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Merge branch 'develop' into pr/xataxxx/6079

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Matthias 2022-01-02 22:21:41 +01:00
commit 711a6a6dbc
42 changed files with 300 additions and 134 deletions
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22
.github/workflows/ci.yml vendored
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@ -3,7 +3,6 @@ name: Freqtrade CI
on:
push:
branches:
- master
- stable
- develop
tags:
@ -20,7 +19,7 @@ jobs:
strategy:
matrix:
os: [ ubuntu-18.04, ubuntu-20.04 ]
python-version: [3.7, 3.8, 3.9]
python-version: ["3.7", "3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v2
@ -39,7 +38,7 @@ jobs:
- name: pip cache (linux)
uses: actions/cache@v2
if: startsWith(matrix.os, 'ubuntu')
if: runner.os == 'Linux'
with:
path: ~/.cache/pip
key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@ -50,8 +49,9 @@ jobs:
cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
- name: Installation - *nix
if: runner.os == 'Linux'
run: |
python -m pip install --upgrade pip
python -m pip install --upgrade pip wheel
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
export TA_LIBRARY_PATH=${HOME}/dependencies/lib
export TA_INCLUDE_PATH=${HOME}/dependencies/include
@ -69,7 +69,7 @@ jobs:
if: matrix.python-version == '3.9'
- name: Coveralls
if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
if: (runner.os == 'Linux' && matrix.python-version == '3.8')
env:
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@ -114,7 +114,7 @@ jobs:
strategy:
matrix:
os: [ macos-latest ]
python-version: [3.7, 3.8, 3.9]
python-version: ["3.7", "3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v2
@ -133,7 +133,7 @@ jobs:
- name: pip cache (macOS)
uses: actions/cache@v2
if: startsWith(matrix.os, 'macOS')
if: runner.os == 'macOS'
with:
path: ~/Library/Caches/pip
key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@ -144,10 +144,11 @@ jobs:
cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
- name: Installation - macOS
if: runner.os == 'macOS'
run: |
brew update
brew install hdf5 c-blosc
python -m pip install --upgrade pip
python -m pip install --upgrade pip wheel
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
export TA_LIBRARY_PATH=${HOME}/dependencies/lib
export TA_INCLUDE_PATH=${HOME}/dependencies/include
@ -159,7 +160,7 @@ jobs:
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
- name: Coveralls
if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
if: (runner.os == 'Linux' && matrix.python-version == '3.8')
env:
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@ -205,7 +206,7 @@ jobs:
strategy:
matrix:
os: [ windows-latest ]
python-version: [3.7, 3.8]
python-version: ["3.7", "3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v2
@ -217,7 +218,6 @@ jobs:
- name: Pip cache (Windows)
uses: actions/cache@preview
if: startsWith(runner.os, 'Windows')
with:
path: ~\AppData\Local\pip\Cache
key: ${{ matrix.os }}-${{ matrix.python-version }}-pip

2
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.9.9-slim-bullseye as base
FROM python:3.10.0-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

2
README.md
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@ -197,7 +197,7 @@ To run this bot we recommend you a cloud instance with a minimum of:
### Software requirements
- [Python 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
- [Python >= 3.7](http://docs.python-guide.org/en/latest/starting/installation/)
- [pip](https://pip.pypa.io/en/stable/installing/)
- [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
- [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)

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build_helpers/TA_Lib-0.4.22-cp310-cp310-win_amd64.whl Normal file
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6
build_helpers/install_windows.ps1
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@ -1,7 +1,7 @@
# Downloads don't work automatically, since the URL is regenerated via javascript.
# Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
python -m pip install --upgrade pip
python -m pip install --upgrade pip wheel
$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
@ -14,6 +14,8 @@ if ($pyv -eq '3.8') {
if ($pyv -eq '3.9') {
pip install build_helpers\TA_Lib-0.4.22-cp39-cp39-win_amd64.whl
}
if ($pyv -eq '3.10') {
pip install build_helpers\TA_Lib-0.4.22-cp310-cp310-win_amd64.whl
}
pip install -r requirements-dev.txt
pip install -e .

