Merge remote-tracking branch 'origin/strategy_utils' into strategy_utils

This commit is contained in:
hippocritical 2023-01-05 22:56:29 +01:00
commit feb6accc6c
22 changed files with 332 additions and 119 deletions

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@ -1,6 +1,7 @@
# ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg) # ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/) [![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)
[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop) [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
[![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io) [![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)
[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability) [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)

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@ -300,7 +300,11 @@ A backtesting result will look like that:
| Absolute profit | 0.00762792 BTC | | Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% | | Total profit % | 76.2% |
| CAGR % | 460.87% | | CAGR % | 460.87% |
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 | | Profit factor | 1.11 |
| Expectancy | -0.15 |
| Avg. stake amount | 0.001 BTC | | Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC | | Total trade volume | 0.429 BTC |
| | | | | |
@ -400,7 +404,11 @@ It contains some useful key metrics about performance of your strategy on backte
| Absolute profit | 0.00762792 BTC | | Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% | | Total profit % | 76.2% |
| CAGR % | 460.87% | | CAGR % | 460.87% |
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 | | Profit factor | 1.11 |
| Expectancy | -0.15 |
| Avg. stake amount | 0.001 BTC | | Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC | | Total trade volume | 0.429 BTC |
| | | | | |
@ -447,6 +455,9 @@ It contains some useful key metrics about performance of your strategy on backte
- `Absolute profit`: Profit made in stake currency. - `Absolute profit`: Profit made in stake currency.
- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital Starting capital) / Starting capital`. - `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital Starting capital) / Starting capital`.
- `CAGR %`: Compound annual growth rate. - `CAGR %`: Compound annual growth rate.
- `Sortino`: Annualized Sortino ratio.
- `Sharpe`: Annualized Sharpe ratio.
- `Calmar`: Annualized Calmar ratio.
- `Profit factor`: profit / loss. - `Profit factor`: profit / loss.
- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount. - `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
- `Total trade volume`: Volume generated on the exchange to reach the above profit. - `Total trade volume`: Volume generated on the exchange to reach the above profit.

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@ -365,7 +365,7 @@ class MyAwesomeStrategy(IStrategy):
timeframe = '15m' timeframe = '15m'
minimal_roi = { minimal_roi = {
"0": 0.10 "0": 0.10
}, }
# Define the parameter spaces # Define the parameter spaces
buy_ema_short = IntParameter(3, 50, default=5) buy_ema_short = IntParameter(3, 50, default=5)
buy_ema_long = IntParameter(15, 200, default=50) buy_ema_long = IntParameter(15, 200, default=50)
@ -400,7 +400,7 @@ class MyAwesomeStrategy(IStrategy):
return dataframe return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = [] conditions = []
conditions.append(qtpylib.crossed_above( conditions.append(qtpylib.crossed_above(
dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}'] dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
)) ))

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@ -1,6 +1,7 @@
![freqtrade](assets/freqtrade_poweredby.svg) ![freqtrade](assets/freqtrade_poweredby.svg)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/) [![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)
[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop) [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability) [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)

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@ -92,6 +92,8 @@ One account is used to share collateral between markets (trading pairs). Margin
"margin_mode": "cross" "margin_mode": "cross"
``` ```
Please read the [exchange specific notes](exchanges.md) for exchanges that support this mode and how they differ.
## Set leverage to use ## Set leverage to use
Different strategies and risk profiles will require different levels of leverage. Different strategies and risk profiles will require different levels of leverage.

