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Merge branch 'develop' into pr/Antreasgr/4838

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This commit is contained in:
Matthias
2021-05-27 14:58:19 +02:00
parent 3014bc3467 cf39dd2163
commit a89364aa98
95 changed files with 2815 additions and 1369 deletions
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78
freqtrade/commands/hyperopt_commands.py
View File

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

7
freqtrade/configuration/timerange.py
View File

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

12
freqtrade/constants.py
View File

@@ -11,6 +11,7 @@ DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
TIMEOUT_UNITS = ['minutes', 'seconds']
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
UNLIMITED_STAKE_AMOUNT = 'unlimited'
@@ -137,7 +138,8 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'buy': {'type': 'number', 'minimum': 1},
'sell': {'type': 'number', 'minimum': 1}
'sell': {'type': 'number', 'minimum': 1},
'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
}
},
'bid_strategy': {
@@ -258,7 +260,13 @@ CONF_SCHEMA = {
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'sell': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell': {
'type': ['string', 'object'],
'additionalProperties': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS
}
},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_fill': {
'type': 'string',

50
freqtrade/data/btanalysis.py
View File

@@ -156,33 +156,35 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
data = data['strategy'][strategy]['trades']
df = pd.DataFrame(data)
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
if not df.empty:
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
else:
# old format - only with lists.
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
df['open_date'] = pd.to_datetime(df['open_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
# Create compatibility with new format
df['profit_abs'] = df['close_rate'] - df['open_rate']
if 'profit_ratio' not in df.columns:
df['profit_ratio'] = df['profit_percent']
df = df.sort_values("open_date").reset_index(drop=True)
if not df.empty:
df['open_date'] = pd.to_datetime(df['open_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
# Create compatibility with new format
df['profit_abs'] = df['close_rate'] - df['open_rate']
if not df.empty:
if 'profit_ratio' not in df.columns:
df['profit_ratio'] = df['profit_percent']
df = df.sort_values("open_date").reset_index(drop=True)
return df

21
freqtrade/data/converter.py
View File

@@ -145,6 +145,27 @@ def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
return df
def trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
startup_candles: int) -> Dict[str, DataFrame]:
"""
Trim startup period from analyzed dataframes
:param preprocessed: Dict of pair: dataframe
:param timerange: timerange (use start and end date if available)
:param startup_candles: Startup-candles that should be removed
:return: Dict of trimmed dataframes
"""
processed: Dict[str, DataFrame] = {}
for pair, df in preprocessed.items():
trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles)
if not trimed_df.empty:
processed[pair] = trimed_df
else:
logger.warning(f'{pair} has no data left after adjusting for startup candles, '
f'skipping.')
return processed
def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
"""
TODO: This should get a dedicated test

174
freqtrade/data/dataprovider.py
View File

@@ -19,14 +19,25 @@ from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
NO_EXCHANGE_EXCEPTION = 'Exchange is not available to DataProvider.'
MAX_DATAFRAME_CANDLES = 1000
class DataProvider:
def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None:
def __init__(self, config: dict, exchange: Optional[Exchange], pairlists=None) -> None:
self._config = config
self._exchange = exchange
self._pairlists = pairlists
self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
self.__slice_index: Optional[int] = None
def _set_dataframe_max_index(self, limit_index: int):
"""
Limit analyzed dataframe to max specified index.
:param limit_index: dataframe index.
"""
self.__slice_index = limit_index
def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None:
"""
@@ -45,40 +56,6 @@ class DataProvider:
"""
self._pairlists = pairlists
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
"""
Refresh data, called with each cycle
"""
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
@property
def available_pairs(self) -> ListPairsWithTimeframes:
"""
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get candle (OHLCV) data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, timeframe or self._config['timeframe']),
copy=copy)
else:
return DataFrame()
def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Get stored historical candle (OHLCV) data
@@ -111,47 +88,27 @@ class DataProvider:
def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
"""
Retrieve the analyzed dataframe. Returns the full dataframe in trade mode (live / dry),
and the last 1000 candles (up to the time evaluated at this moment) in all other modes.
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
:return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe
combination.
Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
"""
if (pair, timeframe) in self.__cached_pairs:
return self.__cached_pairs[(pair, timeframe)]
pair_key = (pair, timeframe)
if pair_key in self.__cached_pairs:
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
df, date = self.__cached_pairs[pair_key]
else:
df, date = self.__cached_pairs[pair_key]
if self.__slice_index is not None:
max_index = self.__slice_index
df = df.iloc[max(0, max_index - MAX_DATAFRAME_CANDLES):max_index]
return df, date
else:
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
return self._exchange.markets.get(pair)
def ticker(self, pair: str):
"""
Return last ticker data from exchange
:param pair: Pair to get the data for
:return: Ticker dict from exchange or empty dict if ticker is not available for the pair
"""
try:
return self._exchange.fetch_ticker(pair)
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
Fetch latest l2 orderbook data
Warning: Does a network request - so use with common sense.
:param pair: pair to get the data for
:param maximum: Maximum number of orderbook entries to query
:return: dict including bids/asks with a total of `maximum` entries.
"""
return self._exchange.fetch_l2_order_book(pair, maximum)
@property
def runmode(self) -> RunMode:
"""
@@ -173,3 +130,86 @@ class DataProvider:
return self._pairlists.whitelist.copy()
else:
raise OperationalException("Dataprovider was not initialized with a pairlist provider.")
def clear_cache(self):
"""
Clear pair dataframe cache.
"""
self.__cached_pairs = {}
# Exchange functions
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
"""
Refresh data, called with each cycle
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
@property
def available_pairs(self) -> ListPairsWithTimeframes:
"""
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get candle (OHLCV) data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, timeframe or self._config['timeframe']),
copy=copy)
else:
return DataFrame()
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return self._exchange.markets.get(pair)
def ticker(self, pair: str):
"""
Return last ticker data from exchange
:param pair: Pair to get the data for
:return: Ticker dict from exchange or empty dict if ticker is not available for the pair
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
try:
return self._exchange.fetch_ticker(pair)
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
Fetch latest l2 orderbook data
Warning: Does a network request - so use with common sense.
:param pair: pair to get the data for
:param maximum: Maximum number of orderbook entries to query
:return: dict including bids/asks with a total of `maximum` entries.
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return self._exchange.fetch_l2_order_book(pair, maximum)

15
freqtrade/data/history/history_utils.py
View File

@@ -265,9 +265,13 @@ def _download_trades_history(exchange: Exchange,
"""
try:
since = timerange.startts * 1000 if \
(timerange and timerange.starttype == 'date') else int(arrow.utcnow().shift(
days=-new_pairs_days).float_timestamp) * 1000
until = None
if (timerange and timerange.starttype == 'date'):
since = timerange.startts * 1000
if timerange.stoptype == 'date':
until = timerange.stopts * 1000
else:
since = int(arrow.utcnow().shift(days=-new_pairs_days).float_timestamp) * 1000
trades = data_handler.trades_load(pair)
@@ -295,6 +299,7 @@ def _download_trades_history(exchange: Exchange,
# Default since_ms to 30 days if nothing is given
new_trades = exchange.get_historic_trades(pair=pair,
since=since,
until=until,
from_id=from_id,
)
trades.extend(new_trades[1])
@@ -367,7 +372,7 @@ def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
logger.exception(f'Could not convert {pair} to OHLCV.')
def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
def get_timerange(data: Dict[str, DataFrame]) -> Tuple[datetime, datetime]:
"""
Get the maximum common timerange for the given backtest data.
@@ -375,7 +380,7 @@ def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
:return: tuple containing min_date, max_date
"""
timeranges = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
(frame['date'].min().to_pydatetime(), frame['date'].max().to_pydatetime())
for frame in data.values()
]
return (min(timeranges, key=operator.itemgetter(0))[0],

38
freqtrade/edge/edge_positioning.py
View File

@@ -1,6 +1,8 @@
# pragma pylint: disable=W0603
""" Edge positioning package """
import logging
from collections import defaultdict
from copy import deepcopy
from typing import Any, Dict, List, NamedTuple
import arrow
@@ -12,8 +14,10 @@ from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange.exchange import timeframe_to_seconds
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.strategy.interface import SellType
from freqtrade.state import RunMode
from freqtrade.strategy.interface import IStrategy, SellType
logger = logging.getLogger(__name__)
@@ -45,7 +49,7 @@ class Edge:
self.config = config
self.exchange = exchange
self.strategy = strategy
self.strategy: IStrategy = strategy
self.edge_config = self.config.get('edge', {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
@@ -102,14 +106,33 @@ class Edge:
logger.info('Using local backtesting data (using whitelist in given config) ...')
if self._refresh_pairs:
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
self.strategy.timeframe) * self.strategy.startup_candle_count)
refresh_data(
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
# Download informative pairs too
res = defaultdict(list)
for p, t in self.strategy.informative_pairs():
res[t].append(p)
for timeframe, inf_pairs in res.items():
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
timeframe) * self.strategy.startup_candle_count)
refresh_data(
datadir=self.config['datadir'],
pairs=inf_pairs,
exchange=self.exchange,
timeframe=timeframe,
timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
data = load_data(
datadir=self.config['datadir'],
@@ -125,8 +148,11 @@ class Edge:
self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...")
return False
# Fake run-mode to Edge
prior_rm = self.config['runmode']
self.config['runmode'] = RunMode.EDGE
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
self.config['runmode'] = prior_rm
# Print timeframe
min_date, max_date = get_timerange(preprocessed)
@@ -183,7 +209,7 @@ class Edge:
if pair in self._cached_pairs:
return self._cached_pairs[pair].stoploss
else:
logger.warning('tried to access stoploss of a non-existing pair, '
logger.warning(f'Tried to access stoploss of non-existing pair {pair}, '
'strategy stoploss is returned instead.')
return self.strategy.stoploss
@@ -214,7 +240,7 @@ class Edge:
return self._final_pairs
def accepted_pairs(self) -> list:
def accepted_pairs(self) -> List[Dict[str, Any]]:
"""
return a list of accepted pairs along with their winrate, expectancy and stoploss
"""

2
freqtrade/exchange/__init__.py
View File

@@ -7,6 +7,7 @@ from freqtrade.exchange.bibox import Bibox
from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bittrex import Bittrex
from freqtrade.exchange.bybit import Bybit
from freqtrade.exchange.coinbasepro import Coinbasepro
from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
is_exchange_known_ccxt, is_exchange_officially_supported,
market_is_active, timeframe_to_minutes, timeframe_to_msecs,
@@ -14,5 +15,6 @@ from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
timeframe_to_seconds, validate_exchange,
validate_exchanges)
from freqtrade.exchange.ftx import Ftx
from freqtrade.exchange.hitbtc import Hitbtc
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.kucoin import Kucoin