3
docs/advanced-setup.md
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@ -176,12 +176,15 @@ Log messages are send to `syslog` with the `user` facility. So you can see them
On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
```
if $programname startswith "freqtrade" then -/var/log/freqtrade.log
```
to one of the rsyslog configuration files, for example at the end of the `/etc/rsyslog.d/50-default.conf`.
For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduce the number of repeating messages. For instance, multiple bot Heartbeat messages will be reduced to a single message when nothing else happens with the bot. To achieve this, set in `/etc/rsyslog.conf`:
```
# Filter duplicated messages
$RepeatedMsgReduction on

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2
docs/backtesting.md
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@ -484,8 +484,8 @@ Since backtesting lacks some detailed information about what happens within a ca
- ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
- Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
- Evaluation sequence (if multiple signals happen on the same candle)
- ROI (if not stoploss)
- Sell-signal
- ROI (if not stoploss)
- Stoploss
Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.

17
docs/installation.md
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@ -56,10 +56,6 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
!!! Note
Python3.7 or higher and the corresponding pip are assumed to be available.
!!! Warning "Python 3.10 support"
Due to issues with dependencies, freqtrade is currently unable to support python 3.10.
We're working on supporting python 3.10, are however dependant on support from dependencies.
=== "Debian/Ubuntu"
#### Install necessary dependencies
@ -424,16 +420,3 @@ open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10
```
If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
### MacOS installation error with python 3.9
When using python 3.9 on macOS, it's currently necessary to install some os-level modules to allow dependencies to compile.
The errors you'll see happen during installation and are related to the installation of `tables` or `blosc`.
You can install the necessary libraries with the following command:
```bash
brew install hdf5 c-blosc
```
After this, please run the installation (script) again.

4
docs/plotting.md
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@ -286,6 +286,8 @@ The `plot-profit` subcommand shows an interactive graph with three plots:
* The summarized profit made by backtesting.
Note that this is not the real-world profit, but more of an estimate.
* Profit for each individual pair.
* Parallelism of trades.
* Underwater (Periods of drawdown).
The first graph is good to get a grip of how the overall market progresses.
@ -295,6 +297,8 @@ This graph will also highlight the start (and end) of the Max drawdown period.
The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
The forth graph can help you analyze trade parallelism, showing how often max_open_trades have been maxed out.
Possible options for the `freqtrade plot-profit` subcommand:
```

2
docs/windows_installation.md
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@ -25,7 +25,7 @@ Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib‑0.4.22‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version).
Freqtrade provides these dependencies for the latest 3 Python versions (3.7, 3.8 and 3.9) and for 64bit Windows.
Freqtrade provides these dependencies for the latest 3 Python versions (3.7, 3.8, 3.9 and 3.10) and for 64bit Windows.
Other versions must be downloaded from the above link.
``` powershell

2
freqtrade/configuration/PeriodicCache.py
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@ -1,6 +1,6 @@
from datetime import datetime, timezone
from cachetools.ttl import TTLCache
from cachetools import TTLCache
class PeriodicCache(TTLCache):

36
freqtrade/data/btanalysis.py
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@ -325,6 +325,7 @@ def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
:param column: Column in the original dataframes to use
:return: DataFrame with the column renamed to the dict key, and a column
named mean, containing the mean of all pairs.
:raise: ValueError if no data is provided.
"""
df_comb = pd.concat([data[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in data], axis=1)
@ -360,6 +361,36 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
return df
def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str
) -> pd.DataFrame:
max_drawdown_df = pd.DataFrame()
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
max_drawdown_df['date'] = profit_results.loc[:, date_col]
return max_drawdown_df
def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio'
):
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
high and low time and high and low value.
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col).reset_index(drop=True)
max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
return max_drawdown_df
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio'
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
@ -375,10 +406,7 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col).reset_index(drop=True)
max_drawdown_df = pd.DataFrame()
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
idxmin = max_drawdown_df['drawdown'].idxmin()
if idxmin == 0:

8
freqtrade/data/history/idatahandler.py
View File

@ -201,7 +201,7 @@ class IDataHandler(ABC):
enddate = pairdf.iloc[-1]['date']
if timerange_startup:
self._validate_pairdata(pair, pairdf, timerange_startup)
self._validate_pairdata(pair, pairdf, timeframe, timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
return pairdf
@ -228,7 +228,7 @@ class IDataHandler(ABC):
return True
return False
def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
def _validate_pairdata(self, pair, pairdata: DataFrame, timeframe: str, timerange: TimeRange):
"""
Validates pairdata for missing data at start end end and logs warnings.
:param pairdata: Dataframe to validate
@ -238,12 +238,12 @@ class IDataHandler(ABC):
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
if pairdata.iloc[0]['date'] > start:
logger.warning(f"Missing data at start for pair {pair}, "
logger.warning(f"Missing data at start for pair {pair} at {timeframe}, "
f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
if pairdata.iloc[-1]['date'] < stop:
logger.warning(f"Missing data at end for pair {pair}, "
logger.warning(f"Missing data at end for pair {pair} at {timeframe}, "
f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")

