},
]
},
}
},
'api_server': {
'type': 'object',
'properties': {

8
freqtrade/data/btanalysis.py
View File

@@ -26,7 +26,7 @@ BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
'profit_ratio', 'profit_abs', 'exit_reason',
'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'enter_tag',
'is_short'
'is_short', 'open_timestamp', 'close_timestamp', 'orders'
]
@@ -283,6 +283,8 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
if 'enter_tag' not in df.columns:
df['enter_tag'] = df['buy_tag']
df = df.drop(['buy_tag'], axis=1)
if 'orders' not in df.columns:
df.loc[:, 'orders'] = None
else:
# old format - only with lists.
@@ -337,7 +339,7 @@ def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
:param trades: List of trade objects
:return: Dataframe with BT_DATA_COLUMNS
"""
df = pd.DataFrame.from_records([t.to_json() for t in trades], columns=BT_DATA_COLUMNS)
df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS)
if len(df) > 0:
df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True)
df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True)
@@ -353,7 +355,7 @@ def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataF
Can also serve as protection to load the correct result.
:return: Dataframe containing Trades
"""
init_db(db_url, clean_open_orders=False)
init_db(db_url)
filters = []
if strategy:

227
freqtrade/data/entryexitanalysis.py Executable file
View File

@@ -0,0 +1,227 @@
import logging
from pathlib import Path
from typing import List, Optional
import joblib
import pandas as pd
from tabulate import tabulate
from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
load_backtest_stats)
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def _load_signal_candles(backtest_dir: Path):
if backtest_dir.is_dir():
scpf = Path(backtest_dir,
Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
)
else:
scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
try:
scp = open(scpf, "rb")
signal_candles = joblib.load(scp)
logger.info(f"Loaded signal candles: {str(scpf)}")
except Exception as e:
logger.error("Cannot load signal candles from pickled results: ", e)
return signal_candles
def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
analysed_trades_dict = {}
analysed_trades_dict[strategy_name] = {}
try:
logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs")
for pair in pairlist:
if pair in signal_candles[strategy_name]:
analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
pair,
trades,
signal_candles[strategy_name][pair])
except Exception as e:
print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
return analysed_trades_dict
def _analyze_candles_and_indicators(pair, trades, signal_candles):
buyf = signal_candles
if len(buyf) > 0:
buyf = buyf.set_index('date', drop=False)
trades_red = trades.loc[trades['pair'] == pair].copy()
trades_inds = pd.DataFrame()
if trades_red.shape[0] > 0 and buyf.shape[0] > 0:
for t, v in trades_red.open_date.items():
allinds = buyf.loc[(buyf['date'] < v)]
if allinds.shape[0] > 0:
tmp_inds = allinds.iloc[[-1]]
trades_red.loc[t, 'signal_date'] = tmp_inds['date'].values[0]
trades_red.loc[t, 'enter_reason'] = trades_red.loc[t, 'enter_tag']
tmp_inds.index.rename('signal_date', inplace=True)
trades_inds = pd.concat([trades_inds, tmp_inds])
if 'signal_date' in trades_red:
trades_red['signal_date'] = pd.to_datetime(trades_red['signal_date'], utc=True)
trades_red.set_index('signal_date', inplace=True)
try:
trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
except Exception as e:
raise e
return trades_red
else:
return pd.DataFrame()
def _do_group_table_output(bigdf, glist):
for g in glist:
# 0: summary wins/losses grouped by enter tag
if g == "0":
group_mask = ['enter_reason']
wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
.groupby(group_mask) \
.agg({'profit_abs': ['sum']})
wins.columns = ['profit_abs_wins']
loss = bigdf.loc[bigdf['profit_abs'] < 0] \
.groupby(group_mask) \
.agg({'profit_abs': ['sum']})
loss.columns = ['profit_abs_loss']
new = bigdf.groupby(group_mask).agg({'profit_abs': [
'count',
lambda x: sum(x > 0),
lambda x: sum(x <= 0)]})
new = pd.concat([new, wins, loss], axis=1).fillna(0)
new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss'])
new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1]).fillna(0)
new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0)
new.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss',
'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss']
sortcols = ['total_num_buys']
_print_table(new, sortcols, show_index=True)
else:
agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
'total_profit_pct']
sortcols = ['profit_abs_sum', 'enter_reason']
# 1: profit summaries grouped by enter_tag
if g == "1":
group_mask = ['enter_reason']
# 2: profit summaries grouped by enter_tag and exit_tag
if g == "2":
group_mask = ['enter_reason', 'exit_reason']
# 3: profit summaries grouped by pair and enter_tag
if g == "3":
group_mask = ['pair', 'enter_reason']
# 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
if g == "4":
group_mask = ['pair', 'enter_reason', 'exit_reason']
if group_mask:
new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
new.columns = group_mask + agg_cols
new['median_profit_pct'] = new['median_profit_pct'] * 100
new['mean_profit_pct'] = new['mean_profit_pct'] * 100
new['total_profit_pct'] = new['total_profit_pct'] * 100
_print_table(new, sortcols)
else:
logger.warning("Invalid group mask specified.")
def _print_results(analysed_trades, stratname, analysis_groups,
enter_reason_list, exit_reason_list,
indicator_list, columns=None):
if columns is None:
columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
bigdf = pd.DataFrame()
for pair, trades in analysed_trades[stratname].items():
bigdf = pd.concat([bigdf, trades], ignore_index=True)
if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
if analysis_groups:
_do_group_table_output(bigdf, analysis_groups)
if enter_reason_list and "all" not in enter_reason_list:
bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
if exit_reason_list and "all" not in exit_reason_list:
bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
if "all" in indicator_list:
print(bigdf)
elif indicator_list is not None:
available_inds = []
for ind in indicator_list:
if ind in bigdf:
available_inds.append(ind)
ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
_print_table(bigdf[ilist], sortcols=['exit_reason'], show_index=False)
else:
print("\\_ No trades to show")
def _print_table(df, sortcols=None, show_index=False):
if (sortcols is not None):
data = df.sort_values(sortcols)
else:
data = df
print(
tabulate(
data,
headers='keys',
tablefmt='psql',
showindex=show_index
)
)
def process_entry_exit_reasons(backtest_dir: Path,
pairlist: List[str],
analysis_groups: Optional[List[str]] = ["0", "1", "2"],
enter_reason_list: Optional[List[str]] = ["all"],
exit_reason_list: Optional[List[str]] = ["all"],
indicator_list: Optional[List[str]] = []):
try:
backtest_stats = load_backtest_stats(backtest_dir)
for strategy_name, results in backtest_stats['strategy'].items():
trades = load_backtest_data(backtest_dir, strategy_name)
if not trades.empty:
signal_candles = _load_signal_candles(backtest_dir)
analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
trades, signal_candles)
_print_results(analysed_trades_dict,
strategy_name,
analysis_groups,
enter_reason_list,
exit_reason_list,
indicator_list)
except ValueError as e:
raise OperationalException(e) from e

10
freqtrade/data/history/history_utils.py
View File

@@ -68,7 +68,8 @@ def load_data(datadir: Path,
startup_candles: int = 0,
fail_without_data: bool = False,
data_format: str = 'json',
candle_type: CandleType = CandleType.SPOT
candle_type: CandleType = CandleType.SPOT,
user_futures_funding_rate: int = None,
) -> Dict[str, DataFrame]:
"""
Load ohlcv history data for a list of pairs.
@@ -100,6 +101,10 @@ def load_data(datadir: Path,
)
if not hist.empty:
result[pair] = hist
else:
if candle_type is CandleType.FUNDING_RATE and user_futures_funding_rate is not None:
logger.warn(f"{pair} using user specified [{user_futures_funding_rate}]")
result[pair] = DataFrame(columns=["open", "close", "high", "low", "volume"])
if fail_without_data and not result:
raise OperationalException("No data found. Terminating.")
@@ -216,7 +221,7 @@ def _download_pair_history(pair: str, *,
prepend=prepend)
logger.info(f'({process}) - Download history data for "{pair}", {timeframe}, '
f'{candle_type} and store in {datadir}.'
f'{candle_type} and store in {datadir}. '
f'From {format_ms_time(since_ms) if since_ms else "start"} to '
f'{format_ms_time(until_ms) if until_ms else "now"}'
)
@@ -277,6 +282,7 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
pairs_not_available = []
data_handler = get_datahandler(datadir, data_format)
candle_type = CandleType.get_default(trading_mode)
process = ''
for idx, pair in enumerate(pairs, start=1):
if pair not in exchange.markets:
pairs_not_available.append(pair)

6
freqtrade/enums/exitchecktuple.py
View File

@@ -15,3 +15,9 @@ class ExitCheckTuple:
@property
def exit_flag(self):
return self.exit_type != ExitType.NONE
def __eq__(self, other):
return self.exit_type == other.exit_type and self.exit_reason == other.exit_reason
def __repr__(self):
return f"ExitCheckTuple({self.exit_type}, {self.exit_reason})"

17
freqtrade/exchange/binance.py
View File

@@ -52,12 +52,17 @@ class Binance(Exchange):
ordertype = 'stop' if self.trading_mode == TradingMode.FUTURES else 'stop_loss_limit'
return order['type'] == ordertype and (
(side == "sell" and stop_loss > float(order['info']['stopPrice'])) or
(side == "buy" and stop_loss < float(order['info']['stopPrice']))
)
return (
order.get('stopPrice', None) is None
or (
order['type'] == ordertype
and (
(side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
)
))
def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
tickers = super().get_tickers(symbols=symbols, cached=cached)
if self.trading_mode == TradingMode.FUTURES:
# Binance's future result has no bid/ask values.
@@ -95,7 +100,7 @@ class Binance(Exchange):
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: int = None
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
"""
Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date

15935
freqtrade/exchange/binance_leverage_tiers.json
View File

File diff suppressed because it is too large Load Diff

14
freqtrade/exchange/bybit.py
View File

@@ -29,3 +29,17 @@ class Bybit(Exchange):
# (TradingMode.FUTURES, MarginMode.CROSS),
# (TradingMode.FUTURES, MarginMode.ISOLATED)
]
@property
def _ccxt_config(self) -> Dict:
# Parameters to add directly to ccxt sync/async initialization.
# ccxt defaults to swap mode.
config = {}
if self.trading_mode == TradingMode.SPOT:
config.update({
"options": {
"defaultType": "spot"
}
})
config.update(super()._ccxt_config)
return config

29
freqtrade/exchange/common.py
View File

@@ -2,6 +2,7 @@ import asyncio
import logging
import time
from functools import wraps
from typing import Any, Callable, Optional, TypeVar, cast, overload
from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
from freqtrade.mixins import LoggingMixin
@@ -11,6 +12,14 @@ logger = logging.getLogger(__name__)
__logging_mixin = None
def _reset_logging_mixin():
"""
Reset global logging mixin - used in tests only.
"""
global __logging_mixin
__logging_mixin = LoggingMixin(logger)
def _get_logging_mixin():
# Logging-mixin to cache kucoin responses
# Only to be used in retrier
@@ -133,8 +142,22 @@ def retrier_async(f):
return wrapper
def retrier(_func=None, retries=API_RETRY_COUNT):
def decorator(f):
F = TypeVar('F', bound=Callable[..., Any])
# Type shenanigans
@overload
def retrier(_func: F) -> F:
...
@overload
def retrier(*, retries=API_RETRY_COUNT) -> Callable[[F], F]:
...
def retrier(_func: Optional[F] = None, *, retries=API_RETRY_COUNT):
def decorator(f: F) -> F:
@wraps(f)
def wrapper(*args, **kwargs):
count = kwargs.pop('count', retries)
@@ -155,7 +178,7 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
else:
logger.warning(msg + 'Giving up.')
raise ex
return wrapper
return cast(F, wrapper)
# Support both @retrier and @retrier(retries=2) syntax
if _func is None:
return decorator

