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Merge branch 'develop' into feat/sort_volatility

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Matthias
2024-02-27 20:50:41 +01:00
parent e80ad309f1 d1028b8ca2
commit 2384ba74a3
21 changed files with 70 additions and 55 deletions
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2
Dockerfile
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@@ -1,4 +1,4 @@
FROM python:3.11.7-slim-bookworm as base
FROM python:3.11.8-slim-bookworm as base
# Setup env
ENV LANG C.UTF-8

2
docker/Dockerfile.armhf
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@@ -1,4 +1,4 @@
FROM python:3.11.7-slim-bookworm as base
FROM python:3.11.8-slim-bookworm as base
# Setup env
ENV LANG C.UTF-8

2
docs/requirements-docs.txt
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@@ -1,6 +1,6 @@
markdown==3.5.2
mkdocs==1.5.3
mkdocs-material==9.5.9
mkdocs-material==9.5.11
mdx_truly_sane_lists==1.3
pymdown-extensions==10.7
jinja2==3.1.3

7
docs/strategy-callbacks.md
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@@ -791,7 +791,7 @@ Returning a value more than the above (so remaining stake_amount would become ne
If you wish to buy additional orders with DCA, then make sure to leave enough funds in the wallet for that.
Using 'unlimited' stake amount with DCA orders requires you to also implement the `custom_stake_amount()` callback to avoid allocating all funds to the initial order.
!!! Warning
!!! Warning "Stoploss calculation"
Stoploss is still calculated from the initial opening price, not averaged price.
Regular stoploss rules still apply (cannot move down).
@@ -801,6 +801,11 @@ Returning a value more than the above (so remaining stake_amount would become ne
During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
This can also cause deviating results between live and backtesting, since backtesting can adjust the trade only once per candle, whereas live could adjust the trade multiple times per candle.
!!! Warning "Performance with many position adjustments"
Position adjustments can be a good approach to increase a strategy's output - but it can also have drawbacks if using this feature extensively.
Each of the orders will be attached to the trade object for the duration of the trade - hence increasing memory usage.
Trades with long duration and 10s or even 100ds of position adjustments are therefore not recommended, and should be closed at regular intervals to not affect performance.
``` python
from freqtrade.persistence import Trade

2
freqtrade/__init__.py
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@@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2024.2-dev'
__version__ = '2024.3-dev'
if 'dev' in __version__:
from pathlib import Path

4
freqtrade/data/history/hdf5datahandler.py
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@@ -35,7 +35,7 @@ class HDF5DataHandler(IDataHandler):
self.create_dir_if_needed(filename)
_data.loc[:, self._columns].to_hdf(
filename, key, mode='a', complevel=9, complib='blosc',
filename, key=key, mode='a', complevel=9, complib='blosc',
format='table', data_columns=['date']
)
@@ -110,7 +110,7 @@ class HDF5DataHandler(IDataHandler):
key = self._pair_trades_key(pair)
data.to_hdf(
self._pair_trades_filename(self._datadir, pair), key,
self._pair_trades_filename(self._datadir, pair), key=key,
mode='a', complevel=9, complib='blosc',
format='table', data_columns=['timestamp']
)

2
freqtrade/data/history/jsondatahandler.py
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@@ -37,7 +37,7 @@ class JsonDataHandler(IDataHandler):
self.create_dir_if_needed(filename)
_data = data.copy()
# Convert date to int
_data['date'] = _data['date'].view(np.int64) // 1000 // 1000
_data['date'] = _data['date'].astype(np.int64) // 1000 // 1000
# Reset index, select only appropriate columns and save as json
_data.reset_index(drop=True).loc[:, self._columns].to_json(

5
freqtrade/freqtradebot.py
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@@ -128,8 +128,9 @@ class FreqtradeBot(LoggingMixin):
self.update_funding_fees()
self.wallets.update()