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@ -11,9 +11,6 @@
{% endif %} {% endif %}
<div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" {{ hidden }}> <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" {{ hidden }}>
<div class="md-sidebar__scrollwrap"> <div class="md-sidebar__scrollwrap">
<div id="widget-wrapper">
</div>
<div class="md-sidebar__inner"> <div class="md-sidebar__inner">
{% include "partials/nav.html" %} {% include "partials/nav.html" %}
</div> </div>
@ -44,25 +41,4 @@
<script src="https://code.jquery.com/jquery-3.4.1.min.js" <script src="https://code.jquery.com/jquery-3.4.1.min.js"
integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
<!-- Load binance SDK -->
<script async defer src="https://public.bnbstatic.com/static/js/broker-sdk/broker-sdk@1.0.0.min.js"></script>
<script>
window.onload = function () {
var sidebar = document.getElementById('widget-wrapper')
var newDiv = document.createElement("div");
newDiv.id = "widget";
try {
sidebar.prepend(newDiv);
window.binanceBrokerPortalSdk.initBrokerSDK('#widget', {
apiHost: 'https://www.binance.com',
brokerId: 'R4BD3S82',
slideTime: 4e4,
});
} catch(err) {
console.log(err)
}
}
</script>
{% endblock %} {% endblock %}

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@ -11,18 +11,3 @@
.rst-versions .rst-other-versions { .rst-versions .rst-other-versions {
color: white; color: white;
} }
#widget-wrapper {
height: calc(220px * 0.5625 + 18px);
width: 220px;
margin: 0 auto 16px auto;
border-style: solid;
border-color: var(--md-code-bg-color);
border-width: 1px;
border-radius: 5px;
}
@media screen and (max-width: calc(76.25em - 1px)) {
#widget-wrapper { display: none; }
}

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@ -1,5 +1,5 @@
""" Freqtrade bot """ """ Freqtrade bot """
__version__ = '2022.12.dev' __version__ = '2023.1.dev'
if 'dev' in __version__: if 'dev' in __version__:
try: try:

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@ -52,7 +52,7 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
return analysed_trades_dict return analysed_trades_dict
def _analyze_candles_and_indicators(pair, trades, signal_candles): def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles: pd.DataFrame):
buyf = signal_candles buyf = signal_candles
if len(buyf) > 0: if len(buyf) > 0:
@ -120,7 +120,7 @@ def _do_group_table_output(bigdf, glist):
else: else:
agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'], agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']} 'profit_ratio': ['median', 'mean', 'sum']}
agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median', agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct', 'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
'total_profit_pct'] 'total_profit_pct']

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@ -1,4 +1,6 @@
import logging import logging
import math
from datetime import datetime
from typing import Dict, Tuple from typing import Dict, Tuple
import numpy as np import numpy as np
@ -190,3 +192,119 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
:return: CAGR :return: CAGR
""" """
return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1 return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
def calculate_expectancy(trades: pd.DataFrame) -> float:
"""
Calculate expectancy
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:return: expectancy
"""
if len(trades) == 0:
return 0
expectancy = 1
profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
if (nb_win_trades > 0) and (nb_loss_trades > 0):
average_win = profit_sum / nb_win_trades
average_loss = loss_sum / nb_loss_trades
risk_reward_ratio = average_win / average_loss
winrate = nb_win_trades / len(trades)
expectancy = ((1 + risk_reward_ratio) * winrate) - 1
elif nb_win_trades == 0:
expectancy = 0
return expectancy
def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
starting_balance: float) -> float:
"""
Calculate sortino
:param trades: DataFrame containing trades (requires columns profit_abs)
:return: sortino
"""
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
return 0
total_profit = trades['profit_abs'] / starting_balance
days_period = max(1, (max_date - min_date).days)
expected_returns_mean = total_profit.sum() / days_period
down_stdev = np.std(trades.loc[trades['profit_abs'] < 0, 'profit_abs'] / starting_balance)
if down_stdev != 0 and not np.isnan(down_stdev):
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 = -100
# print(expected_returns_mean, down_stdev, sortino_ratio)
return sortino_ratio
def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
starting_balance: float) -> float:
"""
Calculate sharpe
:param trades: DataFrame containing trades (requires column profit_abs)
:return: sharpe
"""
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
return 0
total_profit = trades['profit_abs'] / starting_balance
days_period = max(1, (max_date - min_date).days)
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 = -100
# print(expected_returns_mean, up_stdev, sharp_ratio)
return sharp_ratio
def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
starting_balance: float) -> float:
"""
Calculate calmar
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
:return: calmar
"""
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
return 0
total_profit = trades['profit_abs'].sum() / starting_balance
days_period = max(1, (max_date - min_date).days)
# adding slippage of 0.1% per trade
# total_profit = total_profit - 0.0005
expected_returns_mean = total_profit / days_period * 100
# calculate max drawdown
try:
_, _, _, _, _, max_drawdown = calculate_max_drawdown(
trades, value_col="profit_abs", starting_balance=starting_balance
)
except ValueError:
max_drawdown = 0
if max_drawdown != 0:
calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
else:
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
calmar_ratio = -100
# print(expected_returns_mean, max_drawdown, calmar_ratio)
return calmar_ratio

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@ -2035,8 +2035,8 @@ class Exchange:
# Fetch OHLCV asynchronously # Fetch OHLCV asynchronously
s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else '' s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
logger.debug( logger.debug(
"Fetching pair %s, interval %s, since %s %s...", "Fetching pair %s, %s, interval %s, since %s %s...",
pair, timeframe, since_ms, s pair, candle_type, timeframe, since_ms, s
) )
params = deepcopy(self._ft_has.get('ohlcv_params', {})) params = deepcopy(self._ft_has.get('ohlcv_params', {}))
candle_limit = self.ohlcv_candle_limit( candle_limit = self.ohlcv_candle_limit(
@ -2050,11 +2050,12 @@ class Exchange:
limit=candle_limit, params=params) limit=candle_limit, params=params)
else: else:
# Funding rate # Funding rate
data = await self._api_async.fetch_funding_rate_history( data = await self._fetch_funding_rate_history(
pair, since=since_ms, pair=pair,
limit=candle_limit) timeframe=timeframe,
# Convert funding rate to candle pattern limit=candle_limit,
data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data] since_ms=since_ms,
)
# Some exchanges sort OHLCV in ASC order and others in DESC. # 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) # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
# while GDAX returns the list of OHLCV in DESC order (newest first, oldest last) # while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
@ -2082,6 +2083,24 @@ class Exchange:
raise OperationalException(f'Could not fetch historical candle (OHLCV) data ' raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair}. Message: {e}') from e f'for pair {pair}. Message: {e}') from e
async def _fetch_funding_rate_history(
self,
pair: str,
timeframe: str,
limit: int,
since_ms: Optional[int] = None,
) -> List[List]:
"""
Fetch funding rate history - used to selectively override this by subclasses.