36
freqtrade/exchange/common.py
View File

@ -4,9 +4,20 @@ import time
from functools import wraps
from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
from freqtrade.mixins import LoggingMixin
logger = logging.getLogger(__name__)
__logging_mixin = None
def _get_logging_mixin():
# Logging-mixin to cache kucoin responses
# Only to be used in retrier
global __logging_mixin
if not __logging_mixin:
__logging_mixin = LoggingMixin(logger)
return __logging_mixin
# Maximum default retry count.
@ -72,28 +83,33 @@ def calculate_backoff(retrycount, max_retries):
def retrier_async(f):
async def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
kucoin = args[0].name == "Kucoin" # Check if the exchange is KuCoin.
try:
return await f(*args, **kwargs)
except TemporaryError as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
msg = f'{f.__name__}() returned exception: "{ex}". '
if count > 0:
logger.warning('retrying %s() still for %s times', f.__name__, count)
msg += f'Retrying still for {count} times.'
count -= 1
kwargs.update({'count': count})
kwargs['count'] = count
if isinstance(ex, DDosProtection):
if "kucoin" in str(ex) and "429000" in str(ex):
if kucoin and "429000" in str(ex):
# Temporary fix for 429000 error on kucoin
# see https://github.com/freqtrade/freqtrade/issues/5700 for details.
logger.warning(
_get_logging_mixin().log_once(
f"Kucoin 429 error, avoid triggering DDosProtection backoff delay. "
f"{count} tries left before giving up")
f"{count} tries left before giving up", logmethod=logger.warning)
# Reset msg to avoid logging too many times.
msg = ''
else:
backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
await asyncio.sleep(backoff_delay)
if msg:
logger.warning(msg)
return await wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
logger.warning(msg + 'Giving up.')
raise ex
return wrapper
@ -106,9 +122,9 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
try:
return f(*args, **kwargs)
except (TemporaryError, RetryableOrderError) as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
msg = f'{f.__name__}() returned exception: "{ex}". '
if count > 0:
logger.warning('retrying %s() still for %s times', f.__name__, count)
logger.warning(msg + f'Retrying still for {count} times.')
count -= 1
kwargs.update({'count': count})
if isinstance(ex, (DDosProtection, RetryableOrderError)):
@ -118,7 +134,7 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
time.sleep(backoff_delay)
return wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
logger.warning(msg + 'Giving up.')
raise ex
return wrapper
# Support both @retrier and @retrier(retries=2) syntax

20
freqtrade/exchange/exchange.py
View File

@ -83,6 +83,8 @@ class Exchange:
self._api: ccxt.Exchange = None
self._api_async: ccxt_async.Exchange = None
self._markets: Dict = {}
self.loop = asyncio.new_event_loop()
asyncio.set_event_loop(self.loop)
self._config.update(config)
@ -170,8 +172,10 @@ class Exchange:
def close(self):
logger.debug("Exchange object destroyed, closing async loop")
if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
asyncio.get_event_loop().run_until_complete(self._api_async.close())
if (self._api_async and inspect.iscoroutinefunction(self._api_async.close)
and self._api_async.session):
logger.info("Closing async ccxt session.")
self.loop.run_until_complete(self._api_async.close())
def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
ccxt_kwargs: Dict = {}) -> ccxt.Exchange:
@ -326,7 +330,7 @@ class Exchange:
def _load_async_markets(self, reload: bool = False) -> None:
try:
if self._api_async:
asyncio.get_event_loop().run_until_complete(
self.loop.run_until_complete(
self._api_async.load_markets(reload=reload))
except (asyncio.TimeoutError, ccxt.BaseError) as e:
@ -1227,7 +1231,7 @@ class Exchange:
:param since_ms: Timestamp in milliseconds to get history from
:return: List with candle (OHLCV) data
"""
pair, timeframe, data = asyncio.get_event_loop().run_until_complete(
pair, timeframe, data = self.loop.run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms, is_new_pair=is_new_pair))
logger.info(f"Downloaded data for {pair} with length {len(data)}.")
@ -1329,8 +1333,10 @@ class Exchange:
results_df = {}
# Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
for input_coro in chunks(input_coroutines, 100):
results = asyncio.get_event_loop().run_until_complete(
asyncio.gather(*input_coro, return_exceptions=True))
async def gather_stuff():
return await asyncio.gather(*input_coro, return_exceptions=True)
results = self.loop.run_until_complete(gather_stuff())
# handle caching
for res in results:
@ -1566,7 +1572,7 @@ class Exchange:
if not self.exchange_has("fetchTrades"):
raise OperationalException("This exchange does not support downloading Trades.")
return asyncio.get_event_loop().run_until_complete(
return self.loop.run_until_complete(
self._async_get_trade_history(pair=pair, since=since,
until=until, from_id=from_id))

1
freqtrade/freqtradebot.py
View File

@ -126,6 +126,7 @@ class FreqtradeBot(LoggingMixin):
self.rpc.cleanup()
cleanup_db()
self.exchange.close()
def startup(self) -> None:
"""