# TODO: This would be more efficient if scheduled in utc time, and performed at each
# TODO: funding interval, specified by funding_fee_times on the exchange classes
# This would be more efficient if scheduled in utc time, and performed at each
# funding interval, specified by funding_fee_times on the exchange classes
# However, this reduces the precision - and might therefore lead to problems.
for time_slot in range(0, 24):
for minutes in [1, 31]:
t = str(time(time_slot, minutes, 2))

6
freqtrade/optimize/analysis/lookahead_helpers.py
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@@ -107,9 +107,9 @@ class LookaheadAnalysisSubFunctions:
csv_df = add_or_update_row(csv_df, new_row_data)
# Fill NaN values with a default value (e.g., 0)
csv_df['total_signals'] = csv_df['total_signals'].fillna(0)
csv_df['biased_entry_signals'] = csv_df['biased_entry_signals'].fillna(0)
csv_df['biased_exit_signals'] = csv_df['biased_exit_signals'].fillna(0)
csv_df['total_signals'] = csv_df['total_signals'].astype(int).fillna(0)
csv_df['biased_entry_signals'] = csv_df['biased_entry_signals'].astype(int).fillna(0)
csv_df['biased_exit_signals'] = csv_df['biased_exit_signals'].astype(int).fillna(0)
# Convert columns to integers
csv_df['total_signals'] = csv_df['total_signals'].astype(int)

2
freqtrade/optimize/backtesting.py
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@@ -201,7 +201,7 @@ class Backtesting:
self.prepare_backtest(False)
self.wallets = Wallets(self.config, self.exchange, log=False)
self.wallets = Wallets(self.config, self.exchange, is_backtest=True)
self.progress = BTProgress()
self.abort = False

2
freqtrade/optimize/optimize_reports/optimize_reports.py
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@@ -215,7 +215,7 @@ def _get_resample_from_period(period: str) -> str:
# Weekly defaulting to Monday.
return '1W-MON'
if period == 'month':
return '1M'
return '1ME'
raise ValueError(f"Period {period} is not supported.")

2
freqtrade/rpc/rpc.py
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@@ -1155,7 +1155,7 @@ class RPC:
}
if has_content:
dataframe.loc[:, '__date_ts'] = dataframe.loc[:, 'date'].view(int64) // 1000 // 1000
dataframe.loc[:, '__date_ts'] = dataframe.loc[:, 'date'].astype(int64) // 1000 // 1000
# Move signal close to separate column when signal for easy plotting
for sig_type in signals.keys():
if sig_type in dataframe.columns:

20
freqtrade/wallets.py
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@@ -36,9 +36,9 @@ class PositionWallet(NamedTuple):
class Wallets:
def __init__(self, config: Config, exchange: Exchange, log: bool = True) -> None:
def __init__(self, config: Config, exchange: Exchange, is_backtest: bool = False) -> None:
self._config = config
self._log = log
self._is_backtest = is_backtest
self._exchange = exchange
self._wallets: Dict[str, Wallet] = {}
self._positions: Dict[str, PositionWallet] = {}
@@ -78,11 +78,11 @@ class Wallets:
_wallets = {}
_positions = {}
open_trades = Trade.get_trades_proxy(is_open=True)
# If not backtesting...
# TODO: potentially remove the ._log workaround to determine backtest mode.
if self._log:
if not self._is_backtest:
# Live / Dry-run mode
tot_profit = Trade.get_total_closed_profit()
else:
# Backtest mode
tot_profit = LocalTrade.total_profit
tot_profit += sum(trade.realized_profit for trade in open_trades)
tot_in_trades = sum(trade.stake_amount for trade in open_trades)
@@ -177,7 +177,7 @@ class Wallets:
self._update_live()
else:
self._update_dry()
if self._log:
if not self._is_backtest:
logger.info('Wallets synced.')
self._last_wallet_refresh = dt_now()
@@ -341,19 +341,19 @@ class Wallets:
max_allowed_stake = min(max_allowed_stake, max_stake_amount - trade_amount)
if min_stake_amount is not None and min_stake_amount > max_allowed_stake:
if self._log:
if not self._is_backtest:
logger.warning("Minimum stake amount > available balance. "
f"{min_stake_amount} > {max_allowed_stake}")
return 0
if min_stake_amount is not None and stake_amount < min_stake_amount:
if self._log:
if not self._is_backtest:
logger.info(
f"Stake amount for pair {pair} is too small "
f"({stake_amount} < {min_stake_amount}), adjusting to {min_stake_amount}."