"""
# Funding rate
data = await self._api_async.fetch_funding_rate_history(
pair, since=since_ms,
limit=limit)
# Convert funding rate to candle pattern
data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
return data
# Fetch historic trades # Fetch historic trades
@retrier_async @retrier_async
@ -2745,11 +2764,16 @@ class Exchange:
""" """
Important: Must be fetching data from cached values as this is used by backtesting! Important: Must be fetching data from cached values as this is used by backtesting!
PERPETUAL: PERPETUAL:
gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-price gateio: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
> Liquidation Price = (Entry Price ± Margin / Contract Multiplier / Size) /
[ 1 ± (Maintenance Margin Ratio + Taker Rate)]
Wherein, "+" or "-" depends on whether the contract goes long or short:
"-" for long, and "+" for short.
okex: https://www.okex.com/support/hc/en-us/articles/ okex: https://www.okex.com/support/hc/en-us/articles/
360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin 360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
:param exchange_name: :param pair: Pair to calculate liquidation price for
:param open_rate: Entry price of position :param open_rate: Entry price of position
:param is_short: True if the trade is a short, false otherwise :param is_short: True if the trade is a short, false otherwise
:param amount: Absolute value of position size incl. leverage (in base currency) :param amount: Absolute value of position size incl. leverage (in base currency)
@ -2789,7 +2813,7 @@ class Exchange:
def get_maintenance_ratio_and_amt( def get_maintenance_ratio_and_amt(
self, self,
pair: str, pair: str,
nominal_value: float = 0.0, nominal_value: float,
) -> Tuple[float, Optional[float]]: ) -> Tuple[float, Optional[float]]:
""" """
Important: Must be fetching data from cached values as this is used by backtesting! Important: Must be fetching data from cached values as this is used by backtesting!

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@ -1177,6 +1177,7 @@ class Backtesting:
open_trade_count_start = self.backtest_loop( open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades, row, pair, current_time, end_date, max_open_trades,
open_trade_count_start) open_trade_count_start)
continue
detail_data.loc[:, 'enter_long'] = row[LONG_IDX] detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX] detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX] detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]

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@ -9,8 +9,9 @@ from tabulate import tabulate
from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT, from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
Config) Config)
from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change, from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
calculate_max_drawdown) calculate_expectancy, calculate_market_change,
calculate_max_drawdown, calculate_sharpe, calculate_sortino)
from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
@ -448,6 +449,10 @@ def generate_strategy_stats(pairlist: List[str],
'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(), 'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(), 'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']), 'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
'expectancy': calculate_expectancy(results),
'sortino': calculate_sortino(results, min_date, max_date, start_balance),
'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
'calmar': calculate_calmar(results, min_date, max_date, start_balance),
'profit_factor': profit_factor, 'profit_factor': profit_factor,
'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT), 'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts': int(min_date.timestamp() * 1000), 'backtest_start_ts': int(min_date.timestamp() * 1000),
@ -785,8 +790,13 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])), strat_results['stake_currency'])),
('Total profit %', f"{strat_results['profit_total']:.2%}"), ('Total profit %', f"{strat_results['profit_total']:.2%}"),
('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'), ('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor' ('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
in strat_results else 'N/A'), in strat_results else 'N/A'),
('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
in strat_results else 'N/A'),