74
freqtrade/exchange/exchange.py
View File

@@ -92,7 +92,7 @@ class Exchange:
it does basic validation whether the specified exchange and pairs are valid.
:return: None
"""
self._api: ccxt.Exchange = None
self._api: ccxt.Exchange
self._api_async: ccxt_async.Exchange = None
self._markets: Dict = {}
self._trading_fees: Dict[str, Any] = {}
@@ -291,7 +291,7 @@ class Exchange:
return self._markets
@property
def precisionMode(self) -> str:
def precisionMode(self) -> int:
"""exchange ccxt precisionMode"""
return self._api.precisionMode
@@ -322,7 +322,7 @@ class Exchange:
return int(self._ft_has.get('ohlcv_candle_limit_per_timeframe', {}).get(
timeframe, self._ft_has.get('ohlcv_candle_limit')))
def get_markets(self, base_currencies: List[str] = None, quote_currencies: List[str] = None,
def get_markets(self, base_currencies: List[str] = [], quote_currencies: List[str] = [],
spot_only: bool = False, margin_only: bool = False, futures_only: bool = False,
tradable_only: bool = True,
active_only: bool = False) -> Dict[str, Any]:
@@ -953,6 +953,12 @@ class Exchange:
order = self.check_dry_limit_order_filled(order)
return order
except KeyError as e:
from freqtrade.persistence import Order
order = Order.order_by_id(order_id)
if order:
ccxt_order = order.to_ccxt_object()
self._dry_run_open_orders[order_id] = ccxt_order
return ccxt_order
# Gracefully handle errors with dry-run orders.
raise InvalidOrderException(
f'Tried to get an invalid dry-run-order (id: {order_id}). Message: {e}') from e
@@ -1158,7 +1164,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_order(self, order_id: str, pair: str, params={}) -> Dict:
def fetch_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
return self.fetch_dry_run_order(order_id)
try:
@@ -1180,8 +1186,8 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
# Assign method to fetch_stoploss_order to allow easy overriding in other classes
fetch_stoploss_order = fetch_order
def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
return self.fetch_order(order_id, pair, params)
def fetch_order_or_stoploss_order(self, order_id: str, pair: str,
stoploss_order: bool = False) -> Dict:
@@ -1206,7 +1212,7 @@ class Exchange:
and order.get('filled') == 0.0)
@retrier
def cancel_order(self, order_id: str, pair: str, params={}) -> Dict:
def cancel_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
try:
order = self.fetch_dry_run_order(order_id)
@@ -1232,8 +1238,8 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
# Assign method to cancel_stoploss_order to allow easy overriding in other classes
cancel_stoploss_order = cancel_order
def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
return self.cancel_order(order_id, pair, params)
def is_cancel_order_result_suitable(self, corder) -> bool:
if not isinstance(corder, dict):
@@ -1345,7 +1351,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def fetch_bids_asks(self, symbols: List[str] = None, cached: bool = False) -> Dict:
def fetch_bids_asks(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
"""
:param cached: Allow cached result
:return: fetch_tickers result
@@ -1373,7 +1379,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
"""
:param cached: Allow cached result
:return: fetch_tickers result
@@ -1712,7 +1718,7 @@ class Exchange:
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: int = None
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
"""
Download historic ohlcv
@@ -1773,7 +1779,7 @@ class Exchange:
def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
since_ms: Optional[int] = None, cache: bool = True,
drop_incomplete: bool = None
drop_incomplete: Optional[bool] = None
) -> Dict[PairWithTimeframe, DataFrame]:
"""
Refresh in-memory OHLCV asynchronously and set `_klines` with the result
@@ -2125,10 +2131,11 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def get_market_leverage_tiers(self, symbol) -> List[Dict]:
@retrier_async
async def get_market_leverage_tiers(self, symbol: str) -> Tuple[str, List[Dict]]:
try:
return self._api.fetch_market_leverage_tiers(symbol)
tier = await self._api_async.fetch_market_leverage_tiers(symbol)
return symbol, tier
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
@@ -2162,8 +2169,14 @@ class Exchange:
f"Initializing leverage_tiers for {len(symbols)} markets. "
"This will take about a minute.")
for symbol in sorted(symbols):
tiers[symbol] = self.get_market_leverage_tiers(symbol)
coros = [self.get_market_leverage_tiers(symbol) for symbol in sorted(symbols)]
for input_coro in chunks(coros, 100):
results = self.loop.run_until_complete(
asyncio.gather(*input_coro, return_exceptions=True))
for symbol, res in results:
tiers[symbol] = res
logger.info(f"Done initializing {len(symbols)} markets.")
@@ -2413,14 +2426,35 @@ class Exchange:
)
@staticmethod
def combine_funding_and_mark(funding_rates: DataFrame, mark_rates: DataFrame) -> DataFrame:
def combine_funding_and_mark(funding_rates: DataFrame, mark_rates: DataFrame,
futures_funding_rate: Optional[int] = None) -> DataFrame:
"""
Combine funding-rates and mark-rates dataframes
:param funding_rates: Dataframe containing Funding rates (Type FUNDING_RATE)
:param mark_rates: Dataframe containing Mark rates (Type mark_ohlcv_price)
:param futures_funding_rate: Fake funding rate to use if funding_rates are not available
"""
if futures_funding_rate is None:
return mark_rates.merge(
funding_rates, on='date', how="inner", suffixes=["_mark", "_fund"])
else:
if len(funding_rates) == 0:
# No funding rate candles - full fillup with fallback variable
mark_rates['open_fund'] = futures_funding_rate
return mark_rates.rename(
columns={'open': 'open_mark',
'close': 'close_mark',
'high': 'high_mark',
'low': 'low_mark',
'volume': 'volume_mark'})
return funding_rates.merge(mark_rates, on='date', how="inner", suffixes=["_fund", "_mark"])
else:
# Fill up missing funding_rate candles with fallback value
combined = mark_rates.merge(
funding_rates, on='date', how="outer", suffixes=["_mark", "_fund"]
)
combined['open_fund'] = combined['open_fund'].fillna(futures_funding_rate)
return combined
def calculate_funding_fees(
self,

4
freqtrade/exchange/ftx.py
View File

@@ -104,7 +104,7 @@ class Ftx(Exchange):
raise OperationalException(e) from e
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_stoploss_order(self, order_id: str, pair: str) -> Dict:
def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
return self.fetch_dry_run_order(order_id)
@@ -145,7 +145,7 @@ class Ftx(Exchange):
raise OperationalException(e) from e
@retrier
def cancel_stoploss_order(self, order_id: str, pair: str) -> Dict:
def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
return {}
try:

38
freqtrade/exchange/gateio.py
View File

@@ -3,6 +3,7 @@ import logging
from datetime import datetime
from typing import Dict, List, Optional, Tuple
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange
@@ -24,6 +25,8 @@ class Gateio(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ohlcv_volume_currency": "quote",
"time_in_force_parameter": "timeInForce",
"order_time_in_force": ['gtc', 'ioc'],
"stoploss_order_types": {"limit": "limit"},
"stoploss_on_exchange": True,
}
@@ -40,13 +43,33 @@ class Gateio(Exchange):
]
def validate_ordertypes(self, order_types: Dict) -> None:
super().validate_ordertypes(order_types)
if self.trading_mode != TradingMode.FUTURES:
if any(v == 'market' for k, v in order_types.items()):
raise OperationalException(
f'Exchange {self.name} does not support market orders.')
def _get_params(
self,
side: BuySell,
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc',
) -> Dict:
params = super()._get_params(
side=side,
ordertype=ordertype,
leverage=leverage,
reduceOnly=reduceOnly,
time_in_force=time_in_force,
)
if ordertype == 'market' and self.trading_mode == TradingMode.FUTURES:
params['type'] = 'market'
param = self._ft_has.get('time_in_force_parameter', '')
params.update({param: 'ioc'})
return params
def get_trades_for_order(self, order_id: str, pair: str, since: datetime,
params: Optional[Dict] = None) -> List:
trades = super().get_trades_for_order(order_id, pair, since, params)
@@ -61,7 +84,8 @@ class Gateio(Exchange):
pair_fees = self._trading_fees.get(pair, {})
if pair_fees:
for idx, trade in enumerate(trades):
if trade.get('fee', {}).get('cost') is None:
fee = trade.get('fee', {})
if fee and fee.get('cost') is None:
takerOrMaker = trade.get('takerOrMaker', 'taker')
if pair_fees.get(takerOrMaker) is not None:
trades[idx]['fee'] = {
@@ -71,14 +95,14 @@ class Gateio(Exchange):
}
return trades
def fetch_stoploss_order(self, order_id: str, pair: str, params={}) -> Dict:
def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
return self.fetch_order(
order_id=order_id,
pair=pair,
params={'stop': True}
)
def cancel_stoploss_order(self, order_id: str, pair: str, params={}) -> Dict:
def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
return self.cancel_order(
order_id=order_id,
pair=pair,
@@ -90,5 +114,7 @@ class Gateio(Exchange):
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return ((side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice'])))
return (order.get('stopPrice', None) is None or (
side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
)

8
freqtrade/exchange/huobi.py
View File

@@ -27,7 +27,13 @@ class Huobi(Exchange):
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return order['type'] == 'stop' and stop_loss > float(order['stopPrice'])
return (
order.get('stopPrice', None) is None
or (
order['type'] == 'stop'
and stop_loss > float(order['stopPrice'])
)
)
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:

2
freqtrade/exchange/kraken.py
View File

@@ -45,7 +45,7 @@ class Kraken(Exchange):
return (parent_check and
market.get('darkpool', False) is False)
def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
# Only fetch tickers for current stake currency
# Otherwise the request for kraken becomes too large.
symbols = list(self.get_markets(quote_currencies=[self._config['stake_currency']]))

5
freqtrade/exchange/kucoin.py
View File

@@ -33,7 +33,10 @@ class Kucoin(Exchange):
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return order['info'].get('stop') is not None and stop_loss > float(order['stopPrice'])
return (
order.get('stopPrice', None) is None
or stop_loss > float(order['stopPrice'])
)
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:

62
freqtrade/freqtradebot.py
View File

@@ -4,7 +4,7 @@ Freqtrade is the main module of this bot. It contains the class Freqtrade()
import copy
import logging
import traceback
from datetime import datetime, time, timezone
from datetime import datetime, time, timedelta, timezone
from math import isclose
from threading import Lock
from typing import Any, Dict, List, Optional, Tuple
@@ -67,14 +67,12 @@ class FreqtradeBot(LoggingMixin):
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
init_db(self.config['db_url'], clean_open_orders=self.config['dry_run'])
init_db(self.config['db_url'])
self.wallets = Wallets(self.config, self.exchange)
PairLocks.timeframe = self.config['timeframe']
self.protections = ProtectionManager(self.config, self.strategy.protections)
# RPC runs in separate threads, can start handling external commands just after
# initialization, even before Freqtradebot has a chance to start its throttling,
# so anything in the Freqtradebot instance should be ready (initialized), including
@@ -123,7 +121,9 @@ class FreqtradeBot(LoggingMixin):
self._schedule.every().day.at(t).do(update)
self.last_process = datetime(1970, 1, 1, tzinfo=timezone.utc)
self.strategy.bot_start()
self.strategy.ft_bot_start()
# Initialize protections AFTER bot start - otherwise parameters are not loaded.
self.protections = ProtectionManager(self.config, self.strategy.protections)
def notify_status(self, msg: str) -> None:
"""
@@ -227,7 +227,7 @@ class FreqtradeBot(LoggingMixin):
Notify the user when the bot is stopped (not reloaded)
and there are still open trades active.
"""
open_trades = Trade.get_trades([Trade.is_open.is_(True)]).all()
open_trades = Trade.get_open_trades()
if len(open_trades) != 0 and self.state != State.RELOAD_CONFIG:
msg = {
@@ -299,7 +299,17 @@ class FreqtradeBot(LoggingMixin):
fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
order.ft_order_side == 'stoploss')
self.update_trade_state(order.trade, order.order_id, fo)
self.update_trade_state(order.trade, order.order_id, fo,
stoploss_order=(order.ft_order_side == 'stoploss'))
except InvalidOrderException as e:
logger.warning(f"Error updating Order {order.order_id} due to {e}.")
if order.order_date_utc - timedelta(days=5) < datetime.now(timezone.utc):
logger.warning(
"Order is older than 5 days. Assuming order was fully cancelled.")
fo = order.to_ccxt_object()
fo['status'] = 'canceled'
self.handle_timedout_order(fo, order.trade)
except ExchangeError as e:
@@ -780,7 +790,7 @@ class FreqtradeBot(LoggingMixin):
current_rate=enter_limit_requested,
proposed_leverage=1.0,
max_leverage=max_leverage,
side=trade_side,
side=trade_side, entry_tag=entry_tag,
) if self.trading_mode != TradingMode.SPOT else 1.0
# Cap leverage between 1.0 and max_leverage.
leverage = min(max(leverage, 1.0), max_leverage)
@@ -1018,7 +1028,7 @@ class FreqtradeBot(LoggingMixin):
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
self._notify_exit(trade, "stoploss")
self._notify_exit(trade, "stoploss", True)
return True
if trade.open_order_id or not trade.is_open:
@@ -1105,7 +1115,7 @@ class FreqtradeBot(LoggingMixin):
"""
Check and execute trade exit
"""
should_exit: ExitCheckTuple = self.strategy.should_exit(
exits: List[ExitCheckTuple] = self.strategy.should_exit(
trade,
exit_rate,
datetime.now(timezone.utc),
@@ -1113,12 +1123,13 @@ class FreqtradeBot(LoggingMixin):
exit_=exit_,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
if should_exit.exit_flag:
logger.info(f'Exit for {trade.pair} detected. Reason: {should_exit.exit_type}'
f'Tag: {exit_tag if exit_tag is not None else "None"}')
self.execute_trade_exit(trade, exit_rate, should_exit, exit_tag=exit_tag)
return True
for should_exit in exits:
if should_exit.exit_flag:
logger.info(f'Exit for {trade.pair} detected. Reason: {should_exit.exit_type}'
f'{f" Tag: {exit_tag}" if exit_tag is not None else ""}')
exited = self.execute_trade_exit(trade, exit_rate, should_exit, exit_tag=exit_tag)
if exited:
return True
return False
def manage_open_orders(self) -> None:
@@ -1201,15 +1212,15 @@ class FreqtradeBot(LoggingMixin):
current_order_rate=order_obj.price, entry_tag=trade.enter_tag,
side=trade.entry_side)
full_cancel = False
replacing = True
cancel_reason = constants.CANCEL_REASON['REPLACE']
if not adjusted_entry_price:
full_cancel = True if trade.nr_of_successful_entries == 0 else False
replacing = False
cancel_reason = constants.CANCEL_REASON['USER_CANCEL']
if order_obj.price != adjusted_entry_price:
# cancel existing order if new price is supplied or None
self.handle_cancel_enter(trade, order, cancel_reason,
allow_full_cancel=full_cancel)
replacing=replacing)
if adjusted_entry_price:
# place new order only if new price is supplied
self.execute_entry(
@@ -1243,10 +1254,11 @@ class FreqtradeBot(LoggingMixin):
def handle_cancel_enter(
self, trade: Trade, order: Dict, reason: str,
allow_full_cancel: Optional[bool] = True
replacing: Optional[bool] = False
) -> bool:
"""
Buy cancel - cancel order
:param replacing: Replacing order - prevent trade deletion.
:return: True if order was fully cancelled
"""
was_trade_fully_canceled = False
@@ -1284,7 +1296,7 @@ class FreqtradeBot(LoggingMixin):
if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
# if trade is not partially completed and it's the only order, just delete the trade
open_order_count = len([order for order in trade.orders if order.status == 'open'])
if open_order_count <= 1 and allow_full_cancel:
if open_order_count <= 1 and trade.nr_of_successful_entries == 0 and not replacing:
logger.info(f'{side} order fully cancelled. Removing {trade} from database.')
trade.delete()
was_trade_fully_canceled = True
@@ -1293,7 +1305,7 @@ class FreqtradeBot(LoggingMixin):
# FIXME TODO: This could possibly reworked to not duplicate the code 15 lines below.
self.update_trade_state(trade, trade.open_order_id, corder)
trade.open_order_id = None
logger.info(f'Partial {side} order timeout for {trade}.')
logger.info(f'{side} Order timeout for {trade}.')
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
@@ -1405,7 +1417,7 @@ class FreqtradeBot(LoggingMixin):
:param trade: Trade instance
:param limit: limit rate for the sell order
:param exit_check: CheckTuple with signal and reason
:return: True if it succeeds (supported) False (not supported)
:return: True if it succeeds False
"""
trade.funding_fees = self.exchange.get_funding_fees(
pair=trade.pair,
@@ -1452,7 +1464,7 @@ class FreqtradeBot(LoggingMixin):
time_in_force=time_in_force, exit_reason=exit_reason,
sell_reason=exit_reason, # sellreason -> compatibility
current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of exiting {trade.pair}")
logger.info(f"User requested abortion of {trade.pair} exit.")
return False
try:
@@ -1663,7 +1675,7 @@ class FreqtradeBot(LoggingMixin):
if send_msg and not stoploss_order and not trade.open_order_id:
self._notify_exit(trade, '', True)
self.handle_protections(trade.pair, trade.trade_direction)
elif send_msg and not trade.open_order_id:
elif send_msg and not trade.open_order_id and not stoploss_order:
# Enter fill
self._notify_enter(trade, order, fill=True)