)
if stake_amount * 1.3 < min_stake_amount:
# Top-cap stake-amount adjustments to +30%.
if self._log:
if not self._is_backtest:
logger.info(
f"Adjusted stake amount for pair {pair} is more than 30% bigger than "
f"the desired stake amount of ({stake_amount:.8f} * 1.3 = "
@@ -363,7 +363,7 @@ class Wallets:
stake_amount = min_stake_amount
if stake_amount > max_allowed_stake:
if self._log:
if not self._is_backtest:
logger.info(
f"Stake amount for pair {pair} is too big "
f"({stake_amount} > {max_allowed_stake}), adjusting to {max_allowed_stake}."

4
requirements-dev.txt
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@@ -10,7 +10,7 @@ coveralls==3.3.1
ruff==0.2.2
mypy==1.8.0
pre-commit==3.6.2
pytest==8.0.1
pytest==8.0.2
pytest-asyncio==0.23.5
pytest-cov==4.1.0
pytest-mock==3.12.0
@@ -21,7 +21,7 @@ isort==5.13.2
time-machine==2.13.0
# Convert jupyter notebooks to markdown documents
nbconvert==7.16.0
nbconvert==7.16.1
# mypy types
types-cachetools==5.3.0.7

13
requirements.txt
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@@ -1,9 +1,9 @@
numpy==1.26.4
pandas==2.1.4
pandas==2.2.1
pandas-ta==0.3.14b
ccxt==4.2.47
cryptography==42.0.4
ccxt==4.2.51
cryptography==42.0.5
aiohttp==3.9.3
SQLAlchemy==2.0.27
python-telegram-bot==20.8
@@ -30,14 +30,14 @@ py_find_1st==1.1.6
# Load ticker files 30% faster
python-rapidjson==1.14
# Properly format api responses
orjson==3.9.14
orjson==3.9.15
# Notify systemd
sdnotify==0.3.2
# API Server
fastapi==0.109.2
pydantic==2.6.1
fastapi==0.110.0
pydantic==2.6.2
uvicorn==0.27.1
pyjwt==2.8.0
aiofiles==23.2.1
@@ -50,6 +50,7 @@ questionary==2.0.1
prompt-toolkit==3.0.36
# Extensions to datetime library
python-dateutil==2.8.2
pytz==2024.1
#Futures
schedule==1.2.1

1
setup.py
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@@ -110,6 +110,7 @@ setup(
'cryptography',
'sdnotify',
'python-dateutil',
'pytz',
'packaging',
],
extras_require={

2
tests/conftest.py
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@@ -177,7 +177,7 @@ def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05', ran
def generate_test_data_raw(timeframe: str, size: int, start: str = '2020-07-05', random_seed=42):
""" Generates data in the ohlcv format used by ccxt """
df = generate_test_data(timeframe, size, start, random_seed)
df['date'] = df.loc[:, 'date'].view(np.int64) // 1000 // 1000
df['date'] = df.loc[:, 'date'].astype(np.int64) // 1000 // 1000
return list(list(x) for x in zip(*(df[x].values.tolist() for x in df.columns)))

11
tests/freqtradebot/test_freqtradebot.py
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@@ -4680,9 +4680,14 @@ def test_get_valid_price(mocker, default_conf_usdt) -> None:
('futures', 17, "2021-08-31 23:59:59", "2021-09-01 08:01:07"),
('futures', 17, "2021-08-31 23:59:58", "2021-09-01 08:01:07"),
])
@pytest.mark.parametrize('tzoffset', [
'+00:00',
'+01:00',
'-02:00',
])
def test_update_funding_fees_schedule(mocker, default_conf, trading_mode, calls, time_machine,
t1, t2):
time_machine.move_to(f"{t1} +00:00", tick=False)
t1, t2, tzoffset):
time_machine.move_to(f"{t1} {tzoffset}", tick=False)
patch_RPCManager(mocker)
patch_exchange(mocker)
@@ -4691,7 +4696,7 @@ def test_update_funding_fees_schedule(mocker, default_conf, trading_mode, calls,
default_conf['margin_mode'] = 'isolated'
freqtrade = get_patched_freqtradebot(mocker, default_conf)
time_machine.move_to(f"{t2} +00:00", tick=False)