('Trades per day', strat_results['trades_per_day']), ('Trades per day', strat_results['trades_per_day']),
('Avg. daily profit %', ('Avg. daily profit %',
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"), f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),

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@ -135,7 +135,7 @@ class VolumePairList(IPairList):
filtered_tickers = [ filtered_tickers = [
v for k, v in tickers.items() v for k, v in tickers.items()
if (self._exchange.get_pair_quote_currency(k) == self._stake_currency if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
and (self._use_range or v[self._sort_key] is not None) and (self._use_range or v.get(self._sort_key) is not None)
and v['symbol'] in _pairlist)] and v['symbol'] in _pairlist)]
pairlist = [s['symbol'] for s in filtered_tickers] pairlist = [s['symbol'] for s in filtered_tickers]
else: else:

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@ -28,7 +28,7 @@ class FreqaiExampleStrategy(IStrategy):
plot_config = { plot_config = {
"main_plot": {}, "main_plot": {},
"subplots": { "subplots": {
"prediction": {"prediction": {"color": "blue"}}, "&-s_close": {"prediction": {"color": "blue"}},
"do_predict": { "do_predict": {
"do_predict": {"color": "brown"}, "do_predict": {"color": "brown"},
}, },
@ -140,7 +140,8 @@ class FreqaiExampleStrategy(IStrategy):
# If user wishes to use multiple targets, they can add more by # If user wishes to use multiple targets, they can add more by
# appending more columns with '&'. User should keep in mind that multi targets # appending more columns with '&'. User should keep in mind that multi targets
# requires a multioutput prediction model such as # requires a multioutput prediction model such as
# templates/CatboostPredictionMultiModel.py, # freqai/prediction_models/CatboostRegressorMultiTarget.py,
# freqtrade trade --freqaimodel CatboostRegressorMultiTarget
# df["&-s_range"] = ( # df["&-s_range"] = (
# df["close"] # df["close"]

View File

@ -18,7 +18,7 @@ pytest-mock==3.10.0
pytest-random-order==1.1.0 pytest-random-order==1.1.0
isort==5.11.4 isort==5.11.4
# For datetime mocking # For datetime mocking
time-machine==2.8.2 time-machine==2.9.0
# fastapi testing # fastapi testing
httpx==0.23.1 httpx==0.23.1

View File

@ -5,5 +5,5 @@
scipy==1.9.3 scipy==1.9.3
scikit-learn==1.1.3 scikit-learn==1.1.3
scikit-optimize==0.9.0 scikit-optimize==0.9.0
filelock==3.8.2 filelock==3.9.0
progressbar2==4.2.0 progressbar2==4.2.0

View File

@ -19,7 +19,7 @@ technical==1.3.0
tabulate==0.9.0 tabulate==0.9.0
pycoingecko==3.1.0 pycoingecko==3.1.0
jinja2==3.1.2 jinja2==3.1.2
tables==3.7.0 tables==3.8.0
blosc==1.11.1 blosc==1.11.1
joblib==1.2.0 joblib==1.2.0
pyarrow==10.0.1; platform_machine != 'armv7l' pyarrow==10.0.1; platform_machine != 'armv7l'
@ -37,7 +37,7 @@ sdnotify==0.3.2
# API Server # API Server
fastapi==0.88.0 fastapi==0.88.0
pydantic==1.10.2 pydantic==1.10.4
uvicorn==0.20.0 uvicorn==0.20.0
pyjwt==2.6.0 pyjwt==2.6.0
aiofiles==22.1.0 aiofiles==22.1.0

View File

@ -25,6 +25,11 @@ freqai_rl = [
'sb3-contrib' 'sb3-contrib'
] ]
hdf5 = [
'tables',
'blosc',
]
develop = [ develop = [
'coveralls', 'coveralls',
'flake8', 'flake8',
@ -44,7 +49,7 @@ jupyter = [
'nbconvert', 'nbconvert',
] ]
all_extra = plot + develop + jupyter + hyperopt + freqai + freqai_rl all_extra = plot + develop + jupyter + hyperopt + hdf5 + freqai + freqai_rl
setup( setup(
tests_require=[ tests_require=[
@ -78,8 +83,6 @@ setup(
'prompt-toolkit', 'prompt-toolkit',
'numpy', 'numpy',
'pandas', 'pandas',
'tables',
'blosc',
'joblib>=1.2.0', 'joblib>=1.2.0',
'pyarrow; platform_machine != "armv7l"', 'pyarrow; platform_machine != "armv7l"',
'fastapi', 'fastapi',
@ -97,6 +100,7 @@ setup(
'plot': plot, 'plot': plot,
'jupyter': jupyter, 'jupyter': jupyter,
'hyperopt': hyperopt, 'hyperopt': hyperopt,
'hdf5': hdf5,
'freqai': freqai, 'freqai': freqai,
'freqai_rl': freqai_rl, 'freqai_rl': freqai_rl,
'all': all_extra, 'all': all_extra,

View File

@ -12,9 +12,11 @@ from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelis
get_latest_hyperopt_file, load_backtest_data, get_latest_hyperopt_file, load_backtest_data,
load_backtest_metadata, load_trades, load_trades_from_db) load_backtest_metadata, load_trades, load_trades_from_db)
from freqtrade.data.history import load_data, load_pair_history from freqtrade.data.history import load_data, load_pair_history
