stable/freqtrade/optimize/optimize_reports.py

479 lines
21 KiB
Python
Raw Normal View History

import logging
2020-06-26 18:08:45 +00:00
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List, Union
2020-06-09 06:00:35 +00:00
from arrow import Arrow
from numpy import int64
2020-09-28 17:39:41 +00:00
from pandas import DataFrame
from tabulate import tabulate
2020-06-28 07:27:19 +00:00
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
2020-09-28 17:39:41 +00:00
from freqtrade.data.btanalysis import calculate_market_change, calculate_max_drawdown
from freqtrade.misc import file_dump_json
2020-09-28 17:39:41 +00:00
logger = logging.getLogger(__name__)
2020-06-26 05:46:59 +00:00
def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> None:
2020-08-18 14:15:24 +00:00
"""
Stores backtest results
:param recordfilename: Path object, which can either be a filename or a directory.
Filenames will be appended with a timestamp right before the suffix
while for diectories, <directory>/backtest-result-<datetime>.json will be used as filename
:param stats: Dataframe containing the backtesting statistics
"""
if recordfilename.is_dir():
2020-07-03 18:32:04 +00:00
filename = (recordfilename /
f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json')
else:
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
).with_suffix(recordfilename.suffix)
2020-06-26 05:46:59 +00:00
file_dump_json(filename, stats)
latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
2020-06-26 05:46:59 +00:00
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
2020-05-25 17:18:53 +00:00
def _get_line_floatfmt() -> List[str]:
"""
Generate floatformat (goes in line with _generate_result_line())
"""
return ['s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', 'd', 'd', 'd']
def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
"""
Generate header lines (goes in line with _generate_result_line())
"""
return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Wins', 'Draws', 'Losses']
2020-05-25 17:18:53 +00:00
def _generate_result_line(result: DataFrame, max_open_trades: int, first_column: str) -> Dict:
"""
Generate one result dict, with "first_column" as key.
"""
profit_sum = result['profit_percent'].sum()
profit_total = profit_sum / max_open_trades
2020-05-25 17:18:53 +00:00
return {
'key': first_column,
'trades': len(result),
2020-06-26 04:56:59 +00:00
'profit_mean': result['profit_percent'].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_percent'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100.0, 2),
'profit_total_abs': result['profit_abs'].sum(),
'profit_total': profit_total,
'profit_total_pct': round(profit_total * 100.0, 2),
2020-05-25 17:18:53 +00:00
'duration_avg': str(timedelta(
minutes=round(result['trade_duration'].mean()))
2020-05-25 17:18:53 +00:00
) if not result.empty else '0:00',
# 'duration_max': str(timedelta(
# minutes=round(result['trade_duration'].max()))
2020-05-25 17:18:53 +00:00
# ) if not result.empty else '0:00',
# 'duration_min': str(timedelta(
# minutes=round(result['trade_duration'].min()))
2020-05-25 17:18:53 +00:00
# ) if not result.empty else '0:00',
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
'losses': len(result[result['profit_abs'] < 0]),
2020-05-25 17:18:53 +00:00
}
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
2020-05-25 17:50:09 +00:00
results: DataFrame, skip_nan: bool = False) -> List[Dict]:
"""
Generates and returns a list for the given backtest data and the results dataframe
2020-01-02 08:37:54 +00:00
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: List of Dicts containing the metrics per pair
"""
tabular_data = []
2020-05-25 17:18:53 +00:00
for pair in data:
result = results[results['pair'] == pair]
if skip_nan and result['profit_abs'].isnull().all():
continue
tabular_data.append(_generate_result_line(result, max_open_trades, pair))
# Append Total
tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
2020-05-25 17:18:53 +00:00
return tabular_data
def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
"""
Generate small table outlining Backtest results
:param max_open_trades: Max_open_trades parameter
:param results: Dataframe containing the backtest result for one strategy
:return: List of Dicts containing the metrics per Sell reason
"""
tabular_data = []
for reason, count in results['sell_reason'].value_counts().iteritems():
2020-01-09 05:46:39 +00:00
result = results.loc[results['sell_reason'] == reason]
profit_mean = result['profit_percent'].mean()
profit_sum = result['profit_percent'].sum()
profit_total = profit_sum / max_open_trades
tabular_data.append(
{
'sell_reason': reason.value,
'trades': count,
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
'losses': len(result[result['profit_abs'] < 0]),
'profit_mean': profit_mean,
'profit_mean_pct': round(profit_mean * 100, 2),
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100, 2),