12
freqtrade/optimize/backtesting.py
View File

@ -246,6 +246,9 @@ class Backtesting:
Helper function to convert a processed dataframes into lists for performance reasons.
Used by backtest() - so keep this optimized for performance.
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
optimize memory usage!
"""
# Every change to this headers list must evaluate further usages of the resulting tuple
# and eventually change the constants for indexes at the top
@ -254,7 +257,8 @@ class Backtesting:
self.progress.init_step(BacktestState.CONVERT, len(processed))
# Create dict with data
for pair, pair_data in processed.items():
for pair in processed.keys():
pair_data = processed[pair]
self.check_abort()
self.progress.increment()
if not pair_data.empty:
@ -283,6 +287,9 @@ class Backtesting:
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = df_analyzed[headers].values.tolist()
# Do not hold on to old data to reduce memory usage
processed[pair] = pair_data = None
return data
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
@ -571,7 +578,8 @@ class Backtesting:
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
optimize memory usage!
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited

1
freqtrade/optimize/hyperopt.py
View File

@ -422,6 +422,7 @@ class Hyperopt:
self.backtesting.exchange.close()
self.backtesting.exchange._api = None # type: ignore
self.backtesting.exchange._api_async = None # type: ignore
self.backtesting.exchange.loop = None # type: ignore
# self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore

68
freqtrade/plot/plotting.py
View File

@ -5,7 +5,8 @@ from typing import Any, Dict, List
import pandas as pd
from freqtrade.configuration import TimeRange
from freqtrade.data.btanalysis import (calculate_max_drawdown, combine_dataframes_with_mean,
from freqtrade.data.btanalysis import (analyze_trade_parallelism, calculate_max_drawdown,
calculate_underwater, combine_dataframes_with_mean,
create_cum_profit, extract_trades_of_period, load_trades)
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
@ -185,6 +186,48 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
return fig
def add_underwater(fig, row, trades: pd.DataFrame) -> make_subplots:
"""
Add underwater plot
"""
try:
underwater = calculate_underwater(trades, value_col="profit_abs")
underwater = go.Scatter(
x=underwater['date'],
y=underwater['drawdown'],
name="Underwater Plot",
fill='tozeroy',
fillcolor='#cc362b',
line={'color': '#cc362b'},
)
fig.add_trace(underwater, row, 1)
except ValueError:
logger.warning("No trades found - not plotting underwater plot")
return fig
def add_parallelism(fig, row, trades: pd.DataFrame, timeframe: str) -> make_subplots:
"""
Add Chart showing trade parallelism
"""
try:
result = analyze_trade_parallelism(trades, timeframe)
drawdown = go.Scatter(
x=result.index,
y=result['open_trades'],
name="Parallel trades",
fill='tozeroy',
fillcolor='#242222',
line={'color': '#242222'},
)
fig.add_trace(drawdown, row, 1)
except ValueError:
logger.warning("No trades found - not plotting Parallelism.")
return fig
def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
"""
Add trades to "fig"
@ -460,7 +503,12 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
trades: pd.DataFrame, timeframe: str, stake_currency: str) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
df_comb = combine_dataframes_with_mean(data, "close")
try:
df_comb = combine_dataframes_with_mean(data, "close")
except ValueError:
raise OperationalException(
"No data found. Please make sure that data is available for "
"the timerange and pairs selected.")
# Trim trades to available OHLCV data
trades = extract_trades_of_period(df_comb, trades, date_index=True)
@ -477,20 +525,30 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
name='Avg close price',
)
fig = make_subplots(rows=3, cols=1, shared_xaxes=True,
row_width=[1, 1, 1],
fig = make_subplots(rows=5, cols=1, shared_xaxes=True,
row_heights=[1, 1, 1, 0.5, 1],
vertical_spacing=0.05,
subplot_titles=["AVG Close Price", "Combined Profit", "Profit per pair"])
subplot_titles=[
"AVG Close Price",
"Combined Profit",
"Profit per pair",
"Parallelism",
"Underwater",
])
fig['layout'].update(title="Freqtrade Profit plot")
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title=f'Profit {stake_currency}')
fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
fig['layout']['yaxis4'].update(title='Trade count')
fig['layout']['yaxis5'].update(title='Underwater Plot')
fig['layout']['xaxis']['rangeslider'].update(visible=False)
fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
fig.add_trace(avgclose, 1, 1)
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe)
fig = add_parallelism(fig, 4, trades, timeframe)
fig = add_underwater(fig, 5, trades)
for pair in pairs:
profit_col = f'cum_profit_{pair}'

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

@ -8,7 +8,7 @@ from typing import Any, Dict, List, Optional
import arrow
import numpy as np
from cachetools.ttl import TTLCache
from cachetools import TTLCache
from pandas import DataFrame
from freqtrade.exceptions import OperationalException

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

@ -8,7 +8,7 @@ from functools import partial
from typing import Any, Dict, List
import arrow
from cachetools.ttl import TTLCache
from cachetools import TTLCache
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes

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

@ -6,7 +6,7 @@ from copy import deepcopy
from typing import Any, Dict, List, Optional
import arrow
from cachetools.ttl import TTLCache
from cachetools import TTLCache
from pandas import DataFrame
from freqtrade.exceptions import OperationalException

2
freqtrade/rpc/api_server/uvicorn_threaded.py
View File

@ -47,7 +47,7 @@ class UvicornServer(uvicorn.Server):
else:
asyncio.set_event_loop(uvloop.new_event_loop())
try:
loop = asyncio.get_event_loop()
loop = asyncio.get_running_loop()
except RuntimeError:
# When running in a thread, we'll not have an eventloop yet.
loop = asyncio.new_event_loop()

2
freqtrade/rpc/fiat_convert.py
View File

@ -7,7 +7,7 @@ import datetime
import logging
from typing import Dict, List
from cachetools.ttl import TTLCache
from cachetools import TTLCache
from pycoingecko import CoinGeckoAPI
from requests.exceptions import RequestException

5
freqtrade/rpc/telegram.py
View File

@ -199,8 +199,8 @@ class Telegram(RPCHandler):
self._updater.start_polling(
bootstrap_retries=-1,
timeout=30,
read_latency=60,
timeout=20,
read_latency=60, # Assumed transmission latency
drop_pending_updates=True,
)
logger.info(
@ -213,6 +213,7 @@ class Telegram(RPCHandler):
Stops all running telegram threads.
:return: None
"""
# This can take up to `timeout` from the call to `start_polling`.
self._updater.stop()
def _format_buy_msg(self, msg: Dict[str, Any]) -> str:

14
freqtrade/strategy/interface.py
View File

@ -727,23 +727,21 @@ class IStrategy(ABC, HyperStrategyMixin):
custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
else:
custom_reason = None
# TODO: return here if sell-signal should be favored over ROI
if sell_signal in (SellType.CUSTOM_SELL, SellType.SELL_SIGNAL):
logger.debug(f"{trade.pair} - Sell signal received. "
f"sell_type=SellType.{sell_signal.name}" +
(f", custom_reason={custom_reason}" if custom_reason else ""))
return SellCheckTuple(sell_type=sell_signal, sell_reason=custom_reason)
# Start evaluations
# Sequence:
# ROI (if not stoploss)
# Sell-signal
# ROI (if not stoploss)
# Stoploss
if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
logger.debug(f"{trade.pair} - Required profit reached. sell_type=SellType.ROI")
return SellCheckTuple(sell_type=SellType.ROI)
if sell_signal != SellType.NONE:
logger.debug(f"{trade.pair} - Sell signal received. "
f"sell_type=SellType.{sell_signal.name}" +
(f", custom_reason={custom_reason}" if custom_reason else ""))
return SellCheckTuple(sell_type=sell_signal, sell_reason=custom_reason)
if stoplossflag.sell_flag:
logger.debug(f"{trade.pair} - Stoploss hit. sell_type={stoplossflag.sell_type}")

1
pyproject.toml
View File

@ -23,6 +23,7 @@ exclude = '''
line_length = 100
multi_line_output=0
lines_after_imports=2
skip_glob = ["**/.env*", "**/env/*", "**/.venv/*", "**/docs/*"]
[build-system]
requires = ["setuptools >= 46.4.0", "wheel"]

5
requirements.txt
View File

@ -1,4 +1,5 @@
numpy==1.21.5
numpy==1.21.5; python_version <= '3.7'
numpy==1.22.0; python_version > '3.7'
pandas==1.3.5
pandas-ta==0.3.14b
@ -18,7 +19,7 @@ technical==1.3.0
tabulate==0.8.9
pycoingecko==2.2.0
jinja2==3.0.3
tables==3.6.1
tables==3.7.0
blosc==1.10.6
# find first, C search in arrays

1
setup.cfg
View File

@ -17,6 +17,7 @@ classifiers =
Programming Language :: Python :: 3.7
Programming Language :: Python :: 3.8
Programming Language :: Python :: 3.9
Programming Language :: Python :: 3.10
Operating System :: MacOS
Operating System :: Unix
Topic :: Office/Business :: Financial :: Investment

5
setup.sh
View File

@ -25,7 +25,7 @@ function check_installed_python() {
exit 2
fi
for v in 9 8 7
for v in 9 10 8 7
do
PYTHON="python3.${v}"
which $PYTHON
@ -37,7 +37,6 @@ function check_installed_python() {
done
echo "No usable python found. Please make sure to have python3.7 or newer installed."
echo "python3.10 is currently not supported."
exit 1
}
@ -220,7 +219,7 @@ function install() {
install_redhat
else
echo "This script does not support your OS."
echo "If you have Python version 3.7 - 3.9, pip, virtualenv, ta-lib you can continue."
echo "If you have Python version 3.7 - 3.10, pip, virtualenv, ta-lib you can continue."
echo "Wait 10 seconds to continue the next install steps or use ctrl+c to interrupt this shell."
sleep 10
fi