from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change, from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
calculate_max_drawdown, calculate_underwater, calculate_expectancy, calculate_market_change,
combine_dataframes_with_mean, create_cum_profit) calculate_max_drawdown, calculate_sharpe, calculate_sortino,
calculate_underwater, combine_dataframes_with_mean,
create_cum_profit)
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
from tests.conftest_trades import MOCK_TRADE_COUNT from tests.conftest_trades import MOCK_TRADE_COUNT
@ -336,6 +338,69 @@ def test_calculate_csum(testdatadir):
csum_min, csum_max = calculate_csum(DataFrame()) csum_min, csum_max = calculate_csum(DataFrame())
def test_calculate_expectancy(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
expectancy = calculate_expectancy(DataFrame())
assert expectancy == 0.0
expectancy = calculate_expectancy(bt_data)
assert isinstance(expectancy, float)
assert pytest.approx(expectancy) == 0.07151374226574791
def test_calculate_sortino(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
sortino = calculate_sortino(DataFrame(), None, None, 0)
assert sortino == 0.0
sortino = calculate_sortino(
bt_data,
bt_data['open_date'].min(),
bt_data['close_date'].max(),
0.01,
)
assert isinstance(sortino, float)
assert pytest.approx(sortino) == 35.17722
def test_calculate_sharpe(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
sharpe = calculate_sharpe(DataFrame(), None, None, 0)
assert sharpe == 0.0
sharpe = calculate_sharpe(
bt_data,
bt_data['open_date'].min(),
bt_data['close_date'].max(),
0.01,
)
assert isinstance(sharpe, float)
assert pytest.approx(sharpe) == 44.5078669
def test_calculate_calmar(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
calmar = calculate_calmar(DataFrame(), None, None, 0)
assert calmar == 0.0
calmar = calculate_calmar(
bt_data,
bt_data['open_date'].min(),
bt_data['close_date'].max(),
0.01,
)
assert isinstance(calmar, float)
assert pytest.approx(calmar) == 559.040508
@pytest.mark.parametrize('start,end,days, expected', [ @pytest.mark.parametrize('start,end,days, expected', [
(64900, 176000, 3 * 365, 0.3945), (64900, 176000, 3 * 365, 0.3945),
(64900, 176000, 365, 1.7119), (64900, 176000, 365, 1.7119),

View File

@ -8,16 +8,19 @@ suitable to run with freqtrade.
from copy import deepcopy from copy import deepcopy
from datetime import datetime, timedelta, timezone from datetime import datetime, timedelta, timezone
from pathlib import Path from pathlib import Path
from typing import Tuple
import pytest import pytest
from freqtrade.enums import CandleType from freqtrade.enums import CandleType
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
from freqtrade.exchange.exchange import timeframe_to_msecs from freqtrade.exchange.exchange import Exchange, timeframe_to_msecs
from freqtrade.resolvers.exchange_resolver import ExchangeResolver from freqtrade.resolvers.exchange_resolver import ExchangeResolver
from tests.conftest import get_default_conf_usdt from tests.conftest import get_default_conf_usdt
EXCHANGE_FIXTURE_TYPE = Tuple[Exchange, str]
# Exchanges that should be tested # Exchanges that should be tested
EXCHANGES = { EXCHANGES = {
'bittrex': { 'bittrex': {
@ -141,19 +144,19 @@ def exchange_futures(request, exchange_conf, class_mocker):
@pytest.mark.longrun @pytest.mark.longrun
class TestCCXTExchange(): class TestCCXTExchange():
def test_load_markets(self, exchange): def test_load_markets(self, exchange: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair'] pair = EXCHANGES[exchangename]['pair']
markets = exchange.markets markets = exch.markets
assert pair in markets assert pair in markets
assert isinstance(markets[pair], dict) assert isinstance(markets[pair], dict)
assert exchange.market_is_spot(markets[pair]) assert exch.market_is_spot(markets[pair])
def test_has_validations(self, exchange): def test_has_validations(self, exchange: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange exch, exchangename = exchange
exchange.validate_ordertypes({ exch.validate_ordertypes({
'entry': 'limit', 'entry': 'limit',
'exit': 'limit', 'exit': 'limit',
'stoploss': 'limit', 'stoploss': 'limit',
@ -162,13 +165,13 @@ class TestCCXTExchange():
if exchangename == 'gateio': if exchangename == 'gateio':
# gateio doesn't have market orders on spot # gateio doesn't have market orders on spot
return return
exchange.validate_ordertypes({ exch.validate_ordertypes({
'entry': 'market', 'entry': 'market',
