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modify expectancy and expectancy ratio

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This commit is contained in:
Stefano Ariestasia 2023-07-22 08:36:51 +09:00
parent 6874123974
commit c048e7229a
5 changed files with 57 additions and 28 deletions
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53
freqtrade/data/metrics.py
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@ -203,21 +203,48 @@ def calculate_expectancy(trades: pd.DataFrame) -> float:
if len(trades) == 0:
return 0
expectancy = 1
expectancy = 0
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])
winning_trades = trades.loc[trades['profit_abs'] > 0]
losing_trades = trades.loc[trades['profit_abs'] < 0]
profit_sum = winning_trades['profit_abs'].sum()
loss_sum = abs(losing_trades['profit_abs'].sum())
nb_win_trades = len(winning_trades)
nb_loss_trades = len(losing_trades)
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
average_win = profit_sum / nb_win_trades
average_loss = loss_sum / nb_loss_trades
winrate = nb_win_trades / len(trades)
loserate = nb_loss_trades / len(trades)
expectancy = (winrate * average_win) - (loserate * average_loss)
return expectancy
def calculate_expectancy_ratio(trades: pd.DataFrame) -> float:
"""
Calculate expectancy ratio
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
:return: expectancy ratio
"""
expectancy_ratio = float('inf')
if len(trades) > 0:
winning_trades = trades.loc[trades['profit_abs'] > 0]
losing_trades = trades.loc[trades['profit_abs'] < 0]
profit_sum = winning_trades['profit_abs'].sum()
loss_sum = abs(losing_trades['profit_abs'].sum())
nb_win_trades = len(winning_trades)
nb_loss_trades = len(losing_trades)
average_win = (profit_sum / nb_win_trades) if nb_win_trades > 0 else 0
average_loss = (loss_sum / nb_loss_trades) if nb_loss_trades > 0 else 0
if (average_loss > 0):
risk_reward_ratio = average_win / average_loss
winrate = nb_win_trades / len(trades)
expectancy = ((1 + risk_reward_ratio) * winrate) - 1
return expectancy

2
freqtrade/optimize/optimize_reports/bt_output.py
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@ -233,7 +233,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('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'
in strat_results else 'N/A'),
('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
('Expectancy Ratio', f"{strat_results['expectancy_ratio']:.2f}" if 'expectancy_ratio'
in strat_results else 'N/A'),
('Trades per day', strat_results['trades_per_day']),
('Avg. daily profit %',

7
freqtrade/optimize/optimize_reports/optimize_reports.py
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@ -7,8 +7,9 @@ from pandas import DataFrame, concat, to_datetime
from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, IntOrInf
from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
calculate_expectancy, calculate_market_change,
calculate_max_drawdown, calculate_sharpe, calculate_sortino)
calculate_expectancy, calculate_expectancy_ratio,
calculate_market_change, calculate_max_drawdown,
calculate_sharpe, calculate_sortino)
from freqtrade.misc import decimals_per_coin, round_coin_value
@ -414,7 +415,7 @@ def generate_strategy_stats(pairlist: List[str],
'profit_total_long_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']),
'expectancy': calculate_expectancy(results),
'expectancy_ratio': calculate_expectancy_ratio(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),

3
freqtrade/rpc/rpc.py
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@ -18,7 +18,8 @@ from freqtrade import __version__
from freqtrade.configuration.timerange import TimeRange
from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT, Config
from freqtrade.data.history import load_data
from freqtrade.data.metrics import calculate_max_drawdown
from freqtrade.data.metrics import (calculate_expectancy, calculate_expectancy_ratio,
calculate_max_drawdown)
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirection, SignalDirection,
State, TradingMode)
from freqtrade.exceptions import ExchangeError, PricingError

20
tests/data/test_btanalysis.py
View File

@ -13,10 +13,10 @@ from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelis
load_backtest_metadata, load_trades, load_trades_from_db)
from freqtrade.data.history import load_data, load_pair_history
from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
calculate_expectancy, calculate_market_change,
calculate_max_drawdown, calculate_sharpe, calculate_sortino,
calculate_underwater, combine_dataframes_with_mean,
create_cum_profit)
calculate_expectancy, calculate_expectancy_ratio,
calculate_market_change, calculate_max_drawdown,
calculate_sharpe, calculate_sortino, calculate_underwater,
combine_dataframes_with_mean, create_cum_profit)
from freqtrade.exceptions import OperationalException
from freqtrade.util import dt_utc
from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
@ -339,16 +339,16 @@ def test_calculate_csum(testdatadir):
csum_min, csum_max = calculate_csum(DataFrame())
def test_calculate_expectancy(testdatadir):
def test_calculate_expectancy_ratio(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
expectancy = calculate_expectancy(DataFrame())
assert expectancy == 0.0
expectancy_ratio = calculate_expectancy_ratio(DataFrame())
assert expectancy_ratio == 0.0
expectancy = calculate_expectancy(bt_data)
assert isinstance(expectancy, float)
assert pytest.approx(expectancy) == 0.07151374226574791
expectancy_ratio = calculate_expectancy_ratio(bt_data)
assert isinstance(expectancy_ratio, float)
assert pytest.approx(expectancy_ratio) == 0.07151374226574791
def test_calculate_sortino(testdatadir):