84
freqtrade/optimize/backtesting.py
View File

@@ -187,7 +187,8 @@ class Backtesting:
# since a "perfect" stoploss-exit is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
self.strategy.bot_start()
self.strategy.ft_bot_start()
def _load_protections(self, strategy: IStrategy):
if self.config.get('enable_protections', False):
@@ -275,8 +276,12 @@ class Backtesting:
if pair not in self.exchange._leverage_tiers:
unavailable_pairs.append(pair)
continue
self.futures_data[pair] = funding_rates_dict[pair].merge(
mark_rates_dict[pair], on='date', how="inner", suffixes=["_fund", "_mark"])
self.futures_data[pair] = self.exchange.combine_funding_and_mark(
funding_rates=funding_rates_dict[pair],
mark_rates=mark_rates_dict[pair],
futures_funding_rate=self.config.get('futures_funding_rate', None),
)
if unavailable_pairs:
raise OperationalException(
@@ -496,7 +501,8 @@ class Backtesting:
stake_available = self.wallets.get_available_stake_amount()
stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
default_retval=None)(
trade=trade, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
trade=trade, # type: ignore[arg-type]
current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
current_profit=current_profit, min_stake=min_stake,
max_stake=min(max_stake, stake_available))
@@ -527,15 +533,23 @@ class Backtesting:
if check_adjust_entry:
trade = self._get_adjust_trade_entry_for_candle(trade, row)
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
exit_ = self.strategy.should_exit(
trade, row[OPEN_IDX], exit_candle_time, # type: ignore
exits = self.strategy.should_exit(
trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(), # type: ignore
enter=enter, exit_=exit_sig,
low=row[LOW_IDX], high=row[HIGH_IDX]
)
for exit_ in exits:
t = self._get_exit_for_signal(trade, row, exit_)
if t:
return t
return None
def _get_exit_for_signal(self, trade: LocalTrade, row: Tuple,
exit_: ExitCheckTuple) -> Optional[LocalTrade]:
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
if exit_.exit_flag:
trade.close_date = exit_candle_time
exit_reason = exit_.exit_reason
@@ -562,7 +576,8 @@ class Backtesting:
if order_type == 'limit':
close_rate = strategy_safe_wrapper(self.strategy.custom_exit_price,
default_retval=close_rate)(
pair=trade.pair, trade=trade,
pair=trade.pair,
trade=trade, # type: ignore[arg-type]
current_time=exit_candle_time,
proposed_rate=close_rate, current_profit=current_profit,
exit_tag=exit_reason)
@@ -576,7 +591,10 @@ class Backtesting:
time_in_force = self.strategy.order_time_in_force['exit']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
pair=trade.pair,
trade=trade, # type: ignore[arg-type]
order_type='limit',
amount=trade.amount,
rate=close_rate,
time_in_force=time_in_force,
sell_reason=exit_reason, # deprecated
@@ -652,7 +670,7 @@ class Backtesting:
return self._get_exit_trade_entry_for_candle(trade, row)
def get_valid_price_and_stake(
self, pair: str, row: Tuple, propose_rate: float, stake_amount: Optional[float],
self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
direction: LongShort, current_time: datetime, entry_tag: Optional[str],
trade: Optional[LocalTrade], order_type: str
) -> Tuple[float, float, float, float]:
@@ -686,7 +704,7 @@ class Backtesting:
current_rate=row[OPEN_IDX],
proposed_leverage=1.0,
max_leverage=max_leverage,
side=direction,
side=direction, entry_tag=entry_tag,
) if self._can_short else 1.0
# Cap leverage between 1.0 and max_leverage.
leverage = min(max(leverage, 1.0), max_leverage)
@@ -726,8 +744,9 @@ class Backtesting:
order_type = self.strategy.order_types['entry']
pos_adjust = trade is not None and requested_rate is None
stake_amount_ = stake_amount or (trade.stake_amount if trade else 0.0)
propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake(
pair, row, row[OPEN_IDX], stake_amount, direction, current_time, entry_tag, trade,
pair, row, row[OPEN_IDX], stake_amount_, direction, current_time, entry_tag, trade,
order_type
)
@@ -876,28 +895,34 @@ class Backtesting:
self.protections.stop_per_pair(pair, current_time, side)
self.protections.global_stop(current_time, side)
def manage_open_orders(self, trade: LocalTrade, current_time, row: Tuple) -> bool:
def manage_open_orders(self, trade: LocalTrade, current_time: datetime, row: Tuple) -> bool:
"""
Check if any open order needs to be cancelled or replaced.
Returns True if the trade should be deleted.
"""
for order in [o for o in trade.orders if o.ft_is_open]:
if self.check_order_cancel(trade, order, current_time):
oc = self.check_order_cancel(trade, order, current_time)
if oc:
# delete trade due to order timeout
return True
elif self.check_order_replace(trade, order, current_time, row):
elif oc is None and self.check_order_replace(trade, order, current_time, row):
# delete trade due to user request
self.canceled_trade_entries += 1
return True
# default maintain trade
return False
def check_order_cancel(self, trade: LocalTrade, order: Order, current_time) -> bool:
def check_order_cancel(
self, trade: LocalTrade, order: Order, current_time: datetime) -> Optional[bool]:
"""
Check if current analyzed order has to be canceled.
Returns True if the trade should be Deleted (initial order was canceled).
Returns True if the trade should be Deleted (initial order was canceled),
False if it's Canceled
None if the order is still active.
"""
timedout = self.strategy.ft_check_timed_out(trade, order, current_time)
timedout = self.strategy.ft_check_timed_out(
trade, # type: ignore[arg-type]
order, current_time)
if timedout:
if order.side == trade.entry_side:
self.timedout_entry_orders += 1
@@ -907,12 +932,15 @@ class Backtesting:
else:
# Close additional entry order
del trade.orders[trade.orders.index(order)]
trade.open_order_id = None
return False
if order.side == trade.exit_side:
self.timedout_exit_orders += 1
# Close exit order and retry exiting on next signal.
del trade.orders[trade.orders.index(order)]
return False
trade.open_order_id = None
return False
return None
def check_order_replace(self, trade: LocalTrade, order: Order, current_time,
row: Tuple) -> bool:
@@ -926,7 +954,8 @@ class Backtesting:
if order.side == trade.entry_side and current_time > order.order_date_utc:
requested_rate = strategy_safe_wrapper(self.strategy.adjust_entry_price,
default_retval=order.price)(
trade=trade, order=order, pair=trade.pair, current_time=current_time,
trade=trade, # type: ignore[arg-type]
order=order, pair=trade.pair, current_time=current_time,
proposed_rate=row[OPEN_IDX], current_order_rate=order.price,
entry_tag=trade.enter_tag, side=trade.trade_direction
) # default value is current order price
@@ -937,6 +966,7 @@ class Backtesting:
return False
else:
del trade.orders[trade.orders.index(order)]
trade.open_order_id = None
self.canceled_entry_orders += 1
# place new order if result was not None
@@ -1025,6 +1055,7 @@ class Backtesting:
# Close trade
open_trade_count -= 1
open_trades[pair].remove(t)
LocalTrade.trades_open.remove(t)
self.wallets.update()
# 2. Process entries.
@@ -1048,6 +1079,8 @@ class Backtesting:
open_trade_count += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
self.wallets.update()
for trade in list(open_trades[pair]):
# 3. Process entry orders.
@@ -1055,7 +1088,6 @@ class Backtesting:
if order and self._get_order_filled(order.price, row):
order.close_bt_order(current_time, trade)
trade.open_order_id = None
LocalTrade.add_bt_trade(trade)
self.wallets.update()
# 4. Create exit orders (if any)
@@ -1065,6 +1097,7 @@ class Backtesting:
# 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True)
if order and self._get_order_filled(order.price, row):
order.close_bt_order(current_time, trade)
trade.open_order_id = None
trade.close_date = current_time
trade.close(order.price, show_msg=False)
@@ -1233,13 +1266,14 @@ class Backtesting:
self.results['strategy_comparison'].extend(results['strategy_comparison'])
else:
self.results = results
dt_appendix = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
if self.config.get('export', 'none') in ('trades', 'signals'):
store_backtest_stats(self.config['exportfilename'], self.results)
store_backtest_stats(self.config['exportfilename'], self.results, dt_appendix)
if (self.config.get('export', 'none') == 'signals' and
self.dataprovider.runmode == RunMode.BACKTEST):
store_backtest_signal_candles(self.config['exportfilename'], self.processed_dfs)
store_backtest_signal_candles(
self.config['exportfilename'], self.processed_dfs, dt_appendix)
# Results may be mixed up now. Sort them so they follow --strategy-list order.
if 'strategy_list' in self.config and len(self.results) > 0:

2
freqtrade/optimize/edge_cli.py
View File

@@ -44,7 +44,7 @@ class EdgeCli:
self.edge._timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
self.strategy.bot_start()
self.strategy.ft_bot_start()
def start(self) -> None:
result = self.edge.calculate(self.config['exchange']['pair_whitelist'])

18
freqtrade/optimize/hyperopt.py
View File

@@ -27,8 +27,7 @@ from freqtrade.misc import deep_merge_dicts, 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_loss_interface import IHyperOptLoss
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
@@ -62,7 +61,6 @@ class Hyperopt:
hyperopt = Hyperopt(config)
hyperopt.start()
"""
custom_hyperopt: IHyperOpt
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
@@ -77,6 +75,7 @@ class Hyperopt:
self.backtesting = Backtesting(self.config)
self.pairlist = self.backtesting.pairlists.whitelist
self.custom_hyperopt: HyperOptAuto
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
@@ -88,7 +87,8 @@ class Hyperopt:
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.custom_hyperoptloss: IHyperOptLoss = 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'])
@@ -429,7 +429,7 @@ class Hyperopt:
return new_list
i = 0
asked_non_tried: List[List[Any]] = []
is_random: List[bool] = []
is_random_non_tried: List[bool] = []
while i < 5 and len(asked_non_tried) < n_points:
if i < 3:
self.opt.cache_ = {}
@@ -438,9 +438,9 @@ class Hyperopt:
else:
asked = unique_list(self.opt.space.rvs(n_samples=n_points * 5))
is_random = [True for _ in range(len(asked))]
is_random += [rand for x, rand in zip(asked, is_random)
if x not in self.opt.Xi
and x not in asked_non_tried]
is_random_non_tried += [rand for x, rand in zip(asked, is_random)
if x not in self.opt.Xi
and x not in asked_non_tried]
asked_non_tried += [x for x in asked
if x not in self.opt.Xi
and x not in asked_non_tried]
@@ -449,7 +449,7 @@ class Hyperopt:
if asked_non_tried:
return (
asked_non_tried[:min(len(asked_non_tried), n_points)],
is_random[:min(len(asked_non_tried), n_points)]
is_random_non_tried[:min(len(asked_non_tried), n_points)]
)
else:
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]