'exit': 'market', 'exit': 'market',
'stoploss': 'market', 'stoploss': 'market',
}) })
def test_load_markets_futures(self, exchange_futures): def test_load_markets_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures exchange, exchangename = exchange_futures
if not exchange: if not exchange:
# exchange_futures only returns values for supported exchanges # exchange_futures only returns values for supported exchanges
@ -181,11 +184,11 @@ class TestCCXTExchange():
assert exchange.market_is_future(markets[pair]) assert exchange.market_is_future(markets[pair])
def test_ccxt_fetch_tickers(self, exchange): def test_ccxt_fetch_tickers(self, exchange: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair'] pair = EXCHANGES[exchangename]['pair']
tickers = exchange.get_tickers() tickers = exch.get_tickers()
assert pair in tickers assert pair in tickers
assert 'ask' in tickers[pair] assert 'ask' in tickers[pair]
assert tickers[pair]['ask'] is not None assert tickers[pair]['ask'] is not None
@ -195,11 +198,11 @@ class TestCCXTExchange():
if EXCHANGES[exchangename].get('hasQuoteVolume'): if EXCHANGES[exchangename].get('hasQuoteVolume'):
assert tickers[pair]['quoteVolume'] is not None assert tickers[pair]['quoteVolume'] is not None
def test_ccxt_fetch_ticker(self, exchange): def test_ccxt_fetch_ticker(self, exchange: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair'] pair = EXCHANGES[exchangename]['pair']
ticker = exchange.fetch_ticker(pair) ticker = exch.fetch_ticker(pair)
assert 'ask' in ticker assert 'ask' in ticker
assert ticker['ask'] is not None assert ticker['ask'] is not None
assert 'bid' in ticker assert 'bid' in ticker
@ -208,21 +211,21 @@ class TestCCXTExchange():
if EXCHANGES[exchangename].get('hasQuoteVolume'): if EXCHANGES[exchangename].get('hasQuoteVolume'):
assert ticker['quoteVolume'] is not None assert ticker['quoteVolume'] is not None
def test_ccxt_fetch_l2_orderbook(self, exchange): def test_ccxt_fetch_l2_orderbook(self, exchange: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair'] pair = EXCHANGES[exchangename]['pair']
l2 = exchange.fetch_l2_order_book(pair) l2 = exch.fetch_l2_order_book(pair)
assert 'asks' in l2 assert 'asks' in l2
assert 'bids' in l2 assert 'bids' in l2
assert len(l2['asks']) >= 1 assert len(l2['asks']) >= 1
assert len(l2['bids']) >= 1 assert len(l2['bids']) >= 1
l2_limit_range = exchange._ft_has['l2_limit_range'] l2_limit_range = exch._ft_has['l2_limit_range']
l2_limit_range_required = exchange._ft_has['l2_limit_range_required'] l2_limit_range_required = exch._ft_has['l2_limit_range_required']
if exchangename == 'gateio': if exchangename == 'gateio':
# TODO: Gateio is unstable here at the moment, ignoring the limit partially. # TODO: Gateio is unstable here at the moment, ignoring the limit partially.
return return
for val in [1, 2, 5, 25, 100]: for val in [1, 2, 5, 25, 100]:
l2 = exchange.fetch_l2_order_book(pair, val) l2 = exch.fetch_l2_order_book(pair, val)
if not l2_limit_range or val in l2_limit_range: if not l2_limit_range or val in l2_limit_range:
if val > 50: if val > 50:
# Orderbooks are not always this deep. # Orderbooks are not always this deep.
@ -232,7 +235,7 @@ class TestCCXTExchange():
assert len(l2['asks']) == val assert len(l2['asks']) == val
assert len(l2['bids']) == val assert len(l2['bids']) == val
else: else:
next_limit = exchange.get_next_limit_in_list( next_limit = exch.get_next_limit_in_list(
val, l2_limit_range, l2_limit_range_required) val, l2_limit_range, l2_limit_range_required)
if next_limit is None: if next_limit is None:
assert len(l2['asks']) > 100 assert len(l2['asks']) > 100
@ -245,23 +248,23 @@ class TestCCXTExchange():
assert len(l2['asks']) == next_limit assert len(l2['asks']) == next_limit
assert len(l2['asks']) == next_limit assert len(l2['asks']) == next_limit
def test_ccxt_fetch_ohlcv(self, exchange): def test_ccxt_fetch_ohlcv(self, exchange: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair'] pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe'] timeframe = EXCHANGES[exchangename]['timeframe']
pair_tf = (pair, timeframe, CandleType.SPOT) pair_tf = (pair, timeframe, CandleType.SPOT)
ohlcv = exchange.refresh_latest_ohlcv([pair_tf]) ohlcv = exch.refresh_latest_ohlcv([pair_tf])
assert isinstance(ohlcv, dict) assert isinstance(ohlcv, dict)
assert len(ohlcv[pair_tf]) == len(exchange.klines(pair_tf)) assert len(ohlcv[pair_tf]) == len(exch.klines(pair_tf))