1
freqtrade/exchange/bybit.py
View File

@@ -18,7 +18,6 @@ class Bybit(Exchange):
may still not work as expected.
"""
# fetchCurrencies API point requires authentication for Bybit,
_ft_has: Dict = {
"ohlcv_candle_limit": 200,
}

23
freqtrade/exchange/coinbasepro.py Normal file
View File

@@ -0,0 +1,23 @@
""" CoinbasePro exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Coinbasepro(Exchange):
"""
CoinbasePro exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 300,
}

34
freqtrade/exchange/exchange.py
View File

@@ -59,6 +59,7 @@ class Exchange:
_ft_has_default: Dict = {
"stoploss_on_exchange": False,
"order_time_in_force": ["gtc"],
"ohlcv_params": {},
"ohlcv_candle_limit": 500,
"ohlcv_partial_candle": True,
"trades_pagination": "time", # Possible are "time" or "id"
@@ -465,7 +466,7 @@ class Exchange:
def amount_to_precision(self, pair: str, amount: float) -> float:
'''
Returns the amount to buy or sell to a precision the Exchange accepts
Reimplementation of ccxt internal methods - ensuring we can test the result is correct
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
'''
if self.markets[pair]['precision']['amount']:
@@ -479,7 +480,7 @@ class Exchange:
def price_to_precision(self, pair: str, price: float) -> float:
'''
Returns the price rounded up to the precision the Exchange accepts.
Partial Reimplementation of ccxt internal method decimal_to_precision(),
Partial Re-implementation of ccxt internal method decimal_to_precision(),
which does not support rounding up
TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
align with amount_to_precision().
@@ -862,10 +863,11 @@ class Exchange:
"Fetching pair %s, interval %s, since %s %s...",
pair, timeframe, since_ms, s
)
params = self._ft_has.get('ohlcv_params', {})
data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
since=since_ms,
limit=self.ohlcv_candle_limit(timeframe))
limit=self.ohlcv_candle_limit(timeframe),
params=params)
# Some exchanges sort OHLCV in ASC order and others in DESC.
# Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
@@ -1118,6 +1120,27 @@ class Exchange:
return order
def cancel_stoploss_order_with_result(self, order_id: str, pair: str, amount: float) -> Dict:
"""
Cancel stoploss order returning a result.
Creates a fake result if cancel order returns a non-usable result
and fetch_order does not work (certain exchanges don't return cancelled orders)
:param order_id: stoploss-order-id to cancel
:param pair: Pair corresponding to order_id
:param amount: Amount to use for fake response
:return: Result from either cancel_order if usable, or fetch_order
"""
corder = self.cancel_stoploss_order(order_id, pair)
if self.is_cancel_order_result_suitable(corder):
return corder
try:
order = self.fetch_stoploss_order(order_id, pair)
except InvalidOrderException:
logger.warning(f"Could not fetch cancelled stoploss order {order_id}.")
order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
return order
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
@@ -1237,6 +1260,9 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
return order['id']
@retrier
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: str = 'maker') -> float:

6
freqtrade/exchange/ftx.py
View File

@@ -8,6 +8,7 @@ from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, Invali
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT, retrier
from freqtrade.misc import safe_value_fallback2
logger = logging.getLogger(__name__)
@@ -135,3 +136,8 @@ class Ftx(Exchange):
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
if order['type'] == 'stop':
return safe_value_fallback2(order['info'], order, 'orderId', 'id')
return order['id']

23
freqtrade/exchange/hitbtc.py Normal file
View File

@@ -0,0 +1,23 @@
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Hitbtc(Exchange):
"""
Hitbtc exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ohlcv_params": {"sort": "DESC"}
}

2
freqtrade/exchange/kraken.py
View File

@@ -53,6 +53,8 @@ class Kraken(Exchange):
# x["side"], x["amount"],
) for x in orders]
for bal in balances:
if not isinstance(balances[bal], dict):
continue
balances[bal]['used'] = sum(order[1] for order in order_list if order[0] == bal)
balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used']

68
freqtrade/freqtradebot.py
View File

@@ -11,7 +11,6 @@ from typing import Any, Dict, List, Optional
import arrow
from cachetools import TTLCache
from pandas import DataFrame
from freqtrade import __version__, constants
from freqtrade.configuration import validate_config_consistency
@@ -268,7 +267,7 @@ class FreqtradeBot(LoggingMixin):
def update_closed_trades_without_assigned_fees(self):
"""
Update closed trades without close fees assigned.
Only acts when Orders are in the database, otherwise the last orderid is unknown.
Only acts when Orders are in the database, otherwise the last order-id is unknown.
"""
if self.config['dry_run']:
# Updating open orders in dry-run does not make sense and will fail.
@@ -553,7 +552,7 @@ class FreqtradeBot(LoggingMixin):
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force):
time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of buying {pair}")
return False
amount = self.exchange.amount_to_precision(pair, amount)
@@ -602,6 +601,7 @@ class FreqtradeBot(LoggingMixin):
pair=pair,
stake_amount=stake_amount,
amount=amount,
is_open=True,
amount_requested=amount_requested,
fee_open=fee,
fee_close=fee,
@@ -631,7 +631,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_buy(self, trade: Trade, order_type: str) -> None:
"""
Sends rpc notification when a buy occured.
Sends rpc notification when a buy occurred.
"""
msg = {
'trade_id': trade.id,
@@ -653,7 +653,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a buy cancel occured.
Sends rpc notification when a buy cancel occurred.
"""
current_rate = self.get_buy_rate(trade.pair, False)
@@ -714,7 +714,7 @@ class FreqtradeBot(LoggingMixin):
except DependencyException as exception:
logger.warning('Unable to sell trade %s: %s', trade.pair, exception)
# Updating wallets if any trade occured
# Updating wallets if any trade occurred
if trades_closed:
self.wallets.update()
@@ -784,10 +784,10 @@ class FreqtradeBot(LoggingMixin):
config_ask_strategy = self.config.get('ask_strategy', {})
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
self.strategy.timeframe)
if (config_ask_strategy.get('use_sell_signal', True) or
config_ask_strategy.get('ignore_roi_if_buy_signal', False)):
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
self.strategy.timeframe)
(buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.timeframe, analyzed_df)
@@ -814,13 +814,13 @@ class FreqtradeBot(LoggingMixin):
# resulting in outdated RPC messages
self._sell_rate_cache[trade.pair] = sell_rate
if self._check_and_execute_sell(analyzed_df, trade, sell_rate, buy, sell):
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
else:
logger.debug('checking sell')
sell_rate = self.get_sell_rate(trade.pair, True)
if self._check_and_execute_sell(analyzed_df, trade, sell_rate, buy, sell):
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
logger.debug('Found no sell signal for %s.', trade)
@@ -933,14 +933,15 @@ class FreqtradeBot(LoggingMixin):
:return: None
"""
if self.exchange.stoploss_adjust(trade.stop_loss, order):
# we check if the update is neccesary
# we check if the update is necessary
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
# cancelling the current stoploss on exchange first
logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
f"(orderid:{order['id']}) in order to add another one ...")
try:
co = self.exchange.cancel_stoploss_order(order['id'], trade.pair)
co = self.exchange.cancel_stoploss_order_with_result(order['id'], trade.pair,
trade.amount)
trade.update_order(co)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {order['id']} "
@@ -951,13 +952,13 @@ class FreqtradeBot(LoggingMixin):
logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.")
def _check_and_execute_sell(self, dataframe: DataFrame, trade: Trade, sell_rate: float,
def _check_and_execute_sell(self, trade: Trade, sell_rate: float,
buy: bool, sell: bool) -> bool:
"""
Check and execute sell
"""
should_sell = self.strategy.should_sell(
dataframe, trade, sell_rate, datetime.now(timezone.utc), buy, sell,
trade, sell_rate, datetime.now(timezone.utc), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
@@ -974,15 +975,16 @@ class FreqtradeBot(LoggingMixin):
timeout = self.config.get('unfilledtimeout', {}).get(side)
ordertime = arrow.get(order['datetime']).datetime
if timeout is not None:
timeout_threshold = arrow.utcnow().shift(minutes=-timeout).datetime
timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
timeout_kwargs = {timeout_unit: -timeout}
timeout_threshold = arrow.utcnow().shift(**timeout_kwargs).datetime
return (order['status'] == 'open' and order['side'] == side
and ordertime < timeout_threshold)
return False
def check_handle_timedout(self) -> None:
"""
Check if any orders are timed out and cancel if neccessary
Check if any orders are timed out and cancel if necessary
:param timeoutvalue: Number of minutes until order is considered timed out
:return: None
"""
@@ -1044,6 +1046,16 @@ class FreqtradeBot(LoggingMixin):
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in ('cancelled', 'canceled', 'closed'):
filled_val = order.get('filled', 0.0) or 0.0
filled_stake = filled_val * trade.open_rate
minstake = self.exchange.get_min_pair_stake_amount(
trade.pair, trade.open_rate, self.strategy.stoploss)
if filled_val > 0 and filled_stake < minstake:
logger.warning(
f"Order {trade.open_order_id} for {trade.pair} not cancelled, "
f"as the filled amount of {filled_val} would result in an unsellable trade.")
return False
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
# Avoid race condition where the order could not be cancelled coz its already filled.
@@ -1173,7 +1185,9 @@ class FreqtradeBot(LoggingMixin):
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
try:
self.exchange.cancel_stoploss_order(trade.stoploss_order_id, trade.pair)
co = self.exchange.cancel_stoploss_order_with_result(trade.stoploss_order_id,
trade.pair, trade.amount)
trade.update_order(co)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
@@ -1191,8 +1205,8 @@ class FreqtradeBot(LoggingMixin):
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit,
time_in_force=time_in_force,
sell_reason=sell_reason.sell_reason):
time_in_force=time_in_force, sell_reason=sell_reason.sell_reason,
current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of selling {trade.pair}")
return False
@@ -1221,7 +1235,7 @@ class FreqtradeBot(LoggingMixin):
self.update_trade_state(trade, trade.open_order_id, order)
Trade.query.session.flush()
# Lock pair for one candle to prevent immediate rebuys
# Lock pair for one candle to prevent immediate re-buys
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
@@ -1231,7 +1245,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_sell(self, trade: Trade, order_type: str, fill: bool = False) -> None:
"""
Sends rpc notification when a sell occured.
Sends rpc notification when a sell occurred.
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
@@ -1272,7 +1286,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a sell cancel occured.
Sends rpc notification when a sell cancel occurred.
"""
if trade.sell_order_status == reason:
return
@@ -1325,7 +1339,7 @@ class FreqtradeBot(LoggingMixin):
Handles closing both buy and sell orders.
:param trade: Trade object of the trade we're analyzing
:param order_id: Order-id of the order we're analyzing
:param action_order: Already aquired order object
:param action_order: Already acquired order object
:return: True if order has been cancelled without being filled partially, False otherwise
"""
if not order_id:
@@ -1395,7 +1409,7 @@ class FreqtradeBot(LoggingMixin):
def get_real_amount(self, trade: Trade, order: Dict) -> float:
"""
Detect and update trade fee.
Calls trade.update_fee() uppon correct detection.
Calls trade.update_fee() upon correct detection.
Returns modified amount if the fee was taken from the destination currency.
Necessary for exchanges which charge fees in base currency (e.g. binance)
:return: identical (or new) amount for the trade
@@ -1428,8 +1442,8 @@ class FreqtradeBot(LoggingMixin):
"""
fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
"""
trades = self.exchange.get_trades_for_order(order['id'], trade.pair,
trade.open_date)
trades = self.exchange.get_trades_for_order(self.exchange.get_order_id_conditional(order),
trade.pair, trade.open_date)
if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)

