Merge branch 'freqtrade:develop' into develop
This commit is contained in:
commit
8095a260b7
@ -197,7 +197,7 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
|
||||
def calculate_expectancy(trades: pd.DataFrame) -> float:
|
||||
"""
|
||||
Calculate expectancy
|
||||
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
|
||||
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
|
||||
:return: expectancy
|
||||
"""
|
||||
if len(trades) == 0:
|
||||
|
@ -474,7 +474,7 @@ class Exchange:
|
||||
try:
|
||||
if self._api_async:
|
||||
self.loop.run_until_complete(
|
||||
self._api_async.load_markets(reload=reload))
|
||||
self._api_async.load_markets(reload=reload, params={}))
|
||||
|
||||
except (asyncio.TimeoutError, ccxt.BaseError) as e:
|
||||
logger.warning('Could not load async markets. Reason: %s', e)
|
||||
@ -483,7 +483,7 @@ class Exchange:
|
||||
def _load_markets(self) -> None:
|
||||
""" Initialize markets both sync and async """
|
||||
try:
|
||||
self._markets = self._api.load_markets()
|
||||
self._markets = self._api.load_markets(params={})
|
||||
self._load_async_markets()
|
||||
self._last_markets_refresh = arrow.utcnow().int_timestamp
|
||||
if self._ft_has['needs_trading_fees']:
|
||||
@ -501,7 +501,7 @@ class Exchange:
|
||||
return None
|
||||
logger.debug("Performing scheduled market reload..")
|
||||
try:
|
||||
self._markets = self._api.load_markets(reload=True)
|
||||
self._markets = self._api.load_markets(reload=True, params={})
|
||||
# Also reload async markets to avoid issues with newly listed pairs
|
||||
self._load_async_markets(reload=True)
|
||||
self._last_markets_refresh = arrow.utcnow().int_timestamp
|
||||
@ -1705,7 +1705,7 @@ class Exchange:
|
||||
return self._config['fee']
|
||||
# validate that markets are loaded before trying to get fee
|
||||
if self._api.markets is None or len(self._api.markets) == 0:
|
||||
self._api.load_markets()
|
||||
self._api.load_markets(params={})
|
||||
|
||||
return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
|
||||
price=price, takerOrMaker=taker_or_maker)['rate']
|
||||
|
@ -374,7 +374,7 @@ class FreqtradeBot(LoggingMixin):
|
||||
for trade in trades:
|
||||
if not trade.is_open and not trade.fee_updated(trade.exit_side):
|
||||
# Get sell fee
|
||||
order = trade.select_order(trade.exit_side, False)
|
||||
order = trade.select_order(trade.exit_side, False, only_filled=True)
|
||||
if not order:
|
||||
order = trade.select_order('stoploss', False)
|
||||
if order:
|
||||
@ -390,7 +390,7 @@ class FreqtradeBot(LoggingMixin):
|
||||
for trade in trades:
|
||||
with self._exit_lock:
|
||||
if trade.is_open and not trade.fee_updated(trade.entry_side):
|
||||
order = trade.select_order(trade.entry_side, False)
|
||||
order = trade.select_order(trade.entry_side, False, only_filled=True)
|
||||
open_order = trade.select_order(trade.entry_side, True)
|
||||
if order and open_order is None:
|
||||
logger.info(
|
||||
|
@ -5,13 +5,11 @@ This module defines the alternative HyperOptLoss class which can be used for
|
||||
Hyperoptimization.