# assert len(exchange.klines(pair_tf)) > 200 # assert len(exch.klines(pair_tf)) > 200
# Assume 90% uptime ... # Assume 90% uptime ...
assert len(exchange.klines(pair_tf)) > exchange.ohlcv_candle_limit( assert len(exch.klines(pair_tf)) > exch.ohlcv_candle_limit(
timeframe, CandleType.SPOT) * 0.90 timeframe, CandleType.SPOT) * 0.90
# Check if last-timeframe is within the last 2 intervals # Check if last-timeframe is within the last 2 intervals
now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2)) now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2))
assert exchange.klines(pair_tf).iloc[-1]['date'] >= timeframe_to_prev_date(timeframe, now) assert exch.klines(pair_tf).iloc[-1]['date'] >= timeframe_to_prev_date(timeframe, now)
def ccxt__async_get_candle_history(self, exchange, exchangename, pair, timeframe, candle_type): def ccxt__async_get_candle_history(self, exchange, exchangename, pair, timeframe, candle_type):
@ -289,17 +292,17 @@ class TestCCXTExchange():
assert len(candles) >= min(candle_count, candle_count1) assert len(candles) >= min(candle_count, candle_count1)
assert candles[0][0] == since_ms or (since_ms + timeframe_ms) assert candles[0][0] == since_ms or (since_ms + timeframe_ms)
def test_ccxt__async_get_candle_history(self, exchange): def test_ccxt__async_get_candle_history(self, exchange: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange exc, exchangename = exchange
# For some weired reason, this test returns random lengths for bittrex. # For some weired reason, this test returns random lengths for bittrex.
if not exchange._ft_has['ohlcv_has_history'] or exchangename in ('bittrex'): if not exc._ft_has['ohlcv_has_history'] or exchangename in ('bittrex'):
return return
pair = EXCHANGES[exchangename]['pair'] pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe'] timeframe = EXCHANGES[exchangename]['timeframe']
self.ccxt__async_get_candle_history( self.ccxt__async_get_candle_history(
exchange, exchangename, pair, timeframe, CandleType.SPOT) exc, exchangename, pair, timeframe, CandleType.SPOT)
def test_ccxt__async_get_candle_history_futures(self, exchange_futures): def test_ccxt__async_get_candle_history_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures exchange, exchangename = exchange_futures
if not exchange: if not exchange:
# exchange_futures only returns values for supported exchanges # exchange_futures only returns values for supported exchanges
@ -309,7 +312,7 @@ class TestCCXTExchange():
self.ccxt__async_get_candle_history( self.ccxt__async_get_candle_history(
exchange, exchangename, pair, timeframe, CandleType.FUTURES) exchange, exchangename, pair, timeframe, CandleType.FUTURES)
def test_ccxt_fetch_funding_rate_history(self, exchange_futures): def test_ccxt_fetch_funding_rate_history(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures exchange, exchangename = exchange_futures
if not exchange: if not exchange:
# exchange_futures only returns values for supported exchanges # exchange_futures only returns values for supported exchanges
@ -347,7 +350,7 @@ class TestCCXTExchange():
(rate['open'].min() != rate['open'].max()) (rate['open'].min() != rate['open'].max())
) )
def test_ccxt_fetch_mark_price_history(self, exchange_futures): def test_ccxt_fetch_mark_price_history(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures exchange, exchangename = exchange_futures
if not exchange: if not exchange:
# exchange_futures only returns values for supported exchanges # exchange_futures only returns values for supported exchanges
@ -371,7 +374,7 @@ class TestCCXTExchange():
assert mark_candles[mark_candles['date'] == prev_hour].iloc[0]['open'] != 0.0 assert mark_candles[mark_candles['date'] == prev_hour].iloc[0]['open'] != 0.0
assert mark_candles[mark_candles['date'] == this_hour].iloc[0]['open'] != 0.0 assert mark_candles[mark_candles['date'] == this_hour].iloc[0]['open'] != 0.0
def test_ccxt__calculate_funding_fees(self, exchange_futures): def test_ccxt__calculate_funding_fees(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures exchange, exchangename = exchange_futures
if not exchange: if not exchange:
# exchange_futures only returns values for supported exchanges # exchange_futures only returns values for supported exchanges
@ -387,16 +390,16 @@ class TestCCXTExchange():