32
freqtrade/optimize/optimize_reports.py
View File

@@ -4,7 +4,6 @@ from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List, Union
from numpy import int64
from pandas import DataFrame, to_datetime
from tabulate import tabulate
@@ -18,21 +17,21 @@ from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
logger = logging.getLogger(__name__)
def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> None:
def store_backtest_stats(
recordfilename: Path, stats: Dict[str, DataFrame], dtappendix: str) -> None:
"""
Stores backtest results
:param recordfilename: Path object, which can either be a filename or a directory.
Filenames will be appended with a timestamp right before the suffix
while for directories, <directory>/backtest-result-<datetime>.json will be used as filename
:param stats: Dataframe containing the backtesting statistics
:param dtappendix: Datetime to use for the filename
"""
if recordfilename.is_dir():
filename = (recordfilename /
f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json')
filename = (recordfilename / f'backtest-result-{dtappendix}.json')
else:
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}'
).with_suffix(recordfilename.suffix)
# Store metadata separately.
@@ -45,7 +44,8 @@ def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> N
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
def store_backtest_signal_candles(recordfilename: Path, candles: Dict[str, Dict]) -> Path:
def store_backtest_signal_candles(
recordfilename: Path, candles: Dict[str, Dict], dtappendix: str) -> Path:
"""
Stores backtest trade signal candles
:param recordfilename: Path object, which can either be a filename or a directory.
@@ -53,14 +53,13 @@ def store_backtest_signal_candles(recordfilename: Path, candles: Dict[str, Dict]
while for directories, <directory>/backtest-result-<datetime>_signals.pkl will be used
as filename
:param stats: Dict containing the backtesting signal candles
:param dtappendix: Datetime to use for the filename
"""
if recordfilename.is_dir():
filename = (recordfilename /
f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}_signals.pkl')
filename = (recordfilename / f'backtest-result-{dtappendix}_signals.pkl')
else:
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}_signals.pkl'
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_signals.pkl'
)
file_dump_joblib(filename, candles)
@@ -417,9 +416,9 @@ def generate_strategy_stats(pairlist: List[str],
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
if not results.empty:
results['open_timestamp'] = results['open_date'].view(int64) // 1e6
results['close_timestamp'] = results['close_date'].view(int64) // 1e6
winning_profit = results.loc[results['profit_abs'] > 0, 'profit_abs'].sum()
losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum()
profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
backtest_days = (max_date - min_date).days or 1
strat_stats = {
@@ -447,6 +446,7 @@ def generate_strategy_stats(pairlist: List[str],
'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
'profit_factor': profit_factor,
'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts': int(min_date.timestamp() * 1000),
'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
@@ -501,8 +501,10 @@ def generate_strategy_stats(pairlist: List[str],
(drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
max_drawdown) = calculate_max_drawdown(
results, value_col='profit_abs', starting_balance=start_balance)
# max_relative_drawdown = Underwater
(_, _, _, _, _, max_relative_drawdown) = calculate_max_drawdown(
results, value_col='profit_abs', starting_balance=start_balance, relative=True)
strat_stats.update({
'max_drawdown': max_drawdown_legacy, # Deprecated - do not use
'max_drawdown_account': max_drawdown,
@@ -781,6 +783,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Total profit %', f"{strat_results['profit_total']:.2%}"),
('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
in strat_results else 'N/A'),
('Trades per day', strat_results['trades_per_day']),
('Avg. daily profit %',
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),

2
freqtrade/persistence/__init__.py
View File

@@ -1,5 +1,5 @@
# flake8: noqa: F401
from freqtrade.persistence.models import clean_dry_run_db, cleanup_db, init_db
from freqtrade.persistence.models import cleanup_db, init_db
from freqtrade.persistence.pairlock_middleware import PairLocks
from freqtrade.persistence.trade_model import LocalTrade, Order, Trade

41
freqtrade/persistence/migrations.py
View File

@@ -201,16 +201,18 @@ def migrate_orders_table(engine, table_back_name: str, cols_order: List):
ft_fee_base = get_column_def(cols_order, 'ft_fee_base', 'null')
average = get_column_def(cols_order, 'average', 'null')
stop_price = get_column_def(cols_order, 'stop_price', 'null')
# sqlite does not support literals for booleans
with engine.begin() as connection:
connection.execute(text(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, ft_fee_base)
stop_price, order_date, order_filled_date, order_update_date, ft_fee_base)
select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
status, symbol, order_type, side, price, amount, filled, {average} average, remaining,
cost, order_date, order_filled_date, order_update_date, {ft_fee_base} ft_fee_base
cost, {stop_price} stop_price, order_date, order_filled_date,
order_update_date, {ft_fee_base} ft_fee_base
from {table_back_name}
"""))
@@ -247,6 +249,35 @@ def set_sqlite_to_wal(engine):
connection.execute(text("PRAGMA journal_mode=wal"))
def fix_old_dry_orders(engine):
with engine.begin() as connection:
connection.execute(
text(
"""
update orders
set ft_is_open = 0
where ft_is_open = 1 and (ft_trade_id, order_id) not in (
select id, stoploss_order_id from trades where stoploss_order_id is not null
) and ft_order_side = 'stoploss'
and order_id like 'dry_%'
"""
)
)
connection.execute(
text(
"""
update orders
set ft_is_open = 0
where ft_is_open = 1
and (ft_trade_id, order_id) not in (
select id, open_order_id from trades where open_order_id is not null
) and ft_order_side != 'stoploss'
and order_id like 'dry_%'
"""
)
)
def check_migrate(engine, decl_base, previous_tables) -> None:
"""
Checks if migration is necessary and migrates if necessary
@@ -265,9 +296,8 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
# Check if migration necessary
# Migrates both trades and orders table!
# if ('orders' not in previous_tables
# or not has_column(cols_orders, 'leverage')):
if not has_column(cols_trades, 'base_currency'):
if not has_column(cols_orders, 'stop_price'):
# if not has_column(cols_trades, 'base_currency'):
logger.info(f"Running database migration for trades - "
f"backup: {table_back_name}, {order_table_bak_name}")
migrate_trades_and_orders_table(
@@ -288,3 +318,4 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
"start with a fresh database.")
set_sqlite_to_wal(engine)
fix_old_dry_orders(engine)

20
freqtrade/persistence/models.py
View File

@@ -21,14 +21,12 @@ logger = logging.getLogger(__name__)
_SQL_DOCS_URL = 'http://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls'
def init_db(db_url: str, clean_open_orders: bool = False) -> None:
def init_db(db_url: str) -> None:
"""
Initializes this module with the given config,
registers all known command handlers
and starts polling for message updates
:param db_url: Database to use
:param clean_open_orders: Remove open orders from the database.
Useful for dry-run or if all orders have been reset on the exchange.
:return: None
"""
kwargs = {}
@@ -64,10 +62,6 @@ def init_db(db_url: str, clean_open_orders: bool = False) -> None:
_DECL_BASE.metadata.create_all(engine)
check_migrate(engine, decl_base=_DECL_BASE, previous_tables=previous_tables)
# Clean dry_run DB if the db is not in-memory
if clean_open_orders and db_url != 'sqlite://':
clean_dry_run_db()
def cleanup_db() -> None:
"""
@@ -75,15 +69,3 @@ def cleanup_db() -> None:
:return: None
"""
Trade.commit()
def clean_dry_run_db() -> None:
"""
Remove open_order_id from a Dry_run DB
:return: None
"""
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
# Check we are updating only a dry_run order not a prod one
if 'dry_run' in trade.open_order_id:
trade.open_order_id = None
Trade.commit()

214
freqtrade/persistence/trade_model.py
View File

@@ -8,7 +8,7 @@ from typing import Any, Dict, List, Optional
from sqlalchemy import (Boolean, Column, DateTime, Enum, Float, ForeignKey, Integer, String,
UniqueConstraint, desc, func)
from sqlalchemy.orm import Query, relationship
from sqlalchemy.orm import Query, lazyload, relationship
from freqtrade.constants import DATETIME_PRINT_FORMAT, NON_OPEN_EXCHANGE_STATES, BuySell, LongShort
from freqtrade.enums import ExitType, TradingMode
@@ -57,6 +57,7 @@ class Order(_DECL_BASE):
filled = Column(Float, nullable=True)
remaining = Column(Float, nullable=True)
cost = Column(Float, nullable=True)
stop_price = Column(Float, nullable=True)
order_date = Column(DateTime, nullable=True, default=datetime.utcnow)
order_filled_date = Column(DateTime, nullable=True)
order_update_date = Column(DateTime, nullable=True)
@@ -74,7 +75,7 @@ class Order(_DECL_BASE):
@property
def safe_filled(self) -> float:
return self.filled or self.amount or 0.0
return self.filled if self.filled is not None else self.amount or 0.0
@property
def safe_fee_base(self) -> float:
@@ -107,6 +108,7 @@ class Order(_DECL_BASE):
self.average = order.get('average', self.average)
self.remaining = order.get('remaining', self.remaining)
self.cost = order.get('cost', self.cost)
self.stop_price = order.get('stopPrice', self.stop_price)
if 'timestamp' in order and order['timestamp'] is not None:
self.order_date = datetime.fromtimestamp(order['timestamp'] / 1000, tz=timezone.utc)
@@ -118,35 +120,60 @@ class Order(_DECL_BASE):
self.order_filled_date = datetime.now(timezone.utc)
self.order_update_date = datetime.now(timezone.utc)
def to_json(self, entry_side: str) -> Dict[str, Any]:
def to_ccxt_object(self) -> Dict[str, Any]:
return {
'pair': self.ft_pair,
'order_id': self.order_id,
'status': self.status,
'id': self.order_id,
'symbol': self.ft_pair,
'price': self.price,
'average': self.average,
'amount': self.amount,
'average': round(self.average, 8) if self.average else 0,
'safe_price': self.safe_price,
'cost': self.cost if self.cost else 0,
'cost': self.cost,
'type': self.order_type,
'side': self.ft_order_side,
'filled': self.filled,
'remaining': self.remaining,
'stopPrice': self.stop_price,
'datetime': self.order_date_utc.strftime('%Y-%m-%dT%H:%M:%S.%f'),
'timestamp': int(self.order_date_utc.timestamp() * 1000),
'status': self.status,
'fee': None,
'info': {},
}
def to_json(self, entry_side: str, minified: bool = False) -> Dict[str, Any]:
resp = {
'amount': self.amount,
'safe_price': self.safe_price,
'ft_order_side': self.ft_order_side,
'is_open': self.ft_is_open,
'order_date': self.order_date.strftime(DATETIME_PRINT_FORMAT)
if self.order_date else None,
'order_timestamp': int(self.order_date.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.order_date else None,
'order_filled_date': self.order_filled_date.strftime(DATETIME_PRINT_FORMAT)
if self.order_filled_date else None,
'order_filled_timestamp': int(self.order_filled_date.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.order_filled_date else None,
'order_type': self.order_type,
'price': self.price,
'ft_is_entry': self.ft_order_side == entry_side,
'remaining': self.remaining,
}
if not minified:
resp.update({
'pair': self.ft_pair,
'order_id': self.order_id,
'status': self.status,
'average': round(self.average, 8) if self.average else 0,
'cost': self.cost if self.cost else 0,
'filled': self.filled,
'is_open': self.ft_is_open,
'order_date': self.order_date.strftime(DATETIME_PRINT_FORMAT)
if self.order_date else None,
'order_timestamp': int(self.order_date.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.order_date else None,
'order_filled_date': self.order_filled_date.strftime(DATETIME_PRINT_FORMAT)
if self.order_filled_date else None,
'order_type': self.order_type,
'price': self.price,
'remaining': self.remaining,
})
return resp
def close_bt_order(self, close_date: datetime, trade: 'LocalTrade'):
self.order_filled_date = close_date
self.filled = self.amount
self.remaining = 0
self.status = 'closed'
self.ft_is_open = False
if (self.ft_order_side == trade.entry_side
@@ -190,6 +217,14 @@ class Order(_DECL_BASE):
"""
return Order.query.filter(Order.ft_is_open.is_(True)).all()
@staticmethod
def order_by_id(order_id: str) -> Optional['Order']:
"""
Retrieve order based on order_id
:return: Order or None
"""
return Order.query.filter(Order.order_id == order_id).first()
class LocalTrade():
"""
@@ -366,9 +401,9 @@ class LocalTrade():
f'open_rate={self.open_rate:.8f}, open_since={open_since})'
)
def to_json(self) -> Dict[str, Any]:
filled_orders = self.select_filled_orders()
orders = [order.to_json(self.entry_side) for order in filled_orders]
def to_json(self, minified: bool = False) -> Dict[str, Any]:
filled_orders = self.select_filled_or_open_orders()
orders = [order.to_json(self.entry_side, minified) for order in filled_orders]
return {
'trade_id': self.id,
@@ -592,8 +627,8 @@ class LocalTrade():
"""
self.close_rate = rate
self.close_date = self.close_date or datetime.utcnow()
self.close_profit = self.calc_profit_ratio()
self.close_profit_abs = self.calc_profit()
self.close_profit = self.calc_profit_ratio(rate)
self.close_profit_abs = self.calc_profit(rate)
self.is_open = False
self.exit_order_status = 'closed'
self.open_order_id = None
@@ -661,10 +696,9 @@ class LocalTrade():
"""
self.open_trade_value = self._calc_open_trade_value()
def calculate_interest(self, interest_rate: Optional[float] = None) -> Decimal:
def calculate_interest(self) -> Decimal:
"""
:param interest_rate: interest_charge for borrowing this coin(optional).
If interest_rate is not set self.interest_rate will be used
Calculate interest for this trade. Only applicable for Margin trading.
"""
zero = Decimal(0.0)
# If nothing was borrowed
@@ -677,34 +711,26 @@ class LocalTrade():
total_seconds = Decimal((now - open_date).total_seconds())
hours = total_seconds / sec_per_hour or zero
rate = Decimal(interest_rate or self.interest_rate)
rate = Decimal(self.interest_rate)
borrowed = Decimal(self.borrowed)
return interest(exchange_name=self.exchange, borrowed=borrowed, rate=rate, hours=hours)
def _calc_base_close(self, amount: Decimal, rate: Optional[float] = None,
fee: Optional[float] = None) -> Decimal:
def _calc_base_close(self, amount: Decimal, rate: float, fee: float) -> Decimal:
close_trade = Decimal(amount) * Decimal(rate or self.close_rate) # type: ignore
fees = close_trade * Decimal(fee or self.fee_close)
close_trade = amount * Decimal(rate)
fees = close_trade * Decimal(fee)
if self.is_short:
return close_trade + fees
else:
return close_trade - fees
def calc_close_trade_value(self, rate: Optional[float] = None,
fee: Optional[float] = None,
interest_rate: Optional[float] = None) -> float:
def calc_close_trade_value(self, rate: float) -> float:
"""
Calculate the close_rate including fee
:param fee: fee to use on the close rate (optional).
If rate is not set self.fee will be used
:param rate: rate to compare with (optional).
If rate is not set self.close_rate will be used
:param interest_rate: interest_charge for borrowing this coin (optional).
If interest_rate is not set self.interest_rate will be used
:return: Price in BTC of the open trade
Calculate the Trade's close value including fees
:param rate: rate to compare with.
:return: value in stake currency of the open trade
"""
if rate is None and not self.close_rate:
return 0.0
@@ -713,49 +739,38 @@ class LocalTrade():
trading_mode = self.trading_mode or TradingMode.SPOT
if trading_mode == TradingMode.SPOT:
return float(self._calc_base_close(amount, rate, fee))
return float(self._calc_base_close(amount, rate, self.fee_close))
elif (trading_mode == TradingMode.MARGIN):
total_interest = self.calculate_interest(interest_rate)
total_interest = self.calculate_interest()
if self.is_short:
amount = amount + total_interest
return float(self._calc_base_close(amount, rate, fee))
return float(self._calc_base_close(amount, rate, self.fee_close))
else:
# Currency already owned for longs, no need to purchase
return float(self._calc_base_close(amount, rate, fee) - total_interest)
return float(self._calc_base_close(amount, rate, self.fee_close) - total_interest)
elif (trading_mode == TradingMode.FUTURES):
funding_fees = self.funding_fees or 0.0
# Positive funding_fees -> Trade has gained from fees.
# Negative funding_fees -> Trade had to pay the fees.
if self.is_short:
return float(self._calc_base_close(amount, rate, fee)) - funding_fees
return float(self._calc_base_close(amount, rate, self.fee_close)) - funding_fees
else:
return float(self._calc_base_close(amount, rate, fee)) + funding_fees
return float(self._calc_base_close(amount, rate, self.fee_close)) + funding_fees
else:
raise OperationalException(
f"{self.trading_mode.value} trading is not yet available using freqtrade")
def calc_profit(self, rate: Optional[float] = None,
fee: Optional[float] = None,
interest_rate: Optional[float] = None) -> float:
def calc_profit(self, rate: float) -> float:
"""
Calculate the absolute profit in stake currency between Close and Open trade
:param fee: fee to use on the close rate (optional).
If fee is not set self.fee will be used
:param rate: close rate to compare with (optional).
If rate is not set self.close_rate will be used
:param interest_rate: interest_charge for borrowing this coin (optional).
If interest_rate is not set self.interest_rate will be used
:return: profit in stake currency as float
:param rate: close rate to compare with.
:return: profit in stake currency as float
"""
close_trade_value = self.calc_close_trade_value(
rate=(rate or self.close_rate),
fee=(fee or self.fee_close),
interest_rate=(interest_rate or self.interest_rate)
)
close_trade_value = self.calc_close_trade_value(rate)
if self.is_short:
profit = self.open_trade_value - close_trade_value
@@ -763,23 +778,13 @@ class LocalTrade():
profit = close_trade_value - self.open_trade_value
return float(f"{profit:.8f}")
def calc_profit_ratio(self, rate: Optional[float] = None,
fee: Optional[float] = None,
interest_rate: Optional[float] = None) -> float:
def calc_profit_ratio(self, rate: float) -> float:
"""
Calculates the profit as ratio (including fee).
:param rate: rate to compare with (optional).
If rate is not set self.close_rate will be used
:param fee: fee to use on the close rate (optional).
:param interest_rate: interest_charge for borrowing this coin (optional).
If interest_rate is not set self.interest_rate will be used
:param rate: rate to compare with.
:return: profit ratio as float
"""
close_trade_value = self.calc_close_trade_value(
rate=(rate or self.close_rate),
fee=(fee or self.fee_close),
interest_rate=(interest_rate or self.interest_rate)
)
close_trade_value = self.calc_close_trade_value(rate)
short_close_zero = (self.is_short and close_trade_value == 0.0)
long_close_zero = (not self.is_short and self.open_trade_value == 0.0)
@@ -796,14 +801,6 @@ class LocalTrade():
return float(f"{profit_ratio:.8f}")
def recalc_trade_from_orders(self):
# We need at least 2 entry orders for averaging amounts and rates.
# TODO: this condition could probably be removed
if len(self.select_filled_orders(self.entry_side)) < 2:
self.stake_amount = self.amount * self.open_rate / self.leverage
# Just in case, still recalc open trade value
self.recalc_open_trade_value()
return
total_amount = 0.0
total_stake = 0.0
@@ -815,8 +812,6 @@ class LocalTrade():
tmp_amount = o.safe_amount_after_fee
tmp_price = o.average or o.price
if o.filled is not None:
tmp_amount = o.filled
if tmp_amount > 0.0 and tmp_price is not None:
total_amount += tmp_amount
total_stake += tmp_price * tmp_amount
@@ -841,8 +836,8 @@ class LocalTrade():
return o
return None
def select_order(
self, order_side: str = None, is_open: Optional[bool] = None) -> Optional[Order]:
def select_order(self, order_side: Optional[str] = None,
is_open: Optional[bool] = None) -> Optional[Order]:
"""
Finds latest order for this orderside and status
:param order_side: ft_order_side of the order (either 'buy', 'sell' or 'stoploss')
@@ -870,6 +865,21 @@ class LocalTrade():
(o.filled or 0) > 0 and
o.status in NON_OPEN_EXCHANGE_STATES]
def select_filled_or_open_orders(self) -> List['Order']:
"""
Finds filled or open orders
:param order_side: Side of the order (either 'buy', 'sell', or None)
:return: array of Order objects
"""
return [o for o in self.orders if
(
o.ft_is_open is False
and (o.filled or 0) > 0
and o.status in NON_OPEN_EXCHANGE_STATES
)
or (o.ft_is_open is True and o.status is not None)
]
@property
def nr_of_successful_entries(self) -> int:
"""
@@ -1108,7 +1118,7 @@ class Trade(_DECL_BASE, LocalTrade):
)
@staticmethod
def get_trades(trade_filter=None) -> Query:
def get_trades(trade_filter=None, include_orders: bool = True) -> Query:
"""
Helper function to query Trades using filters.
NOTE: Not supported in Backtesting.
@@ -1123,9 +1133,14 @@ class Trade(_DECL_BASE, LocalTrade):
if trade_filter is not None:
if not isinstance(trade_filter, list):
trade_filter = [trade_filter]
return Trade.query.filter(*trade_filter)
this_query = Trade.query.filter(*trade_filter)
else:
return Trade.query
this_query = Trade.query
if not include_orders:
# Don't load order relations
# Consider using noload or raiseload instead of lazyload
this_query = this_query.options(lazyload(Trade.orders))
return this_query
@staticmethod
def get_open_order_trades() -> List['Trade']:
@@ -1345,3 +1360,18 @@ class Trade(_DECL_BASE, LocalTrade):
.group_by(Trade.pair) \
.order_by(desc('profit_sum')).first()
return best_pair
@staticmethod
def get_trading_volume(start_date: datetime = datetime.fromtimestamp(0)) -> float:
"""
Get Trade volume based on Orders
NOTE: Not supported in Backtesting.
:returns: Tuple containing (pair, profit_sum)
"""
trading_volume = Order.query.with_entities(
func.sum(Order.cost).label('volume')
).filter(
Order.order_filled_date >= start_date,
Order.status == 'closed'
).scalar()
return trading_volume