|
||||
"""
|
||||
from datetime import datetime
|
||||
from math import sqrt as msqrt
|
||||
from typing import Any, Dict
|
||||
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.data.metrics import calculate_max_drawdown
|
||||
from freqtrade.data.metrics import calculate_calmar
|
||||
from freqtrade.optimize.hyperopt import IHyperOptLoss
|
||||
|
||||
|
||||
@ -23,42 +21,15 @@ class CalmarHyperOptLoss(IHyperOptLoss):
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def hyperopt_loss_function(
|
||||
results: DataFrame,
|
||||
trade_count: int,
|
||||
min_date: datetime,
|
||||
max_date: datetime,
|
||||
config: Config,
|
||||
processed: Dict[str, DataFrame],
|
||||
backtest_stats: Dict[str, Any],
|
||||
*args,
|
||||
**kwargs
|
||||
) -> float:
|
||||
def hyperopt_loss_function(results: DataFrame, trade_count: int,
|
||||
min_date: datetime, max_date: datetime,
|
||||
config: Config, *args, **kwargs) -> float:
|
||||
"""
|
||||
Objective function, returns smaller number for more optimal results.
|
||||
|
||||
Uses Calmar Ratio calculation.
|
||||
"""
|
||||
total_profit = backtest_stats["profit_total"]
|
||||
days_period = (max_date - min_date).days
|
||||
|
||||
# adding slippage of 0.1% per trade
|
||||
total_profit = total_profit - 0.0005
|
||||
expected_returns_mean = total_profit.sum() / days_period * 100
|
||||
|
||||
# calculate max drawdown
|
||||
try:
|
||||
_, _, _, _, _, max_drawdown = calculate_max_drawdown(
|
||||
results, value_col="profit_abs"
|
||||
)
|
||||
except ValueError:
|
||||
max_drawdown = 0
|
||||
|
||||
if max_drawdown != 0:
|
||||
calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
|
||||
else:
|
||||
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
|
||||
calmar_ratio = -20.0
|
||||
|
||||
starting_balance = config['dry_run_wallet']
|
||||
calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance)
|
||||
# print(expected_returns_mean, max_drawdown, calmar_ratio)
|
||||
return -calmar_ratio
|
||||
|
@ -6,9 +6,10 @@ Hyperoptimization.
|
||||
"""
|
||||
from datetime import datetime
|
||||
|
||||
import numpy as np
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.data.metrics import calculate_sharpe
|
||||
from freqtrade.optimize.hyperopt import IHyperOptLoss
|
||||
|
||||
|
||||
@ -22,25 +23,13 @@ class SharpeHyperOptLoss(IHyperOptLoss):
|
||||
@staticmethod
|
||||
def hyperopt_loss_function(results: DataFrame, trade_count: int,
|
||||
min_date: datetime, max_date: datetime,
|
||||
*args, **kwargs) -> float:
|
||||
config: Config, *args, **kwargs) -> float:
|
||||
"""
|
||||
Objective function, returns smaller number for more optimal results.
|
||||
|
||||
Uses Sharpe Ratio calculation.
|
||||
"""
|
||||
total_profit = results["profit_ratio"]
|
||||
days_period = (max_date - min_date).days
|
||||
|
||||
# adding slippage of 0.1% per trade
|
||||
total_profit = total_profit - 0.0005
|
||||
expected_returns_mean = total_profit.sum() / days_period
|
||||
up_stdev = np.std(total_profit)
|
||||
|
||||
if up_stdev != 0:
|
||||
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
|
||||
else:
|
||||
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
|
||||
sharp_ratio = -20.
|
||||
|
||||
starting_balance = config['dry_run_wallet']
|
||||
sharp_ratio = calculate_sharpe(results, min_date, max_date, starting_balance)
|
||||
# print(expected_returns_mean, up_stdev, sharp_ratio)
|
||||
return -sharp_ratio
|
||||
|
@ -6,9 +6,10 @@ Hyperoptimization.
|
||||
"""
|
||||
from datetime import datetime
|
||||
|
||||
import numpy as np
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.data.metrics import calculate_sortino
|
||||
from freqtrade.optimize.hyperopt import IHyperOptLoss
|
||||
|
||||
|
||||
@ -22,28 +23,13 @@ class SortinoHyperOptLoss(IHyperOptLoss):
|
||||
@staticmethod
|
||||
def hyperopt_loss_function(results: DataFrame, trade_count: int,
|
||||
min_date: datetime, max_date: datetime,
|
||||
*args, **kwargs) -> float:
|
||||
config: Config, *args, **kwargs) -> float:
|
||||
"""
|
||||
Objective function, returns smaller number for more optimal results.