time_machine.move_to(f"{t2} {tzoffset}", tick=False)
# Check schedule jobs in debugging with freqtrade._schedule.jobs
freqtrade._schedule.run_pending()

16
tests/optimize/test_backtesting_adjust_position.py
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@@ -57,28 +57,30 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
),
'close_date': pd.to_datetime([dt_utc(2018, 1, 29, 22, 00, 0),
dt_utc(2018, 1, 30, 4, 10, 0)], utc=True),
'open_rate': [0.10401764894444211, 0.10302485],
'close_rate': [0.10453904066847439, 0.103541],
'open_rate': [0.10401764891917063, 0.10302485],
'close_rate': [0.10453904064307624, 0.10354126528822055],
'fee_open': [0.0025, 0.0025],
'fee_close': [0.0025, 0.0025],
'trade_duration': [200, 40],
'profit_ratio': [0.0, 0.0],
'profit_abs': [0.0, 0.0],
'exit_reason': [ExitType.ROI.value, ExitType.ROI.value],
'initial_stop_loss_abs': [0.0940005, 0.09272236],
'initial_stop_loss_abs': [0.0940005, 0.092722365],
'initial_stop_loss_ratio': [-0.1, -0.1],
'stop_loss_abs': [0.0940005, 0.09272236],
'stop_loss_abs': [0.0940005, 0.092722365],
'stop_loss_ratio': [-0.1, -0.1],
'min_rate': [0.10370188, 0.10300000000000001],
'max_rate': [0.10481985, 0.1038888],
'max_rate': [0.10481985, 0.10388887000000001],
'is_open': [False, False],
'enter_tag': ['', ''],
'leverage': [1.0, 1.0],
'is_short': [False, False],
'open_timestamp': [1517251200000, 1517283000000],
'close_timestamp': [1517265300000, 1517285400000],
'close_timestamp': [1517263200000, 1517285400000],
})
pd.testing.assert_frame_equal(results.drop(columns=['orders']), expected)
results_no = results.drop(columns=['orders'])
pd.testing.assert_frame_equal(results_no, expected, check_exact=True)
data_pair = processed[pair]
assert len(results.iloc[0]['orders']) == 6
assert len(results.iloc[1]['orders']) == 2

2
tests/optimize/test_optimize_reports.py
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@@ -498,7 +498,7 @@ def test__get_resample_from_period():
assert _get_resample_from_period('day') == '1d'
assert _get_resample_from_period('week') == '1W-MON'
assert _get_resample_from_period('month') == '1M'
assert _get_resample_from_period('month') == '1ME'
with pytest.raises(ValueError, match=r"Period noooo is not supported."):
_get_resample_from_period('noooo')

18
tests/strategy/test_interface.py
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@@ -1022,22 +1022,22 @@ def test_auto_hyperopt_interface_loadparams(default_conf, mocker, caplog):
@pytest.mark.parametrize('function,raises', [
('populate_entry_trend', True),
('populate_entry_trend', False),
('advise_entry', False),
('populate_exit_trend', True),
('populate_exit_trend', False),
('advise_exit', False),
])
def test_pandas_warning_direct(ohlcv_history, function, raises):
def test_pandas_warning_direct(ohlcv_history, function, raises, recwarn):
df = _STRATEGY.populate_indicators(ohlcv_history, {'pair': 'ETH/BTC'})
if raises:
with pytest.warns(FutureWarning):
# Test for Future warning
# FutureWarning: Setting an item of incompatible dtype is
# deprecated and will raise in a future error of pandas
# https://github.com/pandas-dev/pandas/issues/56503
getattr(_STRATEGY, function)(df, {'pair': 'ETH/BTC'})
assert len(recwarn) == 1
# https://github.com/pandas-dev/pandas/issues/56503
# Fixed in 2.2.x
getattr(_STRATEGY, function)(df, {'pair': 'ETH/BTC'})
else:
assert len(recwarn) == 0
getattr(_STRATEGY, function)(df, {'pair': 'ETH/BTC'})