2
freqtrade/plot/plotting.py
View File

@@ -633,7 +633,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
IStrategy.dp = DataProvider(config, exchange)
strategy.bot_start()
strategy.ft_bot_start()
strategy.bot_loop_start()
plot_elements = init_plotscript(config, list(exchange.markets), strategy.startup_candle_count)
timerange = plot_elements['timerange']

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

@@ -50,7 +50,7 @@ class SpreadFilter(IPairList):
: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']:
if 'bid' in ticker and 'ask' in ticker and ticker['ask'] and ticker['bid']:
spread = 1 - ticker['bid'] / ticker['ask']
if spread > self._max_spread_ratio:
self.log_once(f"Removed {pair} from whitelist, because spread "

21
freqtrade/resolvers/strategy_resolver.py
View File

@@ -47,26 +47,7 @@ class StrategyResolver(IResolver):
strategy: IStrategy = StrategyResolver._load_strategy(
strategy_name, config=config,
extra_dir=config.get('strategy_path'))
if strategy._ft_params_from_file:
# Set parameters from Hyperopt results file
params = strategy._ft_params_from_file
strategy.minimal_roi = params.get('roi', getattr(strategy, 'minimal_roi', {}))
strategy.stoploss = params.get('stoploss', {}).get(
'stoploss', getattr(strategy, 'stoploss', -0.1))
trailing = params.get('trailing', {})
strategy.trailing_stop = trailing.get(
'trailing_stop', getattr(strategy, 'trailing_stop', False))
strategy.trailing_stop_positive = trailing.get(
'trailing_stop_positive', getattr(strategy, 'trailing_stop_positive', None))
strategy.trailing_stop_positive_offset = trailing.get(
'trailing_stop_positive_offset',
getattr(strategy, 'trailing_stop_positive_offset', 0))
strategy.trailing_only_offset_is_reached = trailing.get(
'trailing_only_offset_is_reached',
getattr(strategy, 'trailing_only_offset_is_reached', 0.0))
strategy.ft_load_params_from_file()
# Set attributes
# Check if we need to override configuration
# (Attribute name, default, subkey)

6
freqtrade/rpc/api_server/api_backtest.py
View File

@@ -1,6 +1,7 @@
import asyncio
import logging
from copy import deepcopy
from datetime import datetime
from typing import Any, Dict, List
from fastapi import APIRouter, BackgroundTasks, Depends
@@ -102,7 +103,10 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
min_date=min_date, max_date=max_date)
if btconfig.get('export', 'none') == 'trades':
store_backtest_stats(btconfig['exportfilename'], ApiServer._bt.results)
store_backtest_stats(
btconfig['exportfilename'], ApiServer._bt.results,
datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)
logger.info("Backtest finished.")

10
freqtrade/rpc/api_server/api_schemas.py
View File

@@ -104,6 +104,10 @@ class Profit(BaseModel):
best_pair_profit_ratio: float
winning_trades: int
losing_trades: int
profit_factor: float
max_drawdown: float
max_drawdown_abs: float
trading_volume: Optional[float]
class SellReason(BaseModel):
@@ -120,6 +124,8 @@ class Stats(BaseModel):
class DailyRecord(BaseModel):
date: date
abs_profit: float
rel_profit: float
starting_balance: float
fiat_value: float
trade_count: int
@@ -166,7 +172,7 @@ class ShowConfig(BaseModel):
trailing_stop_positive: Optional[float]
trailing_stop_positive_offset: Optional[float]
trailing_only_offset_is_reached: Optional[bool]
unfilledtimeout: UnfilledTimeout
unfilledtimeout: Optional[UnfilledTimeout] # Empty in webserver mode
order_types: Optional[OrderTypes]
use_custom_stoploss: Optional[bool]
timeframe: Optional[str]
@@ -256,6 +262,7 @@ class TradeSchema(BaseModel):
leverage: Optional[float]
interest_rate: Optional[float]
liquidation_price: Optional[float]
funding_fees: Optional[float]
trading_mode: Optional[TradingMode]
@@ -276,6 +283,7 @@ class OpenTradeSchema(TradeSchema):
class TradeResponse(BaseModel):
trades: List[TradeSchema]
trades_count: int
offset: int
total_trades: int

7
freqtrade/rpc/api_server/api_v1.py
View File

@@ -36,7 +36,8 @@ logger = logging.getLogger(__name__)
# versions 2.xx -> futures/short branch
# 2.14: Add entry/exit orders to trade response
# 2.15: Add backtest history endpoints
API_VERSION = 2.15
# 2.16: Additional daily metrics
API_VERSION = 2.16
# Public API, requires no auth.
router_public = APIRouter()
@@ -86,8 +87,8 @@ def stats(rpc: RPC = Depends(get_rpc)):
@router.get('/daily', response_model=Daily, tags=['info'])
def daily(timescale: int = 7, rpc: RPC = Depends(get_rpc), config=Depends(get_config)):
return rpc._rpc_daily_profit(timescale, config['stake_currency'],
config.get('fiat_display_currency', ''))
return rpc._rpc_timeunit_profit(timescale, config['stake_currency'],
config.get('fiat_display_currency', ''))
@router.get('/status', response_model=List[OpenTradeSchema], tags=['info'])

59
freqtrade/rpc/discord.py Normal file
View File

@@ -0,0 +1,59 @@
import logging
from typing import Any, Dict
from freqtrade.enums.rpcmessagetype import RPCMessageType
from freqtrade.rpc import RPC
from freqtrade.rpc.webhook import Webhook
logger = logging.getLogger(__name__)
class Discord(Webhook):
def __init__(self, rpc: 'RPC', config: Dict[str, Any]):
# super().__init__(rpc, config)
self.rpc = rpc
self.config = config
self.strategy = config.get('strategy', '')
self.timeframe = config.get('timeframe', '')
self._url = self.config['discord']['webhook_url']
self._format = 'json'
self._retries = 1
self._retry_delay = 0.1
def cleanup(self) -> None:
"""
Cleanup pending module resources.
This will do nothing for webhooks, they will simply not be called anymore
"""
pass
def send_msg(self, msg) -> None:
logger.info(f"Sending discord message: {msg}")
if msg['type'].value in self.config['discord']:
msg['strategy'] = self.strategy
msg['timeframe'] = self.timeframe
fields = self.config['discord'].get(msg['type'].value)
color = 0x0000FF
if msg['type'] in (RPCMessageType.EXIT, RPCMessageType.EXIT_FILL):
profit_ratio = msg.get('profit_ratio')
color = (0x00FF00 if profit_ratio > 0 else 0xFF0000)
embeds = [{
'title': f"Trade: {msg['pair']} {msg['type'].value}",
'color': color,
'fields': [],
}]
for f in fields:
for k, v in f.items():
v = v.format(**msg)
embeds[0]['fields'].append( # type: ignore
{'name': k, 'value': v, 'inline': True})
# Send the message to discord channel
payload = {'embeds': embeds}
self._send_msg(payload)