|
||||
|
||||
Uses Sortino Ratio calculation.
|
||||
"""
|
||||
total_profit = results["profit_ratio"]
|
||||
days_period = (max_date - min_date).days
|
||||
|
||||
# adding slippage of 0.1% per trade
|
||||
total_profit = total_profit - 0.0005
|
||||
expected_returns_mean = total_profit.sum() / days_period
|
||||
|
||||
results['downside_returns'] = 0
|
||||
results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio']
|
||||
down_stdev = np.std(results['downside_returns'])
|
||||
|
||||
if down_stdev != 0:
|
||||
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
|
||||
else:
|
||||
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
|
||||
sortino_ratio = -20.
|
||||
|
||||
starting_balance = config['dry_run_wallet']
|
||||
sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
|
||||
# print(expected_returns_mean, down_stdev, sortino_ratio)
|
||||
return -sortino_ratio
|
||||
|
@ -956,11 +956,12 @@ class LocalTrade():
|
||||
return None
|
||||
|
||||
def select_order(self, order_side: Optional[str] = None,
|
||||
is_open: Optional[bool] = None) -> Optional[Order]:
|
||||
is_open: Optional[bool] = None, only_filled: bool = False) -> Optional[Order]:
|
||||
"""
|
||||
Finds latest order for this orderside and status
|
||||
:param order_side: ft_order_side of the order (either 'buy', 'sell' or 'stoploss')
|
||||
:param is_open: Only search for open orders?
|
||||
:param only_filled: Only search for Filled orders (only valid with is_open=False).
|
||||
:return: latest Order object if it exists, else None
|
||||
"""
|
||||
orders = self.orders
|
||||
@ -968,6 +969,8 @@ class LocalTrade():
|
||||
orders = [o for o in orders if o.ft_order_side == order_side]
|
||||
if is_open is not None:
|
||||
orders = [o for o in orders if o.ft_is_open == is_open]
|
||||
if is_open is False and only_filled:
|
||||
orders = [o for o in orders if o.filled and o.status in NON_OPEN_EXCHANGE_STATES]
|
||||
if len(orders) > 0:
|
||||
return orders[-1]
|
||||
else:
|
||||
|
@ -2,7 +2,7 @@ numpy==1.24.1
|
||||
pandas==1.5.2
|
||||
pandas-ta==0.3.14b
|
||||
|
||||
ccxt==2.4.60
|
||||
ccxt==2.5.46
|
||||
# Pin cryptography for now due to rust build errors with piwheels
|
||||
cryptography==38.0.1; platform_machine == 'armv7l'
|
||||
cryptography==38.0.4; platform_machine != 'armv7l'
|
||||
|
@ -48,8 +48,8 @@ def hyperopt_results():
|
||||
return pd.DataFrame(
|
||||
{
|
||||
'pair': ['ETH/USDT', 'ETH/USDT', 'ETH/USDT', 'ETH/USDT'],
|
||||
'profit_ratio': [-0.1, 0.2, -0.1, 0.3],
|
||||
'profit_abs': [-0.2, 0.4, -0.2, 0.6],
|
||||
'profit_ratio': [-0.1, 0.2, -0.12, 0.3],
|
||||
'profit_abs': [-0.2, 0.4, -0.21, 0.6],
|
||||
'trade_duration': [10, 30, 10, 10],
|
||||
'amount': [0.1, 0.1, 0.1, 0.1],
|
||||
'exit_reason': [ExitType.STOP_LOSS, ExitType.ROI, ExitType.STOP_LOSS, ExitType.ROI],
|
||||
|
Loading…
Reference in New Issue
Block a user