23
tests/conftest.py
View File

@ -4,7 +4,6 @@ import logging
import re
from copy import deepcopy
from datetime import datetime, timedelta
from functools import reduce
from pathlib import Path
from unittest.mock import MagicMock, Mock, PropertyMock
@ -50,17 +49,23 @@ def pytest_configure(config):
def log_has(line, logs):
# caplog mocker returns log as a tuple: ('freqtrade.something', logging.WARNING, 'foobar')
# and we want to match line against foobar in the tuple
return reduce(lambda a, b: a or b,
filter(lambda x: x[2] == line, logs.record_tuples),
False)
"""Check if line is found on some caplog's message."""
return any(line == message for message in logs.messages)
def log_has_re(line, logs):
return reduce(lambda a, b: a or b,
filter(lambda x: re.match(line, x[2]), logs.record_tuples),
False)
"""Check if line matches some caplog's message."""
return any(re.match(line, message) for message in logs.messages)
def num_log_has(line, logs):
"""Check how many times line is found in caplog's messages."""
return sum(line == message for message in logs.messages)
def num_log_has_re(line, logs):
"""Check how many times line matches caplog's messages."""
return sum(bool(re.match(line, message)) for message in logs.messages)
def get_args(args):

22
tests/data/test_btanalysis.py
View File

@ -11,10 +11,10 @@ from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD,
analyze_trade_parallelism, calculate_csum,
calculate_market_change, calculate_max_drawdown,
combine_dataframes_with_mean, create_cum_profit,
extract_trades_of_period, get_latest_backtest_filename,
get_latest_hyperopt_file, load_backtest_data, load_trades,
load_trades_from_db)
calculate_underwater, combine_dataframes_with_mean,
create_cum_profit, extract_trades_of_period,
get_latest_backtest_filename, get_latest_hyperopt_file,
load_backtest_data, load_trades, load_trades_from_db)
from freqtrade.data.history import load_data, load_pair_history
from tests.conftest import create_mock_trades
from tests.conftest_trades import MOCK_TRADE_COUNT
@ -234,6 +234,13 @@ def test_combine_dataframes_with_mean(testdatadir):
assert "mean" in df.columns
def test_combine_dataframes_with_mean_no_data(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe='6m')
with pytest.raises(ValueError, match=r"No objects to concatenate"):
combine_dataframes_with_mean(data)
def test_create_cum_profit(testdatadir):
filename = testdatadir / "backtest-result_test.json"
bt_data = load_backtest_data(filename)
@ -284,9 +291,16 @@ def test_calculate_max_drawdown(testdatadir):
assert isinstance(lval, float)
assert hdate == Timestamp('2018-01-24 14:25:00', tz='UTC')
assert lowdate == Timestamp('2018-01-30 04:45:00', tz='UTC')
underwater = calculate_underwater(bt_data)
assert isinstance(underwater, DataFrame)
with pytest.raises(ValueError, match='Trade dataframe empty.'):
drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(DataFrame())
with pytest.raises(ValueError, match='Trade dataframe empty.'):
calculate_underwater(DataFrame())
def test_calculate_csum(testdatadir):
filename = testdatadir / "backtest-result_test.json"

4
tests/data/test_history.py
View File

@ -311,7 +311,7 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
assert td != len(data['UNITTEST/BTC'])
start_real = data['UNITTEST/BTC'].iloc[0, 0]
assert log_has(f'Missing data at start for pair '
f'UNITTEST/BTC, data starts at {start_real.strftime("%Y-%m-%d %H:%M:%S")}',
f'UNITTEST/BTC at 5m, data starts at {start_real.strftime("%Y-%m-%d %H:%M:%S")}',
caplog)
# Make sure we start fresh - test missing data at end
caplog.clear()
@ -326,7 +326,7 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
# Shift endtime with +5 - as last candle is dropped (partial candle)
end_real = arrow.get(data['UNITTEST/BTC'].iloc[-1, 0]).shift(minutes=5)
assert log_has(f'Missing data at end for pair '
f'UNITTEST/BTC, data ends at {end_real.strftime("%Y-%m-%d %H:%M:%S")}',
f'UNITTEST/BTC at 5m, data ends at {end_real.strftime("%Y-%m-%d %H:%M:%S")}',
caplog)