178
freqtrade/rpc/rpc.py
View File

@@ -18,6 +18,7 @@ from freqtrade import __version__
from freqtrade.configuration.timerange import TimeRange
from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT
from freqtrade.data.history import load_data
from freqtrade.data.metrics import calculate_max_drawdown
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, SignalDirection, State,
TradingMode)
from freqtrade.exceptions import ExchangeError, PricingError
@@ -283,33 +284,57 @@ class RPC:
columns.append('# Entries')
return trades_list, columns, fiat_profit_sum
def _rpc_daily_profit(
def _rpc_timeunit_profit(
self, timescale: int,
stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
today = datetime.now(timezone.utc).date()
profit_days: Dict[date, Dict] = {}
stake_currency: str, fiat_display_currency: str,
timeunit: str = 'days') -> Dict[str, Any]:
"""
:param timeunit: Valid entries are 'days', 'weeks', 'months'
"""
start_date = datetime.now(timezone.utc).date()
if timeunit == 'weeks':
# weekly
start_date = start_date - timedelta(days=start_date.weekday()) # Monday
if timeunit == 'months':
start_date = start_date.replace(day=1)
def time_offset(step: int):
if timeunit == 'months':
return relativedelta(months=step)
return timedelta(**{timeunit: step})
if not (isinstance(timescale, int) and timescale > 0):
raise RPCException('timescale must be an integer greater than 0')
profit_units: Dict[date, Dict] = {}
daily_stake = self._freqtrade.wallets.get_total_stake_amount()
for day in range(0, timescale):
profitday = today - timedelta(days=day)
trades = Trade.get_trades(trade_filter=[
profitday = start_date - time_offset(day)
# Only query for necessary columns for performance reasons.
trades = Trade.query.session.query(Trade.close_profit_abs).filter(
Trade.is_open.is_(False),
Trade.close_date >= profitday,
Trade.close_date < (profitday + timedelta(days=1))
]).order_by(Trade.close_date).all()
Trade.close_date < (profitday + time_offset(1))
).order_by(Trade.close_date).all()
curdayprofit = sum(
trade.close_profit_abs for trade in trades if trade.close_profit_abs is not None)
profit_days[profitday] = {
# Calculate this periods starting balance
daily_stake = daily_stake - curdayprofit
profit_units[profitday] = {
'amount': curdayprofit,
'trades': len(trades)
'daily_stake': daily_stake,
'rel_profit': round(curdayprofit / daily_stake, 8) if daily_stake > 0 else 0,
'trades': len(trades),
}
data = [
{
'date': key,
'date': f"{key.year}-{key.month:02d}" if timeunit == 'months' else key,
'abs_profit': value["amount"],
'starting_balance': value["daily_stake"],
'rel_profit': value["rel_profit"],
'fiat_value': self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
@@ -317,92 +342,7 @@ class RPC:
) if self._fiat_converter else 0,
'trade_count': value["trades"],
}
for key, value in profit_days.items()
]
return {
'stake_currency': stake_currency,
'fiat_display_currency': fiat_display_currency,
'data': data
}
def _rpc_weekly_profit(
self, timescale: int,
stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
today = datetime.now(timezone.utc).date()
first_iso_day_of_week = today - timedelta(days=today.weekday()) # Monday
profit_weeks: Dict[date, Dict] = {}
if not (isinstance(timescale, int) and timescale > 0):
raise RPCException('timescale must be an integer greater than 0')
for week in range(0, timescale):
profitweek = first_iso_day_of_week - timedelta(weeks=week)
trades = Trade.get_trades(trade_filter=[
Trade.is_open.is_(False),
Trade.close_date >= profitweek,
Trade.close_date < (profitweek + timedelta(weeks=1))
]).order_by(Trade.close_date).all()
curweekprofit = sum(
trade.close_profit_abs for trade in trades if trade.close_profit_abs is not None)
profit_weeks[profitweek] = {
'amount': curweekprofit,
'trades': len(trades)
}
data = [
{
'date': key,
'abs_profit': value["amount"],
'fiat_value': self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
fiat_display_currency
) if self._fiat_converter else 0,
'trade_count': value["trades"],
}
for key, value in profit_weeks.items()
]
return {
'stake_currency': stake_currency,
'fiat_display_currency': fiat_display_currency,
'data': data
}
def _rpc_monthly_profit(
self, timescale: int,
stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
first_day_of_month = datetime.now(timezone.utc).date().replace(day=1)
profit_months: Dict[date, Dict] = {}
if not (isinstance(timescale, int) and timescale > 0):
raise RPCException('timescale must be an integer greater than 0')
for month in range(0, timescale):
profitmonth = first_day_of_month - relativedelta(months=month)
trades = Trade.get_trades(trade_filter=[
Trade.is_open.is_(False),
Trade.close_date >= profitmonth,
Trade.close_date < (profitmonth + relativedelta(months=1))
]).order_by(Trade.close_date).all()
curmonthprofit = sum(
trade.close_profit_abs for trade in trades if trade.close_profit_abs is not None)
profit_months[profitmonth] = {
'amount': curmonthprofit,
'trades': len(trades)
}
data = [
{
'date': f"{key.year}-{key.month:02d}",
'abs_profit': value["amount"],
'fiat_value': self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
fiat_display_currency
) if self._fiat_converter else 0,
'trade_count': value["trades"],
}
for key, value in profit_months.items()
for key, value in profit_units.items()
]
return {
'stake_currency': stake_currency,
@@ -425,6 +365,7 @@ class RPC:
return {
"trades": output,
"trades_count": len(output),
"offset": offset,
"total_trades": Trade.get_trades([Trade.is_open.is_(False)]).count(),
}
@@ -439,7 +380,7 @@ class RPC:
return 'losses'
else:
return 'draws'
trades: List[Trade] = Trade.get_trades([Trade.is_open.is_(False)])
trades: List[Trade] = Trade.get_trades([Trade.is_open.is_(False)], include_orders=False)
# Sell reason
exit_reasons = {}
for trade in trades:
@@ -467,7 +408,8 @@ class RPC:
""" Returns cumulative profit statistics """
trade_filter = ((Trade.is_open.is_(False) & (Trade.close_date >= start_date)) |
Trade.is_open.is_(True))
trades: List[Trade] = Trade.get_trades(trade_filter).order_by(Trade.id).all()
trades: List[Trade] = Trade.get_trades(
trade_filter, include_orders=False).order_by(Trade.id).all()
profit_all_coin = []
profit_all_ratio = []
@@ -476,6 +418,8 @@ class RPC:
durations = []
winning_trades = 0
losing_trades = 0
winning_profit = 0.0
losing_profit = 0.0
for trade in trades:
current_rate: float = 0.0
@@ -491,8 +435,10 @@ class RPC:
profit_closed_ratio.append(profit_ratio)
if trade.close_profit >= 0:
winning_trades += 1
winning_profit += trade.close_profit_abs
else:
losing_trades += 1
losing_profit += trade.close_profit_abs
else:
# Get current rate
try:
@@ -508,6 +454,7 @@ class RPC:
profit_all_ratio.append(profit_ratio)
best_pair = Trade.get_best_pair(start_date)
trading_volume = Trade.get_trading_volume(start_date)
# Prepare data to display
profit_closed_coin_sum = round(sum(profit_closed_coin), 8)
@@ -531,6 +478,21 @@ class RPC:
profit_closed_ratio_fromstart = profit_closed_coin_sum / starting_balance
profit_all_ratio_fromstart = profit_all_coin_sum / starting_balance
profit_factor = winning_profit / abs(losing_profit) if losing_profit else float('inf')
trades_df = DataFrame([{'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT),
'profit_abs': trade.close_profit_abs}
for trade in trades if not trade.is_open])
max_drawdown_abs = 0.0
max_drawdown = 0.0
if len(trades_df) > 0:
try:
(max_drawdown_abs, _, _, _, _, max_drawdown) = calculate_max_drawdown(
trades_df, value_col='profit_abs', starting_balance=starting_balance)
except ValueError:
# ValueError if no losing trade.
pass
profit_all_fiat = self._fiat_converter.convert_amount(
profit_all_coin_sum,
stake_currency,
@@ -569,11 +531,15 @@ class RPC:
'best_pair_profit_ratio': best_pair[1] if best_pair else 0,
'winning_trades': winning_trades,
'losing_trades': losing_trades,
'profit_factor': profit_factor,
'max_drawdown': max_drawdown,
'max_drawdown_abs': max_drawdown_abs,
'trading_volume': trading_volume,
}
def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
""" Returns current account balance per crypto """
currencies = []
currencies: List[Dict] = []
total = 0.0
try:
tickers = self._freqtrade.exchange.get_tickers(cached=True)
@@ -608,13 +574,12 @@ class RPC:
except (ExchangeError):
logger.warning(f" Could not get rate for pair {coin}.")
continue
total = total + (est_stake or 0)
total = total + est_stake
currencies.append({
'currency': coin,
# TODO: The below can be simplified if we don't assign None to values.
'free': balance.free if balance.free is not None else 0,
'balance': balance.total if balance.total is not None else 0,
'used': balance.used if balance.used is not None else 0,
'free': balance.free,
'balance': balance.total,
'used': balance.used,
'est_stake': est_stake or 0,
'stake': stake_currency,
'side': 'long',
@@ -644,7 +609,6 @@ class RPC:
total, stake_currency, fiat_display_currency) if self._fiat_converter else 0
trade_count = len(Trade.get_trades_proxy())
starting_capital_ratio = 0.0
starting_capital_ratio = (total / starting_capital) - 1 if starting_capital else 0.0
starting_cap_fiat_ratio = (value / starting_cap_fiat) - 1 if starting_cap_fiat else 0.0
@@ -932,7 +896,7 @@ class RPC:
else:
errors[pair] = {
'error_msg': f"Pair {pair} is not in the current blacklist."
}
}
resp = self._rpc_blacklist()
resp['errors'] = errors
return resp

6
freqtrade/rpc/rpc_manager.py
View File

@@ -27,6 +27,12 @@ class RPCManager:
from freqtrade.rpc.telegram import Telegram
self.registered_modules.append(Telegram(self._rpc, config))
# Enable discord
if config.get('discord', {}).get('enabled', False):
logger.info('Enabling rpc.discord ...')
from freqtrade.rpc.discord import Discord
self.registered_modules.append(Discord(self._rpc, config))
# Enable Webhook
if config.get('webhook', {}).get('enabled', False):
logger.info('Enabling rpc.webhook ...')