# TODO: tests fetch_trades (?) # TODO: tests fetch_trades (?)
def test_ccxt_get_fee(self, exchange): def test_ccxt_get_fee(self, exchange: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair'] pair = EXCHANGES[exchangename]['pair']
threshold = 0.01 threshold = 0.01
assert 0 < exchange.get_fee(pair, 'limit', 'buy') < threshold assert 0 < exch.get_fee(pair, 'limit', 'buy') < threshold
assert 0 < exchange.get_fee(pair, 'limit', 'sell') < threshold assert 0 < exch.get_fee(pair, 'limit', 'sell') < threshold
assert 0 < exchange.get_fee(pair, 'market', 'buy') < threshold assert 0 < exch.get_fee(pair, 'market', 'buy') < threshold
assert 0 < exchange.get_fee(pair, 'market', 'sell') < threshold assert 0 < exch.get_fee(pair, 'market', 'sell') < threshold
def test_ccxt_get_max_leverage_spot(self, exchange): def test_ccxt_get_max_leverage_spot(self, exchange: EXCHANGE_FIXTURE_TYPE):
spot, spot_name = exchange spot, spot_name = exchange
if spot: if spot:
leverage_in_market_spot = EXCHANGES[spot_name].get('leverage_in_spot_market') leverage_in_market_spot = EXCHANGES[spot_name].get('leverage_in_spot_market')
@ -406,7 +409,7 @@ class TestCCXTExchange():
assert (isinstance(spot_leverage, float) or isinstance(spot_leverage, int)) assert (isinstance(spot_leverage, float) or isinstance(spot_leverage, int))
assert spot_leverage >= 1.0 assert spot_leverage >= 1.0
def test_ccxt_get_max_leverage_futures(self, exchange_futures): def test_ccxt_get_max_leverage_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures futures, futures_name = exchange_futures
if futures: if futures:
leverage_tiers_public = EXCHANGES[futures_name].get('leverage_tiers_public') leverage_tiers_public = EXCHANGES[futures_name].get('leverage_tiers_public')
@ -419,7 +422,7 @@ class TestCCXTExchange():
assert (isinstance(futures_leverage, float) or isinstance(futures_leverage, int)) assert (isinstance(futures_leverage, float) or isinstance(futures_leverage, int))
assert futures_leverage >= 1.0 assert futures_leverage >= 1.0
def test_ccxt_get_contract_size(self, exchange_futures): def test_ccxt_get_contract_size(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures futures, futures_name = exchange_futures
if futures: if futures:
futures_pair = EXCHANGES[futures_name].get( futures_pair = EXCHANGES[futures_name].get(
@ -430,7 +433,7 @@ class TestCCXTExchange():
assert (isinstance(contract_size, float) or isinstance(contract_size, int)) assert (isinstance(contract_size, float) or isinstance(contract_size, int))
assert contract_size >= 0.0 assert contract_size >= 0.0
def test_ccxt_load_leverage_tiers(self, exchange_futures): def test_ccxt_load_leverage_tiers(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures futures, futures_name = exchange_futures
if futures and EXCHANGES[futures_name].get('leverage_tiers_public'): if futures and EXCHANGES[futures_name].get('leverage_tiers_public'):
leverage_tiers = futures.load_leverage_tiers() leverage_tiers = futures.load_leverage_tiers()
@ -463,7 +466,7 @@ class TestCCXTExchange():
oldminNotional = tier['minNotional'] oldminNotional = tier['minNotional']
oldmaxNotional = tier['maxNotional'] oldmaxNotional = tier['maxNotional']
def test_ccxt_dry_run_liquidation_price(self, exchange_futures): def test_ccxt_dry_run_liquidation_price(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures futures, futures_name = exchange_futures
if futures and EXCHANGES[futures_name].get('leverage_tiers_public'): if futures and EXCHANGES[futures_name].get('leverage_tiers_public'):
@ -494,7 +497,7 @@ class TestCCXTExchange():
assert (isinstance(liquidation_price, float)) assert (isinstance(liquidation_price, float))
assert liquidation_price >= 0.0 assert liquidation_price >= 0.0
def test_ccxt_get_max_pair_stake_amount(self, exchange_futures): def test_ccxt_get_max_pair_stake_amount(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures futures, futures_name = exchange_futures
if futures: if futures:
futures_pair = EXCHANGES[futures_name].get( futures_pair = EXCHANGES[futures_name].get(

View File

@ -1,5 +1,6 @@
import pytest import pytest
from freqtrade.exceptions import OperationalException
from freqtrade.leverage import interest from freqtrade.leverage import interest
from freqtrade.util import FtPrecise from freqtrade.util import FtPrecise
@ -29,3 +30,13 @@ def test_interest(exchange, interest_rate, hours, expected):
rate=FtPrecise(interest_rate), rate=FtPrecise(interest_rate),
hours=hours hours=hours
))) == expected ))) == expected
def test_interest_exception():
with pytest.raises(OperationalException, match=r"Leverage not available on .* with freqtrade"):
interest(
exchange_name='bitmex',
borrowed=FtPrecise(60.0),
rate=FtPrecise(0.0005),
hours=ten_mins
)