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merge 2 expectancy functions

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pull/8943/head
Stefano Ariestasia 3 years ago
parent c6ee8fcf54
commit 40d7d05e4e
6 changed files with 37 additions and 62 deletions
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4
docs/backtesting.md
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@ -305,7 +305,7 @@ A backtesting result will look like that:
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 |
| Expectancy | -0.15 |
| Expectancy (Ratio) | -0.15 (-0.05) |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
@ -409,7 +409,7 @@ It contains some useful key metrics about performance of your strategy on backte
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 |
| Expectancy | -0.15 |
| Expectancy (Ratio) | -0.15 (-0.05) |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |

33
freqtrade/data/metrics.py
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@ -201,23 +201,28 @@ def calculate_expectancy(trades: pd.DataFrame) -> float:
:return: expectancy
"""
if len(trades) == 0:
return 0
expectancy = 0
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)
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
winrate = (nb_win_trades / len(trades))
loserate = (nb_loss_trades / len(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
winrate = (nb_win_trades / len(trades))
loserate = (nb_loss_trades / len(trades))
expectancy = (winrate * average_win) - (loserate * average_loss)
expectancy = (winrate * average_win) - (loserate * average_loss)
if (average_loss > 0):
risk_reward_ratio = average_win / average_loss
expectancy_ratio = ((1 + risk_reward_ratio) * winrate) - 1
return expectancy
return expectancy, expectancy_ratio
def calculate_expectancy_ratio(trades: pd.DataFrame) -> float:

4
freqtrade/optimize/optimize_reports/bt_output.py
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@ -233,8 +233,8 @@ 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 Ratio', f"{strat_results['expectancy_ratio']:.2f}" if 'expectancy_ratio'
in strat_results else 'N/A'),
('Expectancy (Ratio)', f"{strat_results['expectancy']:.2f} "
f"({strat_results['expectancy_ratio']:.2f})"),
('Trades per day', strat_results['trades_per_day']),
('Avg. daily profit %',
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),

6
freqtrade/optimize/optimize_reports/optimize_reports.py
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@ -7,7 +7,7 @@ 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_ratio, calculate_market_change,
calculate_expectancy, calculate_market_change,
calculate_max_drawdown, calculate_sharpe, calculate_sortino)
from freqtrade.misc import decimals_per_coin, round_coin_value
@ -389,6 +389,7 @@ def generate_strategy_stats(pairlist: List[str],
losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum()
profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
expectancy, expectancy_ratio = calculate_expectancy(results)
backtest_days = (max_date - min_date).days or 1
strat_stats = {
'trades': results.to_dict(orient='records'),
@ -414,7 +415,8 @@ 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_ratio': calculate_expectancy_ratio(results),
'expectancy': expectancy,
'expectancy_ratio': expectancy_ratio,
'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),

27
freqtrade/rpc/rpc.py
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@ -18,8 +18,7 @@ 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_expectancy, calculate_expectancy_ratio,
calculate_max_drawdown)
from freqtrade.data.metrics import calculate_expectancy, calculate_max_drawdown
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirection, SignalDirection,
State, TradingMode)
from freqtrade.exceptions import ExchangeError, PricingError
@ -526,17 +525,11 @@ class RPC:
winrate = (winning_trades / closed_trade_count) if closed_trade_count > 0 else 0
# expectancy, expectancy_ratio = self.__calc_expectancy(mean_winning_profit,
# mean_losing_profit,
# winrate,
# loserate)
trades_df = DataFrame([{'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT),
'profit_abs': trade.close_profit_abs}
for trade in trades if not trade.is_open and trade.close_date])
expectancy = calculate_expectancy(trades_df)
expectancy_ratio = calculate_expectancy_ratio(trades_df)
expectancy, expectancy_ratio = calculate_expectancy(trades_df)
max_drawdown_abs = 0.0
max_drawdown = 0.0
@ -626,22 +619,6 @@ class RPC:
return est_stake, est_bot_stake
def __calc_expectancy(
self, mean_winning_profit: float, mean_losing_profit: float,
winrate: float, loserate: float) -> Tuple[float, float]:
expectancy = (
(winrate * mean_winning_profit) -
(loserate * mean_losing_profit)
)
expectancy_ratio = float('inf')
if mean_losing_profit > 0:
expectancy_ratio = (
((1 + (mean_winning_profit / mean_losing_profit)) * winrate) - 1
)
return expectancy, expectancy_ratio
def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
""" Returns current account balance per crypto """

25
tests/data/test_btanalysis.py
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@ -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_expectancy_ratio,
calculate_market_change, calculate_max_drawdown,
calculate_sharpe, calculate_sortino, calculate_underwater,
combine_dataframes_with_mean, create_cum_profit)
calculate_expectancy, 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
@ -343,23 +343,14 @@ def test_calculate_expectancy(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
expectancy = calculate_expectancy(DataFrame())
expectancy, expectancy_ratio = calculate_expectancy(DataFrame())
assert expectancy == 0.0
expectancy = calculate_expectancy(bt_data)
assert isinstance(expectancy, float)
assert pytest.approx(expectancy) == 5.820687070932315e-06
def test_calculate_expectancy_ratio(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
expectancy_ratio = calculate_expectancy_ratio(DataFrame())
assert expectancy_ratio == float('inf')
expectancy_ratio = calculate_expectancy_ratio(bt_data)
expectancy, expectancy_ratio = calculate_expectancy(bt_data)
assert isinstance(expectancy, float)
assert isinstance(expectancy_ratio, float)
assert pytest.approx(expectancy) == 5.820687070932315e-06
assert pytest.approx(expectancy_ratio) == 0.07151374226574791

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