224
freqtrade/rpc/telegram.py
View File

@@ -6,6 +6,7 @@ This module manage Telegram communication
import json
import logging
import re
from dataclasses import dataclass
from datetime import date, datetime, timedelta
from functools import partial
from html import escape
@@ -37,6 +38,15 @@ logger.debug('Included module rpc.telegram ...')
MAX_TELEGRAM_MESSAGE_LENGTH = 4096
@dataclass
class TimeunitMappings:
header: str
message: str
message2: str
callback: str
default: int
def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
"""
Decorator to check if the message comes from the correct chat_id
@@ -225,6 +235,14 @@ class Telegram(RPCHandler):
# This can take up to `timeout` from the call to `start_polling`.
self._updater.stop()
def _exchange_from_msg(self, msg: Dict[str, Any]) -> str:
"""
Extracts the exchange name from the given message.
:param msg: The message to extract the exchange name from.
:return: The exchange name.
"""
return f"{msg['exchange']}{' (dry)' if self._config['dry_run'] else ''}"
def _format_entry_msg(self, msg: Dict[str, Any]) -> str:
if self._rpc._fiat_converter:
msg['stake_amount_fiat'] = self._rpc._fiat_converter.convert_amount(
@@ -237,7 +255,7 @@ class Telegram(RPCHandler):
entry_side = ({'enter': 'Long', 'entered': 'Longed'} if msg['direction'] == 'Long'
else {'enter': 'Short', 'entered': 'Shorted'})
message = (
f"{emoji} *{msg['exchange']}:*"
f"{emoji} *{self._exchange_from_msg(msg)}:*"
f" {entry_side['entered'] if is_fill else entry_side['enter']} {msg['pair']}"
f" (#{msg['trade_id']})\n"
)
@@ -286,7 +304,7 @@ class Telegram(RPCHandler):
msg['profit_extra'] = ''
is_fill = msg['type'] == RPCMessageType.EXIT_FILL
message = (
f"{msg['emoji']} *{msg['exchange']}:* "
f"{msg['emoji']} *{self._exchange_from_msg(msg)}:* "
f"{'Exited' if is_fill else 'Exiting'} {msg['pair']} (#{msg['trade_id']})\n"
f"*{'Profit' if is_fill else 'Unrealized Profit'}:* "
f"`{msg['profit_ratio']:.2%}{msg['profit_extra']}`\n"
@@ -316,33 +334,33 @@ class Telegram(RPCHandler):
elif msg_type in (RPCMessageType.ENTRY_CANCEL, RPCMessageType.EXIT_CANCEL):
msg['message_side'] = 'enter' if msg_type in [RPCMessageType.ENTRY_CANCEL] else 'exit'
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling {message_side} Order for {pair} (#{trade_id}). "
"Reason: {reason}.".format(**msg))
message = (f"\N{WARNING SIGN} *{self._exchange_from_msg(msg)}:* "
f"Cancelling {msg['message_side']} Order for {msg['pair']} "
f"(#{msg['trade_id']}). Reason: {msg['reason']}.")
elif msg_type == RPCMessageType.PROTECTION_TRIGGER:
message = (
"*Protection* triggered due to {reason}. "
"`{pair}` will be locked until `{lock_end_time}`."
).format(**msg)
f"*Protection* triggered due to {msg['reason']}. "
f"`{msg['pair']}` will be locked until `{msg['lock_end_time']}`."
)
elif msg_type == RPCMessageType.PROTECTION_TRIGGER_GLOBAL:
message = (
"*Protection* triggered due to {reason}. "
"*All pairs* will be locked until `{lock_end_time}`."
).format(**msg)
f"*Protection* triggered due to {msg['reason']}. "
f"*All pairs* will be locked until `{msg['lock_end_time']}`."
)
elif msg_type == RPCMessageType.STATUS:
message = '*Status:* `{status}`'.format(**msg)
message = f"*Status:* `{msg['status']}`"
elif msg_type == RPCMessageType.WARNING:
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
message = f"\N{WARNING SIGN} *Warning:* `{msg['status']}`"
elif msg_type == RPCMessageType.STARTUP:
message = '{status}'.format(**msg)
message = f"{msg['status']}"
else:
raise NotImplementedError('Unknown message type: {}'.format(msg_type))
raise NotImplementedError(f"Unknown message type: {msg_type}")
return message
def send_msg(self, msg: Dict[str, Any]) -> None:
@@ -396,7 +414,7 @@ class Telegram(RPCHandler):
first_avg = filled_orders[0]["safe_price"]
for x, order in enumerate(filled_orders):
if not order['ft_is_entry']:
if not order['ft_is_entry'] or order['is_open'] is True:
continue
cur_entry_datetime = arrow.get(order["order_filled_date"])
cur_entry_amount = order["amount"]
@@ -563,6 +581,60 @@ class Telegram(RPCHandler):
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _timeunit_stats(self, update: Update, context: CallbackContext, unit: str) -> None:
"""
Handler for /daily <n>
Returns a daily profit (in BTC) over the last n days.
:param bot: telegram bot
:param update: message update
:return: None
"""
vals = {
'days': TimeunitMappings('Day', 'Daily', 'days', 'update_daily', 7),
'weeks': TimeunitMappings('Monday', 'Weekly', 'weeks (starting from Monday)',
'update_weekly', 8),
'months': TimeunitMappings('Month', 'Monthly', 'months', 'update_monthly', 6),
}
val = vals[unit]
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
timescale = int(context.args[0]) if context.args else val.default
except (TypeError, ValueError, IndexError):
timescale = val.default
try:
stats = self._rpc._rpc_timeunit_profit(
timescale,
stake_cur,
fiat_disp_cur,
unit
)
stats_tab = tabulate(
[[f"{period['date']} ({period['trade_count']})",
f"{round_coin_value(period['abs_profit'], stats['stake_currency'])}",
f"{period['fiat_value']:.2f} {stats['fiat_display_currency']}",
f"{period['rel_profit']:.2%}",
] for period in stats['data']],
headers=[
f"{val.header} (count)",
f'{stake_cur}',
f'{fiat_disp_cur}',
'Profit %',
'Trades',
],
tablefmt='simple')
message = (
f'<b>{val.message} Profit over the last {timescale} {val.message2}</b>:\n'
f'<pre>{stats_tab}</pre>'
)
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path=val.callback, query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _daily(self, update: Update, context: CallbackContext) -> None:
"""
@@ -572,35 +644,7 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
timescale = int(context.args[0]) if context.args else 7
except (TypeError, ValueError, IndexError):
timescale = 7
try:
stats = self._rpc._rpc_daily_profit(
timescale,
stake_cur,
fiat_disp_cur
)
stats_tab = tabulate(
[[day['date'],
f"{round_coin_value(day['abs_profit'], stats['stake_currency'])}",
f"{day['fiat_value']:.3f} {stats['fiat_display_currency']}",
f"{day['trade_count']} trades"] for day in stats['data']],
headers=[
'Day',
f'Profit {stake_cur}',
f'Profit {fiat_disp_cur}',
'Trades',
],
tablefmt='simple')
message = f'<b>Daily Profit over the last {timescale} days</b>:\n<pre>{stats_tab}</pre>'
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path="update_daily", query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
self._timeunit_stats(update, context, 'days')
@authorized_only
def _weekly(self, update: Update, context: CallbackContext) -> None:
@@ -611,36 +655,7 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
timescale = int(context.args[0]) if context.args else 8
except (TypeError, ValueError, IndexError):
timescale = 8
try:
stats = self._rpc._rpc_weekly_profit(
timescale,
stake_cur,
fiat_disp_cur
)
stats_tab = tabulate(
[[week['date'],
f"{round_coin_value(week['abs_profit'], stats['stake_currency'])}",
f"{week['fiat_value']:.3f} {stats['fiat_display_currency']}",
f"{week['trade_count']} trades"] for week in stats['data']],
headers=[
'Monday',
f'Profit {stake_cur}',
f'Profit {fiat_disp_cur}',
'Trades',
],
tablefmt='simple')
message = f'<b>Weekly Profit over the last {timescale} weeks ' \
f'(starting from Monday)</b>:\n<pre>{stats_tab}</pre> '
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path="update_weekly", query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
self._timeunit_stats(update, context, 'weeks')
@authorized_only
def _monthly(self, update: Update, context: CallbackContext) -> None:
@@ -651,36 +666,7 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
timescale = int(context.args[0]) if context.args else 6
except (TypeError, ValueError, IndexError):
timescale = 6
try:
stats = self._rpc._rpc_monthly_profit(
timescale,
stake_cur,
fiat_disp_cur
)
stats_tab = tabulate(
[[month['date'],
f"{round_coin_value(month['abs_profit'], stats['stake_currency'])}",
f"{month['fiat_value']:.3f} {stats['fiat_display_currency']}",
f"{month['trade_count']} trades"] for month in stats['data']],
headers=[
'Month',
f'Profit {stake_cur}',
f'Profit {fiat_disp_cur}',
'Trades',
],
tablefmt='simple')
message = f'<b>Monthly Profit over the last {timescale} months' \
f'</b>:\n<pre>{stats_tab}</pre> '
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path="update_monthly", query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
self._timeunit_stats(update, context, 'months')
@authorized_only
def _profit(self, update: Update, context: CallbackContext) -> None:
@@ -744,12 +730,18 @@ class Telegram(RPCHandler):
f"*Total Trade Count:* `{trade_count}`\n"
f"*{'First Trade opened' if not timescale else 'Showing Profit since'}:* "
f"`{first_trade_date}`\n"
f"*Latest Trade opened:* `{latest_trade_date}\n`"
f"*Latest Trade opened:* `{latest_trade_date}`\n"
f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`"
)
if stats['closed_trade_count'] > 0:
markdown_msg += (f"\n*Avg. Duration:* `{avg_duration}`\n"
f"*Best Performing:* `{best_pair}: {best_pair_profit_ratio:.2%}`")
markdown_msg += (
f"\n*Avg. Duration:* `{avg_duration}`\n"
f"*Best Performing:* `{best_pair}: {best_pair_profit_ratio:.2%}`\n"
f"*Trading volume:* `{round_coin_value(stats['trading_volume'], stake_cur)}`\n"
f"*Profit factor:* `{stats['profit_factor']:.2f}`\n"
f"*Max Drawdown:* `{stats['max_drawdown']:.2%} "
f"({round_coin_value(stats['max_drawdown_abs'], stake_cur)})`"
)
self._send_msg(markdown_msg, reload_able=True, callback_path="update_profit",
query=update.callback_query)
@@ -785,7 +777,7 @@ class Telegram(RPCHandler):
headers=['Exit Reason', 'Exits', 'Wins', 'Losses']
)
if len(exit_reasons_tabulate) > 25:
self._send_msg(exit_reasons_msg, ParseMode.MARKDOWN)
self._send_msg(f"```\n{exit_reasons_msg}```", ParseMode.MARKDOWN)
exit_reasons_msg = ''
durations = stats['durations']
@@ -889,7 +881,7 @@ class Telegram(RPCHandler):
:return: None
"""
msg = self._rpc._rpc_start()
self._send_msg('Status: `{status}`'.format(**msg))
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
def _stop(self, update: Update, context: CallbackContext) -> None:
@@ -901,7 +893,7 @@ class Telegram(RPCHandler):
:return: None
"""
msg = self._rpc._rpc_stop()
self._send_msg('Status: `{status}`'.format(**msg))
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
def _reload_config(self, update: Update, context: CallbackContext) -> None:
@@ -913,7 +905,7 @@ class Telegram(RPCHandler):
:return: None
"""
msg = self._rpc._rpc_reload_config()
self._send_msg('Status: `{status}`'.format(**msg))
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
def _stopbuy(self, update: Update, context: CallbackContext) -> None:
@@ -925,7 +917,7 @@ class Telegram(RPCHandler):
:return: None
"""
msg = self._rpc._rpc_stopbuy()
self._send_msg('Status: `{status}`'.format(**msg))
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
def _force_exit(self, update: Update, context: CallbackContext) -> None:
@@ -1087,9 +1079,9 @@ class Telegram(RPCHandler):
trade_id = int(context.args[0])
msg = self._rpc._rpc_delete(trade_id)
self._send_msg((
'`{result_msg}`\n'
f"`{msg['result_msg']}`\n"
'Please make sure to take care of this asset on the exchange manually.'
).format(**msg))
))
except RPCException as e:
self._send_msg(str(e))
@@ -1417,7 +1409,7 @@ class Telegram(RPCHandler):
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
"*/forceexit <trade_id>|all:* `Instantly exits the given trade or all trades, "
"regardless of profit`\n"
"*/fe <trade_id>|all:* `Alias to /forceexit`\n"
"*/fx <trade_id>|all:* `Alias to /forceexit`\n"
f"{force_enter_text if self._config.get('force_entry_enable', False) else ''}"
"*/delete <trade_id>:* `Instantly delete the given trade in the database`\n"
"*/whitelist:* `Show current whitelist` \n"

4
freqtrade/strategy/__init__.py
View File

@@ -1,9 +1,9 @@
# 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 (BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter)
from freqtrade.strategy.informative_decorator import informative
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.parameters import (BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter)
from freqtrade.strategy.strategy_helper import (merge_informative_pair, stoploss_from_absolute,
stoploss_from_open)

387
freqtrade/strategy/hyper.py
View File

@@ -3,295 +3,18 @@ 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 pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
from typing import Any, Dict, Iterator, List, Tuple, Type, Union
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, json_load
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.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.strategy.parameters import BaseParameter
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)
@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:
low = int(self.low * pow(10, self._decimals))
high = int(self.high * pow(10, self._decimals)) + 1
return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)]
else:
return [self.value]
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)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List of categories 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:
return self.opt_range
else:
return [self.value]
class BooleanParameter(CategoricalParameter):
def __init__(self, *, default: Optional[Any] = None,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable Boolean Parameter.
It's a shortcut to `CategoricalParameter([True, False])`.
: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.
"""
categories = [True, False]
super().__init__(categories=categories, default=default, space=space, optimize=optimize,
load=load, **kwargs)
class HyperStrategyMixin:
"""
A helper base class which allows HyperOptAuto class to reuse implementations of buy/sell
@@ -307,7 +30,10 @@ class HyperStrategyMixin:
self.ft_sell_params: List[BaseParameter] = []
self.ft_protection_params: List[BaseParameter] = []
self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
params = self.load_params_from_file()
params = params.get('params', {})
self._ft_params_from_file = params
# Init/loading of parameters is done as part of ft_bot_start().
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
"""
@@ -327,28 +53,13 @@ class HyperStrategyMixin:
for par in params:
yield par.name, par
@classmethod
def detect_parameters(cls, category: str) -> Iterator[Tuple[str, BaseParameter]]:
""" Detect all parameters for 'category' """
for attr_name in dir(cls):
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
attr = getattr(cls, 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
@classmethod
def detect_all_parameters(cls) -> Dict:
""" Detect all parameters and return them as a list"""
params: Dict = {
'buy': list(cls.detect_parameters('buy')),
'sell': list(cls.detect_parameters('sell')),
'protection': list(cls.detect_parameters('protection')),
params: Dict[str, Any] = {
'buy': list(detect_parameters(cls, 'buy')),
'sell': list(detect_parameters(cls, 'sell')),
'protection': list(detect_parameters(cls, 'protection')),
}
params.update({
'count': len(params['buy'] + params['sell'] + params['protection'])
@@ -356,21 +67,49 @@ class HyperStrategyMixin:
return params
def _load_hyper_params(self, hyperopt: bool = False) -> None:
def ft_load_params_from_file(self) -> None:
"""
Load Parameters from parameter file
Should/must run before config values are loaded in strategy_resolver.
"""
if self._ft_params_from_file:
# Set parameters from Hyperopt results file
params = self._ft_params_from_file
self.minimal_roi = params.get('roi', getattr(self, 'minimal_roi', {}))
self.stoploss = params.get('stoploss', {}).get(
'stoploss', getattr(self, 'stoploss', -0.1))
trailing = params.get('trailing', {})
self.trailing_stop = trailing.get(
'trailing_stop', getattr(self, 'trailing_stop', False))
self.trailing_stop_positive = trailing.get(
'trailing_stop_positive', getattr(self, 'trailing_stop_positive', None))
self.trailing_stop_positive_offset = trailing.get(
'trailing_stop_positive_offset',
getattr(self, 'trailing_stop_positive_offset', 0))
self.trailing_only_offset_is_reached = trailing.get(
'trailing_only_offset_is_reached',
getattr(self, 'trailing_only_offset_is_reached', 0.0))
def ft_load_hyper_params(self, hyperopt: bool = False) -> None:
"""
Load Hyperoptable parameters
Prevalence:
* Parameters from parameter file
* Parameters defined in parameters objects (buy_params, sell_params, ...)
* Parameter defaults
"""
params = self.load_params_from_file()
params = params.get('params', {})
self._ft_params_from_file = params
buy_params = deep_merge_dicts(params.get('buy', {}), getattr(self, 'buy_params', {}))
sell_params = deep_merge_dicts(params.get('sell', {}), getattr(self, 'sell_params', {}))
protection_params = deep_merge_dicts(params.get('protection', {}),
buy_params = deep_merge_dicts(self._ft_params_from_file.get('buy', {}),
getattr(self, 'buy_params', {}))
sell_params = deep_merge_dicts(self._ft_params_from_file.get('sell', {}),
getattr(self, 'sell_params', {}))
protection_params = deep_merge_dicts(self._ft_params_from_file.get('protection', {}),
getattr(self, 'protection_params', {}))
self._load_params(buy_params, 'buy', hyperopt)
self._load_params(sell_params, 'sell', hyperopt)
self._load_params(protection_params, 'protection', hyperopt)
self._ft_load_params(buy_params, 'buy', hyperopt)
self._ft_load_params(sell_params, 'sell', hyperopt)
self._ft_load_params(protection_params, 'protection', hyperopt)
def load_params_from_file(self) -> Dict:
filename_str = getattr(self, '__file__', '')
@@ -393,7 +132,7 @@ class HyperStrategyMixin:
return {}
def _load_params(self, params: Dict, space: str, hyperopt: bool = False) -> None:
def _ft_load_params(self, params: Dict, space: str, hyperopt: bool = False) -> None:
"""
Set optimizable parameter values.
:param params: Dictionary with new parameter values.
@@ -402,7 +141,7 @@ class HyperStrategyMixin:
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):
for attr_name, attr in detect_parameters(self, space):
attr.name = attr_name
attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
if not attr.category:
@@ -424,7 +163,7 @@ class HyperStrategyMixin:
"""
Returns list of Parameters that are not part of the current optimize job
"""
params = {
params: Dict[str, Dict] = {
'buy': {},
'sell': {},
'protection': {},
@@ -433,3 +172,25 @@ class HyperStrategyMixin:
if not p.optimize or not p.in_space:
params[p.category][name] = p.value
return params
def detect_parameters(
obj: Union[HyperStrategyMixin, Type[HyperStrategyMixin]],
category: str
) -> Iterator[Tuple[str, BaseParameter]]:
"""
Detect all parameters for 'category' for "obj"
:param obj: Strategy object or class
:param category: category - usually `'buy', 'sell', 'protection',...
"""
for attr_name in dir(obj):
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
attr = getattr(obj, 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