13
freqtrade/misc.py
View File

@@ -6,7 +6,7 @@ import logging
import re
from datetime import datetime
from pathlib import Path
from typing import Any
from typing import Any, Iterator, List
from typing.io import IO
import rapidjson
@@ -202,3 +202,14 @@ def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
return render_template(templatefile, arguments)
except TemplateNotFound:
return render_template(templatefallbackfile, arguments)
def chunks(lst: List[Any], n: int) -> Iterator[List[Any]]:
"""
Split lst into chunks of the size n.
:param lst: list to split into chunks
:param n: number of max elements per chunk
:return: None
"""
for chunk in range(0, len(lst), n):
yield (lst[chunk:chunk + n])

116
freqtrade/optimize/backtesting.py
View File

@@ -15,7 +15,7 @@ from freqtrade.configuration import TimeRange, remove_credentials, validate_conf
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@@ -63,9 +63,7 @@ class Backtesting:
self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
self.dataprovider = DataProvider(self.config, None)
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
@@ -96,7 +94,7 @@ class Backtesting:
"PrecisionFilter not allowed for backtesting multiple strategies."
)
dataprovider.add_pairlisthandler(self.pairlists)
self.dataprovider.add_pairlisthandler(self.pairlists)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
@@ -112,15 +110,11 @@ class Backtesting:
PairLocks.timeframe = self.config['timeframe']
PairLocks.use_db = False
PairLocks.reset_locks()
if self.config.get('enable_protections', False):
self.protections = ProtectionManager(self.config)
self.wallets = Wallets(self.config, self.exchange, log=False)
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
self._set_strategy(self.strategylist[0])
def __del__(self):
LoggingMixin.show_output = True
@@ -132,10 +126,17 @@ class Backtesting:
Load strategy into backtesting
"""
self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
if self.config.get('enable_protections', False):
conf = self.config
if hasattr(strategy, 'protections'):
conf = deepcopy(conf)
conf['protections'] = strategy.protections
self.protections = ProtectionManager(conf)
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
"""
@@ -159,7 +160,7 @@ class Backtesting:
logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
f'({(max_date - min_date).days} days).')
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
@@ -176,6 +177,8 @@ class Backtesting:
Trade.use_db = False
PairLocks.reset_locks()
Trade.reset_trades()
self.rejected_trades = 0
self.dataprovider.clear_cache()
def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
"""
@@ -189,8 +192,9 @@ class Backtesting:
data: Dict = {}
# Create dict with data
for pair, pair_data in processed.items():
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
if not pair_data.empty:
pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
@@ -214,6 +218,12 @@ class Backtesting:
"""
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
if trade.stop_loss > sell_row[HIGH_IDX]:
# our stoploss was already higher than candle high,
# possibly due to a cancelled trade exit.
# sell at open price.
return sell_row[OPEN_IDX]
# Set close_rate to stoploss
return trade.stop_loss
elif sell.sell_type == (SellType.ROI):
@@ -247,10 +257,9 @@ class Backtesting:
else:
return sell_row[OPEN_IDX]
def _get_sell_trade_entry(self, dataframe: DataFrame, trade: LocalTrade,
sell_row: Tuple) -> Optional[LocalTrade]:
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
sell = self.strategy.should_sell(dataframe, trade, sell_row[OPEN_IDX], # type: ignore
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
@@ -267,7 +276,8 @@ class Backtesting:
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason):
sell_reason=sell.sell_reason,
current_time=sell_row[DATE_IDX].to_pydatetime()):
return None
trade.close(closerate, show_msg=False)
@@ -287,7 +297,7 @@ class Backtesting:
# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
time_in_force=time_in_force):
time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
return None
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
@@ -327,10 +337,18 @@ class Backtesting:
trades.append(trade1)
return trades
def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
if max_open_trades <= 0 or open_trade_count < max_open_trades:
return True
# Rejected trade
self.rejected_trades += 1
return False
def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False,
enable_protections: bool = False) -> DataFrame:
enable_protections: bool = False) -> Dict[str, Any]:
"""
Implement backtesting functionality
@@ -349,6 +367,10 @@ class Backtesting:
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Update dataprovider cache
for pair, dataframe in processed.items():
self.dataprovider._set_cached_df(pair, self.timeframe, dataframe)
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
@@ -365,8 +387,9 @@ class Backtesting:
open_trade_count_start = open_trade_count
for i, pair in enumerate(data):
row_index = indexes[pair]
try:
row = data[pair][indexes[pair]]
row = data[pair][row_index]
except IndexError:
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
@@ -375,16 +398,22 @@ class Backtesting:
# Waits until the time-counter reaches the start of the data for this pair.
if row[DATE_IDX] > tmp:
continue
indexes[pair] += 1
row_index += 1
self.dataprovider._set_dataframe_max_index(row_index)
indexes[pair] = row_index
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
if ((position_stacking or len(open_trades[pair]) == 0)
and (max_open_trades <= 0 or open_trade_count_start < max_open_trades)
and tmp != end_date
and row[BUY_IDX] == 1 and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
if (
(position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and tmp != end_date
and row[BUY_IDX] == 1
and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
):
trade = self._enter_trade(pair, row)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
@@ -398,7 +427,7 @@ class Backtesting:
for trade in open_trades[pair]:
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(processed[pair], trade, row)
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occured
if trade_entry:
# logger.debug(f"{pair} - Backtesting sell {trade}")
@@ -417,7 +446,14 @@ class Backtesting:
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
return trade_list_to_dataframe(trades)
results = trade_list_to_dataframe(trades)
return {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'rejected_signals': self.rejected_trades,
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@@ -439,32 +475,32 @@ class Backtesting:
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange,
startup_candles=self.required_startup)
min_date, max_date = history.get_timerange(preprocessed)
preprocessed = trim_dataframes(preprocessed, timerange, self.required_startup)
if not preprocessed:
raise OperationalException(
"No data left after adjusting for startup candles.")
min_date, max_date = history.get_timerange(preprocessed)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
f'({(max_date - min_date).days} days).')
# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
start_date=min_date.datetime,
end_date=max_date.datetime,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
self.all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
}
})
self.all_results[self.strategy.get_strategy_name()] = results
return min_date, max_date
def start(self) -> None:

277
freqtrade/optimize/hyperopt.py
View File

@@ -7,20 +7,21 @@ This module contains the hyperopt logic
import logging
import random
import warnings
from datetime import datetime
from datetime import datetime, timezone
from math import ceil
from operator import itemgetter
from pathlib import Path
from typing import Any, Dict, List, Optional
import numpy as np
import progressbar
import rapidjson
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.misc import file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
@@ -29,8 +30,8 @@ from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_tools import HyperoptTools
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
from freqtrade.strategy import IStrategy
# Suppress scikit-learn FutureWarnings from skopt
@@ -64,6 +65,13 @@ class Hyperopt:
custom_hyperopt: IHyperOpt
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config
self.backtesting = Backtesting(self.config)
@@ -72,15 +80,15 @@ class Hyperopt:
self.custom_hyperopt = HyperOptAuto(self.config)
else:
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
strategy = str(self.config['strategy'])
self.results_file = (self.config['user_data_dir'] /
'hyperopt_results' /
f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
f'strategy_{strategy}_{time_now}.fthypt')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
@@ -90,9 +98,7 @@ class Hyperopt:
self.clean_hyperopt()
self.num_epochs_saved = 0
# Previous evaluations
self.epochs: List = []
self.current_best_epoch: Optional[Dict[str, Any]] = None
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
@@ -113,7 +119,7 @@ class Hyperopt:
self.max_open_trades = 0
self.position_stacking = self.config.get('position_stacking', False)
if self.has_space('sell'):
if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_sell_signal is enabled
if 'ask_strategy' not in self.config:
self.config['ask_strategy'] = {}
@@ -139,9 +145,7 @@ class Hyperopt:
logger.info(f"Removing `{p}`.")
p.unlink()
def _get_params_dict(self, raw_params: List[Any]) -> Dict:
dimensions: List[Dimension] = self.dimensions
def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
# Ensure the number of dimensions match
# the number of parameters in the list.
@@ -152,21 +156,30 @@ class Hyperopt:
# and the values are taken from the list of parameters.
return {d.name: v for d, v in zip(dimensions, raw_params)}
def _save_results(self) -> None:
def _save_result(self, epoch: Dict) -> None:
"""
Save hyperopt results to file
Store one line per epoch.
While not a valid json object - this allows appending easily.
:param epoch: result dictionary for this epoch.
"""
num_epochs = len(self.epochs)
if num_epochs > self.num_epochs_saved:
logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
dump(self.epochs, self.results_file)
self.num_epochs_saved = num_epochs
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
# Store hyperopt filename
latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
def default_parser(x):
if isinstance(x, np.integer):
return int(x)
return str(x)
with self.results_file.open('a') as f:
rapidjson.dump(epoch, f, default=default_parser,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN)
f.write("\n")
self.num_epochs_saved += 1
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
# Store hyperopt filename
latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
def _get_params_details(self, params: Dict) -> Dict:
"""
@@ -174,18 +187,16 @@ class Hyperopt:
"""
result: Dict = {}
if self.has_space('buy'):
result['buy'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('buy')}
if self.has_space('sell'):
result['sell'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('sell')}
if self.has_space('roi'):
result['roi'] = self.custom_hyperopt.generate_roi_table(params)
if self.has_space('stoploss'):
result['stoploss'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('stoploss')}
if self.has_space('trailing'):
if HyperoptTools.has_space(self.config, 'buy'):
result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
if HyperoptTools.has_space(self.config, 'sell'):
result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
if HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = {str(k): v for k, v in
self.custom_hyperopt.generate_roi_table(params).items()}
if HyperoptTools.has_space(self.config, 'stoploss'):
result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
return result
@@ -207,71 +218,58 @@ class Hyperopt:
)
self.hyperopt_table_header = 2
def has_space(self, space: str) -> bool:
def init_spaces(self):
"""
Tell if the space value is contained in the configuration
Assign the dimensions in the hyperoptimization space.
"""
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
return any(s in self.config['spaces'] for s in [space, 'all'])
else:
return any(s in self.config['spaces'] for s in [space, 'all', 'default'])
def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
"""
Return the dimensions in the hyperoptimization space.
:param space: Defines hyperspace to return dimensions for.
If None, then the self.has_space() will be used to return dimensions
for all hyperspaces used.
"""
spaces: List[Dimension] = []
if space == 'buy' or (space is None and self.has_space('buy')):
if HyperoptTools.has_space(self.config, 'buy'):
logger.debug("Hyperopt has 'buy' space")
spaces += self.custom_hyperopt.indicator_space()
self.buy_space = self.custom_hyperopt.indicator_space()
if space == 'sell' or (space is None and self.has_space('sell')):
if HyperoptTools.has_space(self.config, 'sell'):
logger.debug("Hyperopt has 'sell' space")
spaces += self.custom_hyperopt.sell_indicator_space()
self.sell_space = self.custom_hyperopt.sell_indicator_space()
if space == 'roi' or (space is None and self.has_space('roi')):
if HyperoptTools.has_space(self.config, 'roi'):
logger.debug("Hyperopt has 'roi' space")
spaces += self.custom_hyperopt.roi_space()
self.roi_space = self.custom_hyperopt.roi_space()
if space == 'stoploss' or (space is None and self.has_space('stoploss')):
if HyperoptTools.has_space(self.config, 'stoploss'):
logger.debug("Hyperopt has 'stoploss' space")
spaces += self.custom_hyperopt.stoploss_space()
self.stoploss_space = self.custom_hyperopt.stoploss_space()
if space == 'trailing' or (space is None and self.has_space('trailing')):
if HyperoptTools.has_space(self.config, 'trailing'):
logger.debug("Hyperopt has 'trailing' space")
spaces += self.custom_hyperopt.trailing_space()
return spaces
self.trailing_space = self.custom_hyperopt.trailing_space()
self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
self.stoploss_space + self.trailing_space)
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
"""
Used Optimize function. Called once per epoch to optimize whatever is configured.
Keep this function as optimized as possible!
"""
params_dict = self._get_params_dict(raw_params)
params_details = self._get_params_details(params_dict)
backtest_start_time = datetime.now(timezone.utc)
params_dict = self._get_params_dict(self.dimensions, raw_params)
if self.has_space('roi'):
# Apply parameters
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict))
if self.has_space('buy'):
if HyperoptTools.has_space(self.config, 'buy'):
self.backtesting.strategy.advise_buy = ( # type: ignore
self.custom_hyperopt.buy_strategy_generator(params_dict))
if self.has_space('sell'):
if HyperoptTools.has_space(self.config, 'sell'):
self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.sell_strategy_generator(params_dict))
if self.has_space('stoploss'):
if HyperoptTools.has_space(self.config, 'stoploss'):
self.backtesting.strategy.stoploss = params_dict['stoploss']
if self.has_space('trailing'):
if HyperoptTools.has_space(self.config, 'trailing'):
d = self.custom_hyperopt.generate_trailing_params(params_dict)
self.backtesting.strategy.trailing_stop = d['trailing_stop']
self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
@@ -280,30 +278,42 @@ class Hyperopt:
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
processed = load(self.data_pickle_file)
min_date, max_date = get_timerange(processed)
backtesting_results = self.backtesting.backtest(
with self.data_pickle_file.open('rb') as f:
processed = load(f, mmap_mode='r')
bt_results = self.backtesting.backtest(
processed=processed,
start_date=min_date.datetime,
end_date=max_date.datetime,
start_date=self.min_date,
end_date=self.max_date,
max_open_trades=self.max_open_trades,
position_stacking=self.position_stacking,
enable_protections=self.config.get('enable_protections', False),
)
return self._get_results_dict(backtesting_results, min_date, max_date,
params_dict, params_details,
backtest_end_time = datetime.now(timezone.utc)
bt_results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
return self._get_results_dict(bt_results, self.min_date, self.max_date,
params_dict,
processed=processed)
def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict, params_details, processed: Dict[str, DataFrame]):
results_metrics = self._calculate_results_metrics(backtesting_results)
results_explanation = self._format_results_explanation_string(results_metrics)
params_dict, processed: Dict[str, DataFrame]
) -> Dict[str, Any]:
params_details = self._get_params_details(params_dict)
trade_count = results_metrics['trade_count']
total_profit = results_metrics['total_profit']
strat_stats = generate_strategy_stats(
processed, self.backtesting.strategy.get_strategy_name(),
backtesting_results, min_date, max_date, market_change=0
)
results_explanation = HyperoptTools.format_results_explanation_string(
strat_stats, self.config['stake_currency'])
not_optimized = self.backtesting.strategy.get_params_dict()
trade_count = strat_stats['total_trades']
total_profit = strat_stats['profit_total']
# If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value)
@@ -311,50 +321,20 @@ class Hyperopt:
# path. We do not want to optimize 'hodl' strategies.
loss: float = MAX_LOSS
if trade_count >= self.config['hyperopt_min_trades']:
loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
min_date=min_date.datetime, max_date=max_date.datetime,
loss = self.calculate_loss(results=backtesting_results['results'],
trade_count=trade_count,
min_date=min_date, max_date=max_date,
config=self.config, processed=processed)
return {
'loss': loss,
'params_dict': params_dict,
'params_details': params_details,
'results_metrics': results_metrics,
'params_not_optimized': not_optimized,
'results_metrics': strat_stats,
'results_explanation': results_explanation,
'total_profit': total_profit,
}
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0])
draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0])
losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0])
return {
'trade_count': len(backtesting_results.index),
'wins': wins,
'draws': draws,
'losses': losses,
'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0,
'median_profit': backtesting_results['profit_ratio'].median() * 100.0,
'total_profit': backtesting_results['profit_abs'].sum(),
'profit': backtesting_results['profit_ratio'].sum() * 100.0,
'duration': backtesting_results['trade_duration'].mean(),
}
def _format_results_explanation_string(self, results_metrics: Dict) -> str:
"""
Return the formatted results explanation in a string
"""
stake_cur = self.config['stake_currency']
return (f"{results_metrics['trade_count']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
f"Median profit {results_metrics['median_profit']: 6.2f}%. "
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
f"({results_metrics['profit']: 7.2f}%). "
f"Avg duration {results_metrics['duration']:5.1f} min."
)
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
return Optimizer(
dimensions,
@@ -373,25 +353,31 @@ class Hyperopt:
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
def prepare_hyperopt_data(self) -> None:
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(processed)
logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
dump(processed, self.data_pickle_file)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Initialize spaces ...
self.init_spaces()
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange,
startup_candles=self.backtesting.required_startup)
min_date, max_date = get_timerange(preprocessed)
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
dump(preprocessed, self.data_pickle_file)
self.prepare_hyperopt_data()
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange.close()
@@ -399,15 +385,12 @@ class Hyperopt:
self.backtesting.exchange._api_async = None # type: ignore
# self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
cpus = cpu_count()
logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
config_jobs = self.config.get('hyperopt_jobs', -1)
logger.info(f'Number of parallel jobs set as: {config_jobs}')
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:
@@ -473,25 +456,21 @@ class Hyperopt:
if is_best:
self.current_best_loss = val['loss']
self.epochs.append(val)
self.current_best_epoch = val
# Save results after each best epoch and every 100 epochs
if is_best or current % 100 == 0:
self._save_results()
self._save_result(val)
pbar.update(current)
except KeyboardInterrupt:
print('User interrupted..')
self._save_results()
logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
if self.epochs:
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
best_epoch = sorted_epochs[0]
HyperoptTools.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
if self.current_best_epoch:
HyperoptTools.print_epoch_details(self.current_best_epoch, self.total_epochs,
self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.