40
tests/exchange/test_exchange.py
View File

@ -20,7 +20,7 @@ from freqtrade.exchange.exchange import (market_is_active, timeframe_to_minutes,
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds)
from freqtrade.resolvers.exchange_resolver import ExchangeResolver
from tests.conftest import get_mock_coro, get_patched_exchange, log_has, log_has_re
from tests.conftest import get_mock_coro, get_patched_exchange, log_has, log_has_re, num_log_has_re
# Make sure to always keep one exchange here which is NOT subclassed!!
@ -1740,6 +1740,44 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_
(arrow.utcnow().int_timestamp - 2000) * 1000)
@pytest.mark.asyncio
async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
api_mock = MagicMock()
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection(
"kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
"symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
"429 Too Many Requests" '{"code":"429000","msg":"Too Many Requests"}'))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id="kucoin")
msg = "Kucoin 429 error, avoid triggering DDosProtection backoff delay"
assert not num_log_has_re(msg, caplog)
for _ in range(3):
with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
await exchange._async_get_candle_history(
"ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
assert num_log_has_re(msg, caplog) == 3
caplog.clear()
# Test regular non-kucoin message
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection(
"kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
"symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
"429 Too Many Requests" '{"code":"2222222","msg":"Too Many Requests"}'))
msg = r'_async_get_candle_history\(\) returned exception: .*'
msg2 = r'Applying DDosProtection backoff delay: .*'
with patch('freqtrade.exchange.common.asyncio.sleep', get_mock_coro(None)):
for _ in range(3):
with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
await exchange._async_get_candle_history(
"ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
# Expect the "returned exception" message 12 times (4 retries * 3 (loop))
assert num_log_has_re(msg, caplog) == 12
assert num_log_has_re(msg2, caplog) == 9
@pytest.mark.asyncio
async def test__async_get_candle_history_empty(default_conf, mocker, caplog):
""" Test empty exchange result """

6
tests/optimize/test_backtest_detail.py
View File

@ -426,8 +426,6 @@ tc26 = BTContainer(data=[
# Test 27: Sell with signal sell in candle 3 (ROI at signal candle)
# Stoploss at 10% (irrelevant), ROI at 5% (will trigger) - Wins over Sell-signal
# TODO: figure out if sell-signal should win over ROI
# Sell-signal wins over stoploss
tc27 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
@ -436,8 +434,8 @@ tc27 = BTContainer(data=[
[3, 5010, 5012, 4986, 5010, 6172, 0, 1], # sell-signal
[4, 5010, 5251, 4855, 4995, 6172, 0, 0], # Triggers ROI, sell-signal acted on
[5, 4995, 4995, 4950, 4950, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.05}, profit_perc=0.05, use_sell_signal=True,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=4)]
stop_loss=-0.10, roi={"0": 0.05}, profit_perc=0.002, use_sell_signal=True,
trades=[BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=1, close_tick=4)]
)
# Test 28: trailing_stop should raise so candle 3 causes a stoploss

7
tests/optimize/test_backtesting.py
View File

@ -1,6 +1,7 @@
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
import random
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from pathlib import Path
from unittest.mock import MagicMock, PropertyMock
@ -648,7 +649,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
result = backtesting.backtest(
processed=processed,
processed=deepcopy(processed),
start_date=min_date,
end_date=max_date,
max_open_trades=10,
@ -887,7 +888,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
backtest_conf = {
'processed': processed,
'processed': deepcopy(processed),
'start_date': min_date,
'end_date': max_date,
'max_open_trades': 3,
@ -909,7 +910,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
'NXT/BTC', '5m')[0]) == len(data['NXT/BTC']) - 1 - backtesting.strategy.startup_candle_count
backtest_conf = {
'processed': processed,
'processed': deepcopy(processed),
'start_date': min_date,
'end_date': max_date,
'max_open_trades': 1,

22
tests/plugins/test_pairlist.py
View File

@ -15,7 +15,7 @@ from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.resolvers import PairListResolver
from tests.conftest import (create_mock_trades, get_patched_exchange, get_patched_freqtradebot,
log_has, log_has_re)
log_has, log_has_re, num_log_has)
@pytest.fixture(scope="function")
@ -237,19 +237,13 @@ def test_remove_logs_for_pairs_already_in_blacklist(mocker, markets, static_pl_c
# Ensure that log message wasn't generated.
assert not log_has('Pair BLK/BTC in your blacklist. Removing it from whitelist...', caplog)
new_whitelist = freqtrade.pairlists.verify_blacklist(whitelist + ['BLK/BTC'], logger.warning)
# Ensure that the pair is removed from the white list, and properly logged.
assert set(whitelist) == set(new_whitelist)
matches = sum(1 for message in caplog.messages
if message == 'Pair BLK/BTC in your blacklist. Removing it from whitelist...')
assert matches == 1
new_whitelist = freqtrade.pairlists.verify_blacklist(whitelist + ['BLK/BTC'], logger.warning)
# Ensure that the pair is not logged anymore when being removed from the pair list.
assert set(whitelist) == set(new_whitelist)
matches = sum(1 for message in caplog.messages
if message == 'Pair BLK/BTC in your blacklist. Removing it from whitelist...')
assert matches == 1
for _ in range(3):
new_whitelist = freqtrade.pairlists.verify_blacklist(
whitelist + ['BLK/BTC'], logger.warning)
# Ensure that the pair is removed from the white list, and properly logged.
assert set(whitelist) == set(new_whitelist)
assert num_log_has('Pair BLK/BTC in your blacklist. Removing it from whitelist...',
caplog) == 1
def test_refresh_pairlist_dynamic(mocker, shitcoinmarkets, tickers, whitelist_conf):