78
freqtrade/strategy/interface.py
View File

@@ -14,9 +14,10 @@ from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, SignalDirection, SignalTagType,
SignalType, TradingMode)
from freqtrade.enums.runmode import RunMode
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
from freqtrade.persistence import Order, PairLocks, Trade
from freqtrade.strategy.hyper import HyperStrategyMixin
from freqtrade.strategy.informative_decorator import (InformativeData, PopulateIndicators,
_create_and_merge_informative_pair,
@@ -82,7 +83,7 @@ class IStrategy(ABC, HyperStrategyMixin):
}
# run "populate_indicators" only for new candle
process_only_new_candles: bool = False
process_only_new_candles: bool = True
use_exit_signal: bool
exit_profit_only: bool
@@ -144,6 +145,15 @@ class IStrategy(ABC, HyperStrategyMixin):
informative_data.candle_type = config['candle_type_def']
self._ft_informative.append((informative_data, cls_method))
def ft_bot_start(self, **kwargs) -> None:
"""
Strategy init - runs after dataprovider has been added.
Must call bot_start()
"""
strategy_safe_wrapper(self.bot_start)()
self.ft_load_hyper_params(self.config.get('runmode') == RunMode.HYPEROPT)
@abstractmethod
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
@@ -277,8 +287,9 @@ class IStrategy(ABC, HyperStrategyMixin):
:param pair: Pair that's about to be bought/shorted.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded.
:param amount: Amount in target (base) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
or current rate for market orders.
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
@@ -304,8 +315,9 @@ class IStrategy(ABC, HyperStrategyMixin):
:param pair: Pair for trade that's about to be exited.
:param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param amount: Amount in base currency.
:param rate: Rate that's going to be used when using limit orders
or current rate for market orders.
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param exit_reason: Exit reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
@@ -429,7 +441,7 @@ class IStrategy(ABC, HyperStrategyMixin):
return self.custom_sell(pair, trade, current_time, current_rate, current_profit, **kwargs)
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
"""
Customize stake size for each new trade.
@@ -447,8 +459,9 @@ class IStrategy(ABC, HyperStrategyMixin):
return proposed_stake
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: float,
max_stake: float, **kwargs) -> Optional[float]:
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
**kwargs) -> Optional[float]:
"""
Custom trade adjustment logic, returning the stake amount that a trade should be increased.
This means extra buy orders with additional fees.
@@ -498,8 +511,8 @@ class IStrategy(ABC, HyperStrategyMixin):
return current_order_rate
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, side: str,
**kwargs) -> float:
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
side: str, **kwargs) -> float:
"""
Customize leverage for each new trade. This method is only called in futures mode.
@@ -508,6 +521,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
:param proposed_leverage: A leverage proposed by the bot.
:param max_leverage: Max leverage allowed on this pair
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A leverage amount, which is between 1.0 and max_leverage.
"""
@@ -878,16 +892,16 @@ class IStrategy(ABC, HyperStrategyMixin):
def should_exit(self, trade: Trade, rate: float, current_time: datetime, *,
enter: bool, exit_: bool,
low: float = None, high: float = None,
force_stoploss: float = 0) -> ExitCheckTuple:
force_stoploss: float = 0) -> List[ExitCheckTuple]:
"""
This function evaluates if one of the conditions required to trigger an exit order
has been reached, which can either be a stop-loss, ROI or exit-signal.
:param low: Only used during backtesting to simulate (long)stoploss/(short)ROI
:param high: Only used during backtesting, to simulate (short)stoploss/(long)ROI
:param force_stoploss: Externally provided stoploss
:return: True if trade should be exited, False otherwise
:return: List of exit reasons - or empty list.
"""
exits: List[ExitCheckTuple] = []
current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate)
@@ -917,19 +931,20 @@ class IStrategy(ABC, HyperStrategyMixin):
if exit_ and not enter:
exit_signal = ExitType.EXIT_SIGNAL
else:
custom_reason = strategy_safe_wrapper(self.custom_exit, default_retval=False)(
reason_cust = strategy_safe_wrapper(self.custom_exit, default_retval=False)(
pair=trade.pair, trade=trade, current_time=current_time,
current_rate=current_rate, current_profit=current_profit)
if custom_reason:
if reason_cust:
exit_signal = ExitType.CUSTOM_EXIT
if isinstance(custom_reason, str):
if len(custom_reason) > CUSTOM_EXIT_MAX_LENGTH:
if isinstance(reason_cust, str):
custom_reason = reason_cust
if len(reason_cust) > CUSTOM_EXIT_MAX_LENGTH:
logger.warning(f'Custom exit reason returned from '
f'custom_exit is too long and was trimmed'
f'to {CUSTOM_EXIT_MAX_LENGTH} characters.')
custom_reason = custom_reason[:CUSTOM_EXIT_MAX_LENGTH]
custom_reason = reason_cust[:CUSTOM_EXIT_MAX_LENGTH]
else:
custom_reason = None
custom_reason = ''
if (
exit_signal == ExitType.CUSTOM_EXIT
or (exit_signal == ExitType.EXIT_SIGNAL
@@ -938,24 +953,29 @@ class IStrategy(ABC, HyperStrategyMixin):
logger.debug(f"{trade.pair} - Sell signal received. "
f"exit_type=ExitType.{exit_signal.name}" +
(f", custom_reason={custom_reason}" if custom_reason else ""))
return ExitCheckTuple(exit_type=exit_signal, exit_reason=custom_reason)
exits.append(ExitCheckTuple(exit_type=exit_signal, exit_reason=custom_reason))
# Sequence:
# Exit-signal
# ROI (if not stoploss)
# Stoploss
if roi_reached and stoplossflag.exit_type != ExitType.STOP_LOSS:
logger.debug(f"{trade.pair} - Required profit reached. exit_type=ExitType.ROI")
return ExitCheckTuple(exit_type=ExitType.ROI)
# ROI
# Trailing stoploss
if stoplossflag.exit_flag:
if stoplossflag.exit_type == ExitType.STOP_LOSS:
logger.debug(f"{trade.pair} - Stoploss hit. exit_type={stoplossflag.exit_type}")
return stoplossflag
exits.append(stoplossflag)
# This one is noisy, commented out...
# logger.debug(f"{trade.pair} - No exit signal.")
return ExitCheckTuple(exit_type=ExitType.NONE)
if roi_reached:
logger.debug(f"{trade.pair} - Required profit reached. exit_type=ExitType.ROI")
exits.append(ExitCheckTuple(exit_type=ExitType.ROI))
if stoplossflag.exit_type == ExitType.TRAILING_STOP_LOSS:
logger.debug(f"{trade.pair} - Trailing stoploss hit.")
exits.append(stoplossflag)
return exits
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
@@ -1070,7 +1090,7 @@ class IStrategy(ABC, HyperStrategyMixin):
else:
return current_profit > roi
def ft_check_timed_out(self, trade: LocalTrade, order: Order,
def ft_check_timed_out(self, trade: Trade, order: Order,
current_time: datetime) -> bool:
"""
FT Internal method.

289
freqtrade/strategy/parameters.py Normal file
View File

@@ -0,0 +1,289 @@
"""
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, Optional, Sequence, Union
with suppress(ImportError):
from skopt.space import Integer, Real, Categorical
from freqtrade.optimize.space import SKDecimal
from freqtrade.exceptions import OperationalException
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
low: int
high: 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)
@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:
low = int(self.low * pow(10, self._decimals))
high = int(self.high * pow(10, self._decimals)) + 1
return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)]
else:
return [self.value]
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)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List of categories 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:
return self.opt_range
else:
return [self.value]
class BooleanParameter(CategoricalParameter):
def __init__(self, *, default: Optional[Any] = None,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable Boolean Parameter.
It's a shortcut to `CategoricalParameter([True, False])`.
: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.
"""
categories = [True, False]
super().__init__(categories=categories, default=default, space=space, optimize=optimize,
load=load, **kwargs)

10
freqtrade/strategy/strategy_wrapper.py
View File

@@ -1,5 +1,7 @@
import logging
from copy import deepcopy
from functools import wraps
from typing import Any, Callable, TypeVar, cast
from freqtrade.exceptions import StrategyError
@@ -7,12 +9,16 @@ from freqtrade.exceptions import StrategyError
logger = logging.getLogger(__name__)
def strategy_safe_wrapper(f, message: str = "", default_retval=None, supress_error=False):
F = TypeVar('F', bound=Callable[..., Any])
def strategy_safe_wrapper(f: F, message: str = "", default_retval=None, supress_error=False) -> F:
"""
Wrapper around user-provided methods and functions.
Caches all exceptions and returns either the default_retval (if it's not None) or raises
a StrategyError exception, which then needs to be handled by the calling method.
"""
@wraps(f)
def wrapper(*args, **kwargs):
try:
if 'trade' in kwargs:
@@ -37,4 +43,4 @@ def strategy_safe_wrapper(f, message: str = "", default_retval=None, supress_err
raise StrategyError(str(error)) from error
return default_retval
return wrapper
return cast(F, wrapper)

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

@@ -6,7 +6,7 @@ import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame # noqa
from datetime import datetime # noqa
from typing import Optional # noqa
from typing import Optional, Union # noqa
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
@@ -64,7 +64,7 @@ class {{ strategy }}(IStrategy):
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
process_only_new_candles = True
# These values can be overridden in the config.
use_exit_signal = True

2
freqtrade/templates/sample_strategy.py
View File

@@ -62,7 +62,7 @@ class SampleStrategy(IStrategy):
timeframe = '5m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
process_only_new_candles = True
# These values can be overridden in the config.
use_exit_signal = True

27
freqtrade/templates/subtemplates/strategy_methods_advanced.j2
View File

@@ -13,7 +13,7 @@ def bot_loop_start(self, **kwargs) -> None:
pass
def custom_entry_price(self, pair: str, current_time: 'datetime', proposed_rate: float,
entry_tag: Optional[str], **kwargs) -> float:
entry_tag: 'Optional[str]', side: str, **kwargs) -> float:
"""
Custom entry price logic, returning the new entry price.
@@ -80,8 +80,8 @@ def custom_exit_price(self, pair: str, trade: 'Trade',
return proposed_rate
def custom_stake_amount(self, pair: str, current_time: 'datetime', current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
side: str, entry_tag: Optional[str], **kwargs) -> float:
proposed_stake: float, min_stake: Optional[float], max_stake: float,
entry_tag: 'Optional[str]', side: str, **kwargs) -> float:
"""
Customize stake size for each new trade.
@@ -159,8 +159,9 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
:param pair: Pair that's about to be bought/shorted.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded.
:param amount: Amount in target (base) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
or current rate for market orders.
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
@@ -175,7 +176,7 @@ def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount:
rate: float, time_in_force: str, exit_reason: str,
current_time: 'datetime', **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Called right before placing a regular exit order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
@@ -183,18 +184,19 @@ def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount:
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's currently analyzed
:param pair: Pair for trade that's about to be exited.
:param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param amount: Amount in base currency.
:param rate: Rate that's going to be used when using limit orders
or current rate for market orders.
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param exit_reason: Exit reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'exit_signal', 'force_exit', 'emergency_exit']
: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 exit-order is placed on the exchange.
:return bool: When True, then the exit-order is placed on the exchange.
False aborts the process
"""
return True
@@ -244,8 +246,8 @@ def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order',
return False
def adjust_trade_position(self, trade: 'Trade', current_time: 'datetime',
current_rate: float, current_profit: float, min_stake: float,
max_stake: float, **kwargs) -> Optional[float]:
current_rate: float, current_profit: float, min_stake: Optional[float],
max_stake: float, **kwargs) -> 'Optional[float]':
"""
Custom trade adjustment logic, returning the stake amount that a trade should be increased.
This means extra buy orders with additional fees.
@@ -267,8 +269,8 @@ def adjust_trade_position(self, trade: 'Trade', current_time: 'datetime',
return None
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, side: str,
**kwargs) -> float:
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
side: str, **kwargs) -> float:
"""
Customize leverage for each new trade. This method is only called in futures mode.
@@ -277,6 +279,7 @@ def leverage(self, pair: str, current_time: datetime, current_rate: float,
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
:param proposed_leverage: A leverage proposed by the bot.
:param max_leverage: Max leverage allowed on this pair
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A leverage amount, which is between 1.0 and max_leverage.
"""