193
freqtrade/optimize/hyperopt_tools.py
View File

@@ -3,17 +3,15 @@ import io
import logging
from collections import OrderedDict
from pathlib import Path
from pprint import pformat
from typing import Dict, List
from typing import Any, Dict, List
import rapidjson
import tabulate
from colorama import Fore, Style
from joblib import load
from pandas import isna, json_normalize
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_dict
from freqtrade.misc import round_coin_value, round_dict
logger = logging.getLogger(__name__)
@@ -21,13 +19,38 @@ logger = logging.getLogger(__name__)
class HyperoptTools():
@staticmethod
def has_space(config: Dict[str, Any], space: str) -> bool:
"""
Tell if the space value is contained in the configuration
"""
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
return any(s in config['spaces'] for s in [space, 'all'])
else:
return any(s in config['spaces'] for s in [space, 'all', 'default'])
@staticmethod
def _read_results_pickle(results_file: Path) -> List:
"""
Read hyperopt results from pickle file
LEGACY method - new files are written as json and cannot be read with this method.
"""
from joblib import load
logger.info(f"Reading pickled epochs from '{results_file}'")
data = load(results_file)
return data
@staticmethod
def _read_results(results_file: Path) -> List:
"""
Read hyperopt results from file
"""
logger.info("Reading epochs from '%s'", results_file)
data = load(results_file)
import rapidjson
logger.info(f"Reading epochs from '{results_file}'")
with results_file.open('r') as f:
data = [rapidjson.loads(line) for line in f]
return data
@staticmethod
@@ -37,7 +60,10 @@ class HyperoptTools():
"""
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
epochs = HyperoptTools._read_results(results_file)
if results_file.suffix == '.pickle':
epochs = HyperoptTools._read_results_pickle(results_file)
else:
epochs = HyperoptTools._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
@@ -53,6 +79,7 @@ class HyperoptTools():
Display details of the hyperopt result
"""
params = results.get('params_details', {})
non_optimized = results.get('params_not_optimized', {})
# Default header string
if header_str is None:
@@ -69,8 +96,10 @@ class HyperoptTools():
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:")
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:")
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
@@ -96,12 +125,12 @@ class HyperoptTools():
result_dict.update(space_params)
@staticmethod
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
if space in params or space in non_optimized:
space_params = HyperoptTools._space_params(params, space, 5)
params_result = f"\n# {header}\n"
result = f"\n# {header}\n"
if space == 'stoploss':
params_result += f"stoploss = {space_params.get('stoploss')}"
result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
@@ -110,28 +139,64 @@ class HyperoptTools():
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing':
for k, v in space_params.items():
params_result += f'{k} = {v}\n'
result += f'{k} = {v}\n'
else:
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
no_params = HyperoptTools._space_params(non_optimized, space, 5)
params_result = params_result.replace("\n", "\n ")
print(params_result)
result += f"{space}_params = {HyperoptTools._pprint(space_params, no_params)}"
result = result.replace("\n", "\n ")
print(result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
d = params[space]
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
d = params.get(space)
if d:
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
return {}
@staticmethod
def _pprint(params, non_optimized, indent: int = 4):
"""
Pretty-print hyperopt results (based on 2 dicts - with add. comment)
"""
p = params.copy()
p.update(non_optimized)
result = '{\n'
for k, param in p.items():
result += " " * indent + f'"{k}": '
result += f'"{param}",' if isinstance(param, str) else f'{param},'
if k in non_optimized:
result += " # value loaded from strategy"
result += "\n"
result += '}'
return result
@staticmethod
def is_best_loss(results, current_best_loss: float) -> bool:
return results['loss'] < current_best_loss
return bool(results['loss'] < current_best_loss)
@staticmethod
def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
"""
Return the formatted results explanation in a string
"""
return (f"{results_metrics['total_trades']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
f"({results_metrics['profit_total'] * 100: 7.2f}%). "
f"Avg duration {results_metrics['holding_avg']} min."
)
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
@@ -156,12 +221,27 @@ class HyperoptTools():
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
legacy_mode = True
if 'results_metrics.total_trades' in trials:
legacy_mode = False
# New mode, using backtest result for metrics
trials['results_metrics.winsdrawslosses'] = trials.apply(
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
f"{x['results_metrics.losses']:>4}", axis=1)
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.winsdrawslosses',
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
'results_metrics.profit_total', 'results_metrics.holding_avg',
'loss', 'is_initial_point', 'is_best']]
else:
# Legacy mode
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
@@ -171,26 +251,28 @@ class HyperoptTools():
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
perc_multi = 1 if legacy_mode else 100
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
stake_currency = config['stake_currency']
trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format(
x['Total profit'], config['stake_currency'],
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
).rjust(25+len(config['stake_currency']))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
lambda x: '{} {}'.format(
round_coin_value(x['Total profit'], stake_currency),
'({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
).rjust(25+len(stake_currency))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
axis=1
)
trials = trials.drop(columns=['Total profit'])
@@ -251,11 +333,21 @@ class HyperoptTools():
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.median_profit',
'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
if 'results_metrics.total_trades' in trials:
base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.profit_mean', 'results_metrics.profit_median',
'results_metrics.profit_total',
'Stake currency',
'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
'loss', 'is_initial_point', 'is_best']
perc_multi = 100
else:
perc_multi = 1
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.median_profit',
'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
@@ -272,21 +364,24 @@ class HyperoptTools():
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Median profit'] = trials['Median profit'] * perc_multi
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
lambda x: f'{x:,.8f}' if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
lambda x: f'{x:,.2f}' if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
)
if perc_multi == 1:
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: f'{x:,.1f} m' if isinstance(
x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
lambda x: f'{x:,.5f}' if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])

445
freqtrade/optimize/optimize_reports.py
View File

@@ -3,7 +3,6 @@ from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List, Union
from arrow import Arrow
from numpy import int64
from pandas import DataFrame
from tabulate import tabulate
@@ -44,7 +43,7 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
Generate floatformat (goes in line with _generate_result_line())
"""
return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
'.2f', 'd', 'd', 'd', 'd']
'.2f', 'd', 's', 's']
def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
@@ -53,7 +52,17 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
"""
return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Wins', 'Draws', 'Losses']
'Win Draw Loss Win%']
def _generate_wins_draws_losses(wins, draws, losses):
if wins > 0 and losses == 0:
wl_ratio = '100'
elif wins == 0:
wl_ratio = '0'
else:
wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
return f'{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}'
def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
@@ -153,7 +162,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
return tabular_data
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
@@ -165,6 +174,17 @@ def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
tabular_data.append(_generate_result_line(
results['results'], results['config']['dry_run_wallet'], strategy)
)
try:
max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_ratio')
max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_abs')
except ValueError:
max_drawdown_per = 0
max_drawdown_abs = 0
tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
tabular_data[-1]['max_drawdown_abs'] = \
round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
return tabular_data
@@ -194,7 +214,40 @@ def generate_edge_table(results: dict) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
""" Generate overall trade statistics """
if len(results) == 0:
return {
'wins': 0,
'losses': 0,
'draws': 0,
'holding_avg': timedelta(),
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
winning_trades = results.loc[results['profit_ratio'] > 0]
draw_trades = results.loc[results['profit_ratio'] == 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
zero_duration_trades = len(results.loc[(results['trade_duration'] == 0) &
(results['sell_reason'] == 'trailing_stop_loss')])
return {
'wins': len(winning_trades),
'losses': len(losing_trades),
'draws': len(draw_trades),
'holding_avg': (timedelta(minutes=round(results['trade_duration'].mean()))
if not results.empty else timedelta()),
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
'zero_duration_trades': zero_duration_trades,
}
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
""" Generate daily statistics """
if len(results) == 0:
return {
'backtest_best_day': 0,
@@ -204,8 +257,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
'winning_days': 0,
'draw_days': 0,
'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
@@ -217,9 +268,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
winning_trades = results.loc[results['profit_ratio'] > 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
return {
'backtest_best_day': best_rel,
'backtest_worst_day': worst_rel,
@@ -228,16 +276,152 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
}
def generate_strategy_stats(btdata: Dict[str, DataFrame],
strategy: str,
content: Dict[str, Any],
min_date: datetime, max_date: datetime,
market_change: float
) -> Dict[str, Any]:
"""
:param btdata: Backtest data
:param strategy: Strategy name
:param content: Backtest result data in the format:
{'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:param market_change: float indicating the market change
:return: Dictionary containing results per strategy and a stratgy summary.
"""
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
return {}
config = content['config']
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
starting_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results.loc[results['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
trade_stats = generate_trading_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = {
'trades': results.to_dict(orient='records'),
'locks': [lock.to_json() for lock in content['locks']],
'best_pair': best_pair,
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
'profit_total': results['profit_abs'].sum() / starting_balance,
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts': int(min_date.timestamp() * 1000),
'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_end_ts': int(max_date.timestamp() * 1000),
'backtest_days': backtest_days,
'backtest_run_start_ts': content['backtest_start_time'],
'backtest_run_end_ts': content['backtest_end_time'],
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'starting_balance': starting_balance,
'dry_run_wallet': starting_balance,
'final_balance': content['final_balance'],
'rejected_signals': content['rejected_signals'],
'max_open_trades': max_open_trades,
'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
'timerange': config.get('timerange', ''),
'enable_protections': config.get('enable_protections', False),
'strategy_name': strategy,
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'use_custom_stoploss': config.get('use_custom_stoploss', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
**trade_stats
}
try:
max_drawdown, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
results, value_col='profit_abs')
strat_stats.update({
'max_drawdown': max_drawdown,
'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end.strftime(DATETIME_PRINT_FORMAT),
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
'max_drawdown_low': low_val,
'max_drawdown_high': high_val,
})
csum_min, csum_max = calculate_csum(results, starting_balance)
strat_stats.update({
'csum_min': csum_min,
'csum_max': csum_max
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0,
'max_drawdown_high': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
'csum_min': 0,
'csum_max': 0
})
return strat_stats
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
min_date: Arrow, max_date: Arrow
min_date: datetime, max_date: datetime
) -> Dict[str, Any]:
"""
:param btdata: Backtest data
@@ -245,132 +429,17 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
{ Strategy: {'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
:return: Dictionary containing results per strategy and a stratgy summary.
"""
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, content in all_results.items():
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
continue
config = content['config']
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
starting_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results.loc[results['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = {
'trades': results.to_dict(orient='records'),
'locks': [lock.to_json() for lock in content['locks']],
'best_pair': best_pair,
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_abs'].sum() / starting_balance,
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.int_timestamp * 1000,
'backtest_end': max_date.datetime,
'backtest_end_ts': max_date.int_timestamp * 1000,
'backtest_days': backtest_days,
'backtest_run_start_ts': content['backtest_start_time'],
'backtest_run_end_ts': content['backtest_end_time'],
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'starting_balance': starting_balance,
'dry_run_wallet': starting_balance,
'final_balance': content['final_balance'],
'max_open_trades': max_open_trades,
'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
'timerange': config.get('timerange', ''),
'enable_protections': config.get('enable_protections', False),
'strategy_name': strategy,
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'use_custom_stoploss': config.get('use_custom_stoploss', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
}
strat_stats = generate_strategy_stats(btdata, strategy, content,
min_date, max_date, market_change=market_change)
result['strategy'][strategy] = strat_stats
try:
max_drawdown, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
results, value_col='profit_abs')
strat_stats.update({
'max_drawdown': max_drawdown,
'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start,
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end,
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
'max_drawdown_low': low_val,
'max_drawdown_high': high_val,
})
csum_min, csum_max = calculate_csum(results, starting_balance)
strat_stats.update({
'csum_min': csum_min,
'csum_max': csum_max
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0,
'max_drawdown_high': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
'csum_min': 0,
'csum_max': 0
})
strategy_results = generate_strategy_metrics(all_results=all_results)
strategy_results = generate_strategy_comparison(all_results=all_results)
result['strategy_comparison'] = strategy_results
@@ -393,7 +462,8 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
floatfmt = _get_line_floatfmt(stake_currency)
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
] for t in pair_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
@@ -410,9 +480,7 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
headers = [
'Sell Reason',
'Sells',
'Wins',
'Draws',
'Losses',
'Win Draws Loss Win%',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
@@ -420,7 +488,8 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
]
output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
t['sell_reason'], t['trades'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
t['profit_mean_pct'], t['profit_sum_pct'],
round_coin_value(t['profit_total_abs'], stake_currency, False),
t['profit_total_pct'],
@@ -438,11 +507,22 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
"""
floatfmt = _get_line_floatfmt(stake_currency)
headers = _get_line_header('Strategy', stake_currency)
# _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
# therefore we slip this column in only for strategy summary here.
headers.append('Drawdown')
# Align drawdown string on the center two space separator.
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
dd_pad_per = max([len(dd) for dd in drawdown])
drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
for t, dd in zip(strategy_results, drawdown)]
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
] for t in strategy_results]
t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
for t, drawdown in zip(strategy_results, drawdown)]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
@@ -452,9 +532,21 @@ def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
# command stores these results and newer version of freqtrade must be able to handle old
# results with missing new fields.
zero_duration_trades = '--'
if 'zero_duration_trades' in strat_results:
zero_duration_trades_per = \
100.0 / strat_results['total_trades'] * strat_results['zero_duration_trades']
zero_duration_trades = f'{zero_duration_trades_per:.2f}% ' \
f'({strat_results["zero_duration_trades"]})'
metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting from', strat_results['backtest_start']),
('Backtesting to', strat_results['backtest_end']),
('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability
('Total trades', strat_results['total_trades']),
@@ -464,13 +556,12 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2):}%"),
('Trades per day', strat_results['trades_per_day']),
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'],
strat_results['stake_currency'])),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
@@ -488,6 +579,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('Zero Duration Trades', zero_duration_trades),
('Rejected Buy signals', strat_results.get('rejected_signals', 'N/A')),
('', ''), # Empty line to improve readability
('Min balance', round_coin_value(strat_results['csum_min'],
@@ -502,8 +595,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end']),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
]
@@ -522,37 +615,43 @@ def text_table_add_metrics(strat_results: Dict) -> str:
return message
def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str):
"""
Print results for one strategy
"""
# Print results
print(f"Result for strategy {strategy}")
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()
def show_backtest_results(config: Dict, backtest_stats: Dict):
stake_currency = config['stake_currency']
for strategy, results in backtest_stats['strategy'].items():
# Print results
print(f"Result for strategy {strategy}")
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()
show_backtest_result(strategy, results, stake_currency)
if len(backtest_stats['strategy']) > 1:
# Print Strategy summary table