4
tests/rpc/test_rpc.py
View File

@ -424,7 +424,7 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
assert stats['trade_count'] == 2
assert stats['first_trade_date'] == 'just now'
assert stats['latest_trade_date'] == 'just now'
assert stats['avg_duration'] in ('0:00:00', '0:00:01')
assert stats['avg_duration'] in ('0:00:00', '0:00:01', '0:00:02')
assert stats['best_pair'] == 'ETH/BTC'
assert prec_satoshi(stats['best_rate'], 6.2)
@ -435,7 +435,7 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
assert stats['trade_count'] == 2
assert stats['first_trade_date'] == 'just now'
assert stats['latest_trade_date'] == 'just now'
assert stats['avg_duration'] in ('0:00:00', '0:00:01')
assert stats['avg_duration'] in ('0:00:00', '0:00:01', '0:00:02')
assert stats['best_pair'] == 'ETH/BTC'
assert prec_satoshi(stats['best_rate'], 6.2)
assert isnan(stats['profit_all_coin'])

2
tests/rpc/test_rpc_telegram.py
View File

@ -584,7 +584,7 @@ def test_monthly_handle(default_conf, update, ticker, limit_buy_order, fee,
assert 'Monthly Profit over the last 2 months</b>:' in msg_mock.call_args_list[0][0][0]
assert 'Month ' in msg_mock.call_args_list[0][0][0]
today = datetime.utcnow().date()
current_month = f"{today.year}-{today.month} "
current_month = f"{today.year}-{today.month:02} "
assert current_month in msg_mock.call_args_list[0][0][0]
assert str(' 0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 0.933 USD') in msg_mock.call_args_list[0][0][0]

8
tests/test_freqtradebot.py
View File

@ -1905,7 +1905,7 @@ def test_handle_trade_roi(default_conf_usdt, ticker_usdt, limit_buy_order_usdt_o
# we might just want to check if we are in a sell condition without
# executing
# if ROI is reached we must sell
patch_get_signal(freqtrade, value=(False, True, None, None))
patch_get_signal(freqtrade, value=(False, False, None, None))
assert freqtrade.handle_trade(trade)
assert log_has("ETH/USDT - Required profit reached. sell_type=SellType.ROI",
caplog)
@ -3242,7 +3242,7 @@ def test_ignore_roi_if_buy_signal(default_conf_usdt, limit_buy_order_usdt,
assert freqtrade.handle_trade(trade) is False
# Test if buy-signal is absent (should sell due to roi = true)
patch_get_signal(freqtrade, value=(False, True, None, None))
patch_get_signal(freqtrade, value=(False, False, None, None))
assert freqtrade.handle_trade(trade) is True
assert trade.sell_reason == SellType.ROI.value
@ -3428,11 +3428,11 @@ def test_disable_ignore_roi_if_buy_signal(default_conf_usdt, limit_buy_order_usd
trade = Trade.query.first()
trade.update(limit_buy_order_usdt)
# Sell due to min_roi_reached
patch_get_signal(freqtrade, value=(True, True, None, None))
patch_get_signal(freqtrade, value=(True, False, None, None))
assert freqtrade.handle_trade(trade) is True
# Test if buy-signal is absent
patch_get_signal(freqtrade, value=(False, True, None, None))
patch_get_signal(freqtrade, value=(False, False, None, None))
assert freqtrade.handle_trade(trade) is True
assert trade.sell_reason == SellType.ROI.value

11
tests/test_plotting.py
View File

@ -336,15 +336,20 @@ def test_generate_profit_graph(testdatadir):
assert fig.layout.yaxis3.title.text == "Profit BTC"
figure = fig.layout.figure
assert len(figure.data) == 5
assert len(figure.data) == 7
avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
assert isinstance(avgclose, go.Scatter)
profit = find_trace_in_fig_data(figure.data, "Profit")
assert isinstance(profit, go.Scatter)
profit = find_trace_in_fig_data(figure.data, "Max drawdown 10.45%")
assert isinstance(profit, go.Scatter)
drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 10.45%")
assert isinstance(drawdown, go.Scatter)
parallel = find_trace_in_fig_data(figure.data, "Parallel trades")
assert isinstance(parallel, go.Scatter)
underwater = find_trace_in_fig_data(figure.data, "Underwater Plot")
assert isinstance(underwater, go.Scatter)
for pair in pairs:
profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")