'profit_total_abs': result['profit_abs'].sum(),
'profit_total': profit_total,
'profit_total_pct': round(profit_total * 100, 2),
}
)
return tabular_data
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: List of Dicts containing the metrics per Strategy
"""
tabular_data = []
for strategy, results in all_results.items():
tabular_data.append(_generate_result_line(
results['results'], results['config']['max_open_trades'], strategy)
)
2020-05-25 17:18:53 +00:00
return tabular_data
def generate_edge_table(results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
tabular_data = []
headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
'Average Duration (min)']
for result in results.items():
if result[1].nb_trades > 0:
tabular_data.append([
result[0],
result[1].stoploss,
result[1].winrate,
result[1].risk_reward_ratio,
result[1].required_risk_reward,
result[1].expectancy,
result[1].nb_trades,
round(result[1].avg_trade_duration)
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
2020-07-03 17:45:45 +00:00
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
if len(results) == 0:
return {
'backtest_best_day': 0,
'backtest_worst_day': 0,
'winning_days': 0,
'draw_days': 0,
'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
2020-07-03 17:45:45 +00:00
daily_profit = results.resample('1d', on='close_date')['profit_percent'].sum()
worst = min(daily_profit)
best = max(daily_profit)
winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
2020-07-03 17:58:02 +00:00
winning_trades = results.loc[results['profit_percent'] > 0]
losing_trades = results.loc[results['profit_percent'] < 0]
2020-07-03 17:45:45 +00:00
return {
'backtest_best_day': best,
'backtest_worst_day': worst,
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
2020-07-03 17:58:02 +00:00
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
2020-07-03 17:58:02 +00:00
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
2020-07-03 17:45:45 +00:00
}
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
2020-06-09 06:00:35 +00:00
min_date: Arrow, max_date: Arrow
) -> Dict[str, Any]:
2020-06-07 12:32:01 +00:00
"""
:param btdata: Backtest data
:param all_results: backtest result - dictionary in the form:
{ Strategy: {'results: results, 'config: config}}.
2020-06-09 06:00:35 +00:00
:param min_date: Backtest start date
:param max_date: Backtest end date
2020-06-07 12:32:01 +00:00
:return:
Dictionary containing results per strategy and a stratgy summary.
"""
2020-06-01 07:24:27 +00:00
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, content in all_results.items():
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
continue
config = content['config']
max_open_trades = config['max_open_trades']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
2020-05-25 17:50:09 +00:00
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
2020-05-25 18:22:22 +00:00
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
2020-05-25 18:22:22 +00:00
results=results.loc[results['open_at_end']],
skip_nan=True)
2020-07-03 17:45:45 +00:00
daily_stats = generate_daily_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
2020-06-09 06:00:35 +00:00
backtest_days = (max_date - min_date).days
strat_stats = {
2020-06-26 05:46:59 +00:00
'trades': results.to_dict(orient='records'),
'best_pair': best_pair,
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
2020-06-09 06:00:35 +00:00
'total_trades': len(results),
'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
2020-08-18 14:59:24 +00:00
'profit_total': results['profit_percent'].sum(),
'profit_total_abs': results['profit_abs'].sum(),
2020-06-09 06:00:35 +00:00
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.int_timestamp * 1000,
2020-06-09 06:00:35 +00:00
'backtest_end': max_date.datetime,
'backtest_end_ts': max_date.int_timestamp * 1000,
2020-06-09 06:00:35 +00:00
'backtest_days': backtest_days,
2020-07-03 17:30:29 +00:00
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
2020-06-28 07:04:19 +00:00
'pairlist': list(btdata.keys()),
2020-07-03 17:45:45 +00:00
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'max_open_trades': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
2020-08-08 18:20:58 +00:00
'timeframe': config['timeframe'],
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
2020-07-03 17:45:45 +00:00
**daily_stats,
}
result['strategy'][strategy] = strat_stats
2020-06-08 04:38:29 +00:00
try:
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
results, value_col='profit_percent')
strat_stats.update({
'max_drawdown': max_drawdown,
'drawdown_start': drawdown_start,
2020-07-26 13:23:21 +00:00
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
2020-06-08 04:38:29 +00:00
'drawdown_end': drawdown_end,
2020-07-26 13:23:21 +00:00
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
2020-06-08 04:38:29 +00:00
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