32
freqtrade/persistence/migrations.py
View File

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

46
freqtrade/persistence/models.py
View File

@@ -112,16 +112,17 @@ class Order(_DECL_BASE):
trade = relationship("Trade", back_populates="orders")
ft_order_side = Column(String, nullable=False)
ft_pair = Column(String, nullable=False)
ft_order_side = Column(String(25), nullable=False)
ft_pair = Column(String(25), nullable=False)
ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
order_id = Column(String, nullable=False, index=True)
status = Column(String, nullable=True)
symbol = Column(String, nullable=True)
order_type = Column(String, nullable=True)
side = Column(String, nullable=True)
order_id = Column(String(255), nullable=False, index=True)
status = Column(String(255), nullable=True)
symbol = Column(String(25), nullable=True)
order_type = Column(String(50), nullable=True)
side = Column(String(25), nullable=True)
price = Column(Float, nullable=True)
average = Column(Float, nullable=True)
amount = Column(Float, nullable=True)
filled = Column(Float, nullable=True)
remaining = Column(Float, nullable=True)
@@ -150,6 +151,7 @@ class Order(_DECL_BASE):
self.price = order.get('price', self.price)
self.amount = order.get('amount', self.amount)
self.filled = order.get('filled', self.filled)
self.average = order.get('average', self.average)
self.remaining = order.get('remaining', self.remaining)
self.cost = order.get('cost', self.cost)
if 'timestamp' in order and order['timestamp'] is not None:
@@ -656,15 +658,15 @@ class Trade(_DECL_BASE, LocalTrade):
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
exchange = Column(String, nullable=False)
pair = Column(String, nullable=False, index=True)
exchange = Column(String(25), nullable=False)
pair = Column(String(25), nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True)
fee_open_currency = Column(String, nullable=True)
fee_open_currency = Column(String(25), nullable=True)
fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True)
fee_close_currency = Column(String, nullable=True)
fee_close_currency = Column(String(25), nullable=True)
open_rate = Column(Float)
open_rate_requested = Column(Float)
# open_trade_value - calculated via _calc_open_trade_value
@@ -678,7 +680,7 @@ class Trade(_DECL_BASE, LocalTrade):
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
open_order_id = Column(String(255))
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the stop loss
@@ -688,16 +690,16 @@ class Trade(_DECL_BASE, LocalTrade):
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
# stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True)
stoploss_order_id = Column(String(255), nullable=True, index=True)
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
# Lowest price reached
min_rate = Column(Float, nullable=True)
sell_reason = Column(String, nullable=True)
sell_order_status = Column(String, nullable=True)
strategy = Column(String, nullable=True)
sell_reason = Column(String(100), nullable=True)
sell_order_status = Column(String(100), nullable=True)
strategy = Column(String(100), nullable=True)
timeframe = Column(Integer, nullable=True)
def __init__(self, **kwargs):
@@ -815,18 +817,20 @@ class Trade(_DECL_BASE, LocalTrade):
pair_rates = Trade.query.with_entities(
Trade.pair,
func.sum(Trade.close_profit).label('profit_sum'),
func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
func.count(Trade.pair).label('count')
).filter(Trade.is_open.is_(False))\
.group_by(Trade.pair) \
.order_by(desc('profit_sum')) \
.order_by(desc('profit_sum_abs')) \
.all()
return [
{
'pair': pair,
'profit': rate,
'profit': profit,
'profit_abs': profit_abs,
'count': count
}
for pair, rate, count in pair_rates
for pair, profit, profit_abs, count in pair_rates
]
@staticmethod
@@ -852,8 +856,8 @@ class PairLock(_DECL_BASE):
id = Column(Integer, primary_key=True)
pair = Column(String, nullable=False, index=True)
reason = Column(String, nullable=True)
pair = Column(String(25), nullable=False, index=True)
reason = Column(String(255), nullable=True)
# Time the pair was locked (start time)
lock_time = Column(DateTime, nullable=False)
# Time until the pair is locked (end time)

10
freqtrade/plot/plotting.py
View File

@@ -77,7 +77,8 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
)
except ValueError as e:
raise OperationalException(e) from e
trades = trim_dataframe(trades, timerange, 'open_date')
if not trades.empty:
trades = trim_dataframe(trades, timerange, 'open_date')
return {"ohlcv": data,
"trades": trades,
@@ -540,8 +541,11 @@ def load_and_plot_trades(config: Dict[str, Any]):
df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
df_analyzed = trim_dataframe(df_analyzed, timerange)
trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
if not trades.empty:
trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
else:
trades_pair = trades
fig = generate_candlestick_graph(
pair=pair,

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

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

8
freqtrade/plugins/pairlist/IPairList.py
View File

@@ -7,7 +7,7 @@ from copy import deepcopy
from typing import Any, Dict, List
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active
from freqtrade.exchange import Exchange, market_is_active
from freqtrade.mixins import LoggingMixin
@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
class IPairList(LoggingMixin, ABC):
def __init__(self, exchange, pairlistmanager,
def __init__(self, exchange: Exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
"""
@@ -28,7 +28,7 @@ class IPairList(LoggingMixin, ABC):
"""
self._enabled = True
self._exchange = exchange
self._exchange: Exchange = exchange
self._pairlistmanager = pairlistmanager
self._config = config
self._pairlistconfig = pairlistconfig
@@ -68,7 +68,7 @@ class IPairList(LoggingMixin, ABC):
filter_pairlist() method.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
raise NotImplementedError()

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

@@ -39,7 +39,12 @@ class PerformanceFilter(IPairList):
:return: new allowlist
"""
# Get the trading performance for pairs from database
performance = pd.DataFrame(Trade.get_overall_performance())
try:
performance = pd.DataFrame(Trade.get_overall_performance())
except AttributeError:
# Performancefilter does not work in backtesting.
self.log_once("PerformanceFilter is not available in this mode.", logger.warning)
return pairlist
# Skip performance-based sorting if no performance data is available
if len(performance) == 0:

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

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

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

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

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

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

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

@@ -90,7 +90,7 @@ class VolatilityFilter(IPairList):
"""
Validate trading range
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache

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

@@ -83,7 +83,7 @@ class RangeStabilityFilter(IPairList):
"""
Validate trading range
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache

3
freqtrade/rpc/api_server/api_schemas.py
View File

@@ -57,6 +57,7 @@ class Count(BaseModel):
class PerformanceEntry(BaseModel):
pair: str
profit: float
profit_abs: float
count: int
@@ -268,7 +269,7 @@ class DeleteTrade(BaseModel):
class PlotConfig_(BaseModel):
main_plot: Dict[str, Any]
subplots: Optional[Dict[str, Any]]
subplots: Dict[str, Any]
class PlotConfig(BaseModel):

27
freqtrade/rpc/fiat_convert.py
View File

@@ -3,11 +3,13 @@ Module that define classes to convert Crypto-currency to FIAT
e.g BTC to USD
"""
import datetime
import logging
from typing import Dict
from cachetools.ttl import TTLCache
from pycoingecko import CoinGeckoAPI
from requests.exceptions import RequestException
from freqtrade.constants import SUPPORTED_FIAT
@@ -25,6 +27,7 @@ class CryptoToFiatConverter:
_coingekko: CoinGeckoAPI = None
_cryptomap: Dict = {}
_backoff: float = 0.0
def __new__(cls):
"""
@@ -47,8 +50,21 @@ class CryptoToFiatConverter:
def _load_cryptomap(self) -> None:
try:
coinlistings = self._coingekko.get_coins_list()
# Create mapping table from synbol to coingekko_id
# Create mapping table from symbol to coingekko_id
self._cryptomap = {x['symbol']: x['id'] for x in coinlistings}
except RequestException as request_exception:
if "429" in str(request_exception):
logger.warning(
"Too many requests for Coingecko API, backing off and trying again later.")
# Set backoff timestamp to 60 seconds in the future
self._backoff = datetime.datetime.now().timestamp() + 60
return
# If the request is not a 429 error we want to raise the normal error
logger.error(
"Could not load FIAT Cryptocurrency map for the following problem: {}".format(
request_exception
)
)
except (Exception) as exception:
logger.error(
f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
@@ -127,6 +143,15 @@ class CryptoToFiatConverter:
if crypto_symbol == fiat_symbol:
return 1.0
if self._cryptomap == {}:
if self._backoff <= datetime.datetime.now().timestamp():
self._load_cryptomap()
# return 0.0 if we still dont have data to check, no reason to proceed
if self._cryptomap == {}:
return 0.0
else:
return 0.0
if crypto_symbol not in self._cryptomap:
# return 0 for unsupported stake currencies (fiat-convert should not break the bot)
logger.warning("unsupported crypto-symbol %s - returning 0.0", crypto_symbol)

13
freqtrade/rpc/rpc.py
View File

@@ -178,7 +178,7 @@ class RPC:
current_rate = trade.close_rate
current_profit = trade.calc_profit_ratio(current_rate)
current_profit_abs = trade.calc_profit(current_rate)
current_profit_fiat: Optional[float] = None
# Calculate fiat profit
if self._fiat_converter:
current_profit_fiat = self._fiat_converter.convert_amount(
@@ -220,12 +220,13 @@ class RPC:
return results
def _rpc_status_table(self, stake_currency: str,
fiat_display_currency: str) -> Tuple[List, List]:
fiat_display_currency: str) -> Tuple[List, List, float]:
trades = Trade.get_open_trades()
if not trades:
raise RPCException('no active trade')
else:
trades_list = []
fiat_profit_sum = NAN
for trade in trades:
# calculate profit and send message to user
try:
@@ -243,6 +244,8 @@ class RPC:
)
if fiat_profit and not isnan(fiat_profit):
profit_str += f" ({fiat_profit:.2f})"
fiat_profit_sum = fiat_profit if isnan(fiat_profit_sum) \
else fiat_profit_sum + fiat_profit
trades_list.append([
trade.id,
trade.pair + ('*' if (trade.open_order_id is not None
@@ -256,7 +259,7 @@ class RPC:
profitcol += " (" + fiat_display_currency + ")"
columns = ['ID', 'Pair', 'Since', profitcol]
return trades_list, columns
return trades_list, columns, fiat_profit_sum
def _rpc_daily_profit(
self, timescale: int,
@@ -845,5 +848,7 @@ class RPC:
df_analyzed, arrow.Arrow.utcnow().datetime)
def _rpc_plot_config(self) -> Dict[str, Any]:
if (self._freqtrade.strategy.plot_config and
'subplots' not in self._freqtrade.strategy.plot_config):
self._freqtrade.strategy.plot_config['subplots'] = {}
return self._freqtrade.strategy.plot_config

183
freqtrade/rpc/telegram.py
View File

@@ -8,19 +8,21 @@ import logging
from datetime import timedelta
from html import escape
from itertools import chain
from typing import Any, Callable, Dict, List, Union
from math import isnan
from typing import Any, Callable, Dict, List, Optional, Union, cast
import arrow
from tabulate import tabulate
from telegram import KeyboardButton, ParseMode, ReplyKeyboardMarkup, Update
from telegram import (InlineKeyboardButton, InlineKeyboardMarkup, KeyboardButton, ParseMode,
ReplyKeyboardMarkup, Update)
from telegram.error import NetworkError, TelegramError
from telegram.ext import CallbackContext, CommandHandler, Updater
from telegram.ext import CallbackContext, CallbackQueryHandler, CommandHandler, Updater
from telegram.utils.helpers import escape_markdown
from freqtrade.__init__ import __version__
from freqtrade.constants import DUST_PER_COIN
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_coin_value
from freqtrade.misc import chunks, round_coin_value
from freqtrade.rpc import RPC, RPCException, RPCHandler, RPCMessageType
@@ -87,7 +89,7 @@ class Telegram(RPCHandler):
Validates the keyboard configuration from telegram config
section.
"""
self._keyboard: List[List[Union[str, KeyboardButton]]] = [
self._keyboard: List[List[Union[str, KeyboardButton, InlineKeyboardButton]]] = [
['/daily', '/profit', '/balance'],
['/status', '/status table', '/performance'],
['/count', '/start', '/stop', '/help']
@@ -169,6 +171,11 @@ class Telegram(RPCHandler):
[h.command for h in handles]
)
self._current_callback_query_handler: Optional[CallbackQueryHandler] = None
self._callback_query_handlers = {
'forcebuy': CallbackQueryHandler(self._forcebuy_inline)
}
def cleanup(self) -> None:
"""
Stops all running telegram threads.
@@ -226,44 +233,58 @@ class Telegram(RPCHandler):
def send_msg(self, msg: Dict[str, Any]) -> None:
""" Send a message to telegram channel """
noti = self._config['telegram'].get('notification_settings', {}
).get(str(msg['type']), 'on')
default_noti = 'on'
msg_type = msg['type']
noti = ''
if msg_type == RPCMessageType.SELL:
sell_noti = self._config['telegram'] \
.get('notification_settings', {}).get(str(msg_type), {})
# For backward compatibility sell still be string
if isinstance(noti, str):
noti = sell_noti
else:
noti = sell_noti.get(str(msg['sell_reason']), default_noti)
else:
noti = self._config['telegram'] \
.get('notification_settings', {}).get(str(msg_type), default_noti)
if noti == 'off':
logger.info(f"Notification '{msg['type']}' not sent.")
logger.info(f"Notification '{msg_type}' not sent.")
# Notification disabled
return
if msg['type'] == RPCMessageType.BUY:
if msg_type == RPCMessageType.BUY:
message = self._format_buy_msg(msg)
elif msg['type'] in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
msg['message_side'] = 'buy' if msg['type'] == RPCMessageType.BUY_CANCEL else 'sell'
elif msg_type in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
msg['message_side'] = 'buy' if msg_type == RPCMessageType.BUY_CANCEL else 'sell'
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling open {message_side} Order for {pair} (#{trade_id}). "
"Reason: {reason}.".format(**msg))
elif msg['type'] == RPCMessageType.BUY_FILL:
elif msg_type == RPCMessageType.BUY_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Buy order for {pair} (#{trade_id}) filled "
"for {open_rate}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL_FILL:
elif msg_type == RPCMessageType.SELL_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Sell order for {pair} (#{trade_id}) filled "
"for {close_rate}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL:
elif msg_type == RPCMessageType.SELL:
message = self._format_sell_msg(msg)
elif msg['type'] == RPCMessageType.STATUS:
elif msg_type == RPCMessageType.STATUS:
message = '*Status:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.WARNING:
elif msg_type == RPCMessageType.WARNING:
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.STARTUP:
elif msg_type == RPCMessageType.STARTUP:
message = '{status}'.format(**msg)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
raise NotImplementedError('Unknown message type: {}'.format(msg_type))
self._send_msg(message, disable_notification=(noti == 'silent'))
@@ -354,19 +375,31 @@ class Telegram(RPCHandler):
:return: None
"""
try:
statlist, head = self._rpc._rpc_status_table(
self._config['stake_currency'], self._config.get('fiat_display_currency', ''))
fiat_currency = self._config.get('fiat_display_currency', '')
statlist, head, fiat_profit_sum = self._rpc._rpc_status_table(
self._config['stake_currency'], fiat_currency)
show_total = not isnan(fiat_profit_sum) and len(statlist) > 1
max_trades_per_msg = 50
"""
Calculate the number of messages of 50 trades per message
0.99 is used to make sure that there are no extra (empty) messages
As an example with 50 trades, there will be int(50/50 + 0.99) = 1 message
"""
for i in range(0, max(int(len(statlist) / max_trades_per_msg + 0.99), 1)):
message = tabulate(statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg],
messages_count = max(int(len(statlist) / max_trades_per_msg + 0.99), 1)
for i in range(0, messages_count):
trades = statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg]
if show_total and i == messages_count - 1:
# append total line
trades.append(["Total", "", "", f"{fiat_profit_sum:.2f} {fiat_currency}"])
message = tabulate(trades,
headers=head,
tablefmt='simple')
if show_total and i == messages_count - 1:
# insert separators line between Total
lines = message.split("\n")
message = "\n".join(lines[:-1] + [lines[1]] + [lines[-1]])
self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@@ -624,6 +657,25 @@ class Telegram(RPCHandler):
except RPCException as e:
self._send_msg(str(e))
def _forcebuy_action(self, pair, price=None):
try:
self._rpc._rpc_forcebuy(pair, price)
except RPCException as e:
self._send_msg(str(e))
def _forcebuy_inline(self, update: Update, _: CallbackContext) -> None:
if update.callback_query:
query = update.callback_query
pair = query.data
query.answer()
query.edit_message_text(text=f"Force Buying: {pair}")
self._forcebuy_action(pair)
@staticmethod
def _layout_inline_keyboard(buttons: List[InlineKeyboardButton],
cols=3) -> List[List[InlineKeyboardButton]]:
return [buttons[i:i + cols] for i in range(0, len(buttons), cols)]
@authorized_only
def _forcebuy(self, update: Update, context: CallbackContext) -> None:
"""
@@ -636,10 +688,13 @@ class Telegram(RPCHandler):
if context.args:
pair = context.args[0]
price = float(context.args[1]) if len(context.args) > 1 else None
try:
self._rpc._rpc_forcebuy(pair, price)
except RPCException as e:
self._send_msg(str(e))
self._forcebuy_action(pair, price)
else:
whitelist = self._rpc._rpc_whitelist()['whitelist']
pairs = [InlineKeyboardButton(pair, callback_data=pair) for pair in whitelist]
self._send_inline_msg("Which pair?",
keyboard=self._layout_inline_keyboard(pairs),
callback_query_handler='forcebuy')
@authorized_only
def _trades(self, update: Update, context: CallbackContext) -> None:
@@ -711,8 +766,11 @@ class Telegram(RPCHandler):
trades = self._rpc._rpc_performance()
output = "<b>Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (f"{i+1}.\t <code>{trade['pair']}\t{trade['profit']:.2f}% "
f"({trade['count']})</code>\n")
stat_line = (
f"{i+1}.\t <code>{trade['pair']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit']:.2f}%) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
@@ -750,17 +808,21 @@ class Telegram(RPCHandler):
Handler for /locks.
Returns the currently active locks
"""
locks = self._rpc._rpc_locks()
message = tabulate([[
lock['id'],
lock['pair'],
lock['lock_end_time'],
lock['reason']] for lock in locks['locks']],
headers=['ID', 'Pair', 'Until', 'Reason'],
tablefmt='simple')
message = f"<pre>{escape(message)}</pre>"
logger.debug(message)
self._send_msg(message, parse_mode=ParseMode.HTML)
rpc_locks = self._rpc._rpc_locks()
if not rpc_locks['locks']:
self._send_msg('No active locks.', parse_mode=ParseMode.HTML)
for locks in chunks(rpc_locks['locks'], 25):
message = tabulate([[
lock['id'],
lock['pair'],
lock['lock_end_time'],
lock['reason']] for lock in locks],
headers=['ID', 'Pair', 'Until', 'Reason'],
tablefmt='simple')
message = f"<pre>{escape(message)}</pre>"
logger.debug(message)
self._send_msg(message, parse_mode=ParseMode.HTML)
@authorized_only
def _delete_locks(self, update: Update, context: CallbackContext) -> None:
@@ -860,9 +922,17 @@ class Telegram(RPCHandler):
"""
try:
edge_pairs = self._rpc._rpc_edge()
edge_pairs_tab = tabulate(edge_pairs, headers='keys', tablefmt='simple')
message = f'<b>Edge only validated following pairs:</b>\n<pre>{edge_pairs_tab}</pre>'
self._send_msg(message, parse_mode=ParseMode.HTML)
if not edge_pairs:
message = '<b>Edge only validated following pairs:</b>'
self._send_msg(message, parse_mode=ParseMode.HTML)
for chunk in chunks(edge_pairs, 25):
edge_pairs_tab = tabulate(chunk, headers='keys', tablefmt='simple')
message = (f'<b>Edge only validated following pairs:</b>\n'
f'<pre>{edge_pairs_tab}</pre>')
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@@ -959,8 +1029,9 @@ class Telegram(RPCHandler):
f"*Current state:* `{val['state']}`"
)
def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN,
disable_notification: bool = False) -> None:
def _send_inline_msg(self, msg: str, callback_query_handler,
parse_mode: str = ParseMode.MARKDOWN, disable_notification: bool = False,
keyboard: List[List[InlineKeyboardButton]] = None, ) -> None:
"""
Send given markdown message
:param msg: message
@@ -968,7 +1039,29 @@ class Telegram(RPCHandler):
:param parse_mode: telegram parse mode
:return: None
"""
reply_markup = ReplyKeyboardMarkup(self._keyboard, resize_keyboard=True)
if self._current_callback_query_handler:
self._updater.dispatcher.remove_handler(self._current_callback_query_handler)
self._current_callback_query_handler = self._callback_query_handlers[callback_query_handler]
self._updater.dispatcher.add_handler(self._current_callback_query_handler)
self._send_msg(msg, parse_mode, disable_notification,
cast(List[List[Union[str, KeyboardButton, InlineKeyboardButton]]], keyboard),
reply_markup=InlineKeyboardMarkup)
def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN,
disable_notification: bool = False,
keyboard: List[List[Union[str, KeyboardButton, InlineKeyboardButton]]] = None,
reply_markup=ReplyKeyboardMarkup) -> None:
"""
Send given markdown message
:param msg: message
:param bot: alternative bot
:param parse_mode: telegram parse mode
:return: None
"""
if keyboard is None:
keyboard = self._keyboard
reply_markup = reply_markup(keyboard, resize_keyboard=True)
try:
try:
self._updater.bot.send_message(