2020-06-26 18:08:45 +00:00
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
2020-06-08 04:38:29 +00:00
})
strategy_results = generate_strategy_metrics(all_results=all_results)
result['strategy_comparison'] = strategy_results
return result
2020-06-07 09:29:14 +00:00
###
# Start output section
###
2020-06-07 09:35:02 +00:00
def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
2020-06-07 09:29:14 +00:00
"""
Generates and returns a text table for the given backtest data and the results dataframe
:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
:param stake_currency: stake-currency - used to correctly name headers
:return: pretty printed table with tabulate as string
"""
headers = _get_line_header('Pair', stake_currency)
floatfmt = _get_line_floatfmt()
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
] for t in pair_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
2020-06-07 09:29:14 +00:00
"""
Generate small table outlining Backtest results
:param sell_reason_stats: Sell reason metrics
:param stake_currency: Stakecurrency used
:return: pretty printed table with tabulate as string
"""
headers = [
'Sell Reason',
'Sells',
'Wins',
'Draws',
'Losses',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
'Tot Profit %',
]
output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
2020-07-26 12:39:00 +00:00
t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'],
2020-06-07 09:29:14 +00:00
] for t in sell_reason_stats]
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
def text_table_strategy(strategy_results, stake_currency: str) -> str:
2020-06-07 09:29:14 +00:00
"""
Generate summary table per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: pretty printed table with tabulate as string
"""
floatfmt = _get_line_floatfmt()
headers = _get_line_header('Strategy', stake_currency)
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
] for t in strategy_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
2020-06-26 07:22:50 +00:00
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
2020-11-28 16:45:56 +00:00
best_trade = max(strat_results['trades'], key=lambda x: x['profit_percent'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_percent'])
2020-06-08 04:38:29 +00:00
metrics = [
2020-07-14 17:34:01 +00:00
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
2020-11-24 05:57:11 +00:00
('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability
2020-06-26 07:22:50 +00:00
('Total trades', strat_results['total_trades']),
2020-08-18 14:59:24 +00:00
('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
2020-06-26 07:22:50 +00:00
('Trades per day', strat_results['trades_per_day']),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} - "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
('Worst Pair', f"{strat_results['worst_pair']['key']} - "
f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
2020-11-28 16:45:56 +00:00
('Best trade', f"{best_trade['pair']} {round(best_trade['profit_percent'] * 100, 2)}%"),
('Worst trade', f"{worst_trade['pair']} {round(worst_trade['profit_percent'] * 100, 2)}%"),
2020-07-03 17:30:29 +00:00
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
2020-07-03 17:45:45 +00:00
('Days win/draw/lose', f"{strat_results['winning_days']} / "
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
2020-07-03 17:58:02 +00:00
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
2020-06-09 06:00:35 +00:00
('', ''), # Empty line to improve readability
2020-06-26 07:22:50 +00:00
('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
2020-06-08 04:38:29 +00:00
]
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
else:
return ''
2020-06-08 04:37:30 +00:00
def show_backtest_results(config: Dict, backtest_stats: Dict):
stake_currency = config['stake_currency']
for strategy, results in backtest_stats['strategy'].items():
2020-05-25 18:22:22 +00:00
# Print results
print(f"Result for strategy {strategy}")
2020-06-07 09:35:02 +00:00
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
2020-06-26 04:47:04 +00:00
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
2020-06-07 09:35:02 +00:00
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
2020-06-26 04:47:04 +00:00
if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
2020-06-08 04:37:30 +00:00
table = text_table_add_metrics(results)
2020-06-26 04:47:04 +00:00
if isinstance(table, str) and len(table) > 0:
2020-06-08 04:37:30 +00:00
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
2020-06-26 04:47:04 +00:00
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()
if len(backtest_stats['strategy']) > 1:
# Print Strategy summary table
table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency)
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
print(table)
print('=' * len(table.splitlines()[0]))
print('\nFor more details, please look at the detail tables above')