52
freqtrade/strategy/hyper.py
View File

@@ -5,7 +5,9 @@ This module defines a base class for auto-hyperoptable strategies.
import logging
from abc import ABC, abstractmethod
from contextlib import suppress
from typing import Any, Dict, Iterator, Optional, Sequence, Tuple, Union
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
from freqtrade.optimize.hyperopt_tools import HyperoptTools
with suppress(ImportError):
@@ -26,7 +28,8 @@ class BaseParameter(ABC):
category: Optional[str]
default: Any
value: Any
hyperopt: bool = False
in_space: bool = False
name: str
def __init__(self, *, default: Any, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs):
@@ -131,7 +134,7 @@ class IntParameter(NumericParameter):
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.hyperopt:
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:
@@ -247,6 +250,10 @@ class HyperStrategyMixin(object):
"""
Initialize hyperoptable strategy mixin.
"""
self.config = config
self.ft_buy_params: List[BaseParameter] = []
self.ft_sell_params: List[BaseParameter] = []
self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
@@ -257,15 +264,26 @@ class HyperStrategyMixin(object):
"""
if category not in ('buy', 'sell', None):
raise OperationalException('Category must be one of: "buy", "sell", None.')
if category is None:
params = self.ft_buy_params + self.ft_sell_params
else:
params = getattr(self, f"ft_{category}_params")
for par in params:
yield par.name, par
def _detect_parameters(self, category: str) -> Iterator[Tuple[str, BaseParameter]]:
""" Detect all parameters for 'category' """
for attr_name in dir(self):
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
attr = getattr(self, attr_name)
if issubclass(attr.__class__, BaseParameter):
if (category and attr_name.startswith(category + '_')
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 is None or category == attr.category or
if (category == attr.category or
(attr_name.startswith(category + '_') and attr.category is None)):
yield attr_name, attr
@@ -283,9 +301,16 @@ class HyperStrategyMixin(object):
"""
if not params:
logger.info(f"No params for {space} found, using default values.")
param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
for attr_name, attr in self._detect_parameters(space):
attr.name = attr_name
attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
if not attr.category:
attr.category = space
param_container.append(attr)
for attr_name, attr in self.enumerate_parameters():
attr.hyperopt = hyperopt
if params and attr_name in params:
if attr.load:
attr.value = params[attr_name]
@@ -295,3 +320,16 @@ class HyperStrategyMixin(object):
f'Default value "{attr.value}" used.')
else:
logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}')
def get_params_dict(self):
"""
Returns list of Parameters that are not part of the current optimize job
"""
params = {
'buy': {},
'sell': {}
}
for name, p in self.enumerate_parameters():
if not p.optimize or not p.in_space:
params[p.category][name] = p.value
return params

36
freqtrade/strategy/interface.py
View File

@@ -161,6 +161,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
return dataframe
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -170,6 +171,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
return dataframe
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -179,6 +181,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
return dataframe
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
"""
@@ -226,7 +229,7 @@ class IStrategy(ABC, HyperStrategyMixin):
pass
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool:
time_in_force: str, current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -241,6 +244,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
@@ -248,7 +252,8 @@ class IStrategy(ABC, HyperStrategyMixin):
return True
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -267,6 +272,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
@@ -274,7 +280,7 @@ class IStrategy(ABC, HyperStrategyMixin):
return True
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, dataframe: DataFrame, **kwargs) -> float:
current_profit: float, **kwargs) -> float:
"""
Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
e.g. returning -0.05 would create a stoploss 5% below current_rate.
@@ -290,15 +296,13 @@ class IStrategy(ABC, HyperStrategyMixin):
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param dataframe: Analyzed dataframe for this pair. Can contain future data in backtesting.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New stoploss value, relative to the currentrate
"""
return self.stoploss
def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, dataframe: DataFrame,
**kwargs) -> Optional[Union[str, bool]]:
current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
"""
Custom sell signal logic indicating that specified position should be sold. Returning a
string or True from this method is equal to setting sell signal on a candle at specified
@@ -536,8 +540,8 @@ class IStrategy(ABC, HyperStrategyMixin):
else:
return False
def should_sell(self, dataframe: DataFrame, trade: Trade, rate: float, date: datetime,
buy: bool, sell: bool, low: float = None, high: float = None,
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
sell: bool, low: float = None, high: float = None,
force_stoploss: float = 0) -> SellCheckTuple:
"""
This function evaluates if one of the conditions required to trigger a sell
@@ -553,9 +557,8 @@ class IStrategy(ABC, HyperStrategyMixin):
trade.adjust_min_max_rates(high or current_rate)
stoplossflag = self.stop_loss_reached(dataframe=dataframe, current_rate=current_rate,
trade=trade, current_time=date,
current_profit=current_profit,
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
current_time=date, current_profit=current_profit,
force_stoploss=force_stoploss, high=high)
# Set current rate to high for backtesting sell
@@ -570,6 +573,10 @@ class IStrategy(ABC, HyperStrategyMixin):
sell_signal = SellType.NONE
custom_reason = ''
# use provided rate in backtesting, not high/low.
current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate)
if (ask_strategy.get('sell_profit_only', False)
and current_profit <= ask_strategy.get('sell_profit_offset', 0)):
# sell_profit_only and profit doesn't reach the offset - ignore sell signal
@@ -580,7 +587,7 @@ class IStrategy(ABC, HyperStrategyMixin):
else:
custom_reason = strategy_safe_wrapper(self.custom_sell, default_retval=False)(
pair=trade.pair, trade=trade, current_time=date, current_rate=current_rate,
current_profit=current_profit, dataframe=dataframe)
current_profit=current_profit)
if custom_reason:
sell_signal = SellType.CUSTOM_SELL
if isinstance(custom_reason, str):
@@ -617,7 +624,7 @@ class IStrategy(ABC, HyperStrategyMixin):
# logger.debug(f"{trade.pair} - No sell signal.")
return SellCheckTuple(sell_type=SellType.NONE)
def stop_loss_reached(self, dataframe: DataFrame, current_rate: float, trade: Trade,
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
force_stoploss: float, high: float = None) -> SellCheckTuple:
"""
@@ -635,8 +642,7 @@ class IStrategy(ABC, HyperStrategyMixin):
)(pair=trade.pair, trade=trade,
current_time=current_time,
current_rate=current_rate,
current_profit=current_profit,
dataframe=dataframe)
current_profit=current_profit)
# Sanity check - error cases will return None
if stop_loss_value:
# logger.info(f"{trade.pair} {stop_loss_value=} {current_profit=}")

3
freqtrade/templates/base_config.json.j2
View File

@@ -9,7 +9,8 @@
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30
"sell": 30,
"unit": "minutes"
},
"bid_strategy": {
"price_side": "bid",

7
freqtrade/templates/subtemplates/strategy_methods_advanced.j2
View File

@@ -39,7 +39,7 @@ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime',
return self.stoploss
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool:
time_in_force: str, current_time: 'datetime', **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -54,6 +54,7 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
@@ -61,7 +62,8 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
return True
def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
rate: float, time_in_force: str, sell_reason: str,
current_time: 'datetime', **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -80,6 +82,7 @@ def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount:
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process