def test_calculate_max_drawdown2():
values = [
0.011580, 0.010048, 0.011340, 0.012161, 0.010416, 0.010009, 0.020024,
-0.024662, -0.022350, 0.020496, -0.029859, -0.030511, 0.010041,
0.010872, -0.025782, 0.010400, 0.012374, 0.012467, 0.114741, 0.010303,
0.010088, -0.033961, 0.010680, 0.010886, -0.029274, 0.011178, 0.010693,
0.010711
]
dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))]
df = DataFrame(zip(values, dates), columns=['profit', 'open_date'])
# sort by profit and reset index
df = df.sort_values('profit').reset_index(drop=True)
df1 = df.copy()
drawdown, hdate, ldate, hval, lval = calculate_max_drawdown(
df, date_col='open_date', value_col='profit')
# Ensure df has not been altered.
assert df.equals(df1)
assert isinstance(drawdown, float)
# High must be before low
assert hdate < ldate
# High value must be higher than low value
assert hval > lval
assert drawdown == 0.091755
df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])
with pytest.raises(ValueError,
match='No losing trade, therefore no drawdown.'):
calculate_max_drawdown(df, date_col='open_date', value_col='profit')
def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param all_results: Dict of <Strategyname: DataFrame> 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']['dry_run_wallet'],
strategy))
try:
max_drawdown_per, _, _, _, _ = calculate_max_drawdown(
results['results'], value_col='profit_ratio')
max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(
results['results'], value_col='profit_abs')
except ValueError:
max_drawdown_per = 0
max_drawdown_abs = 0
tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
tabular_data[-1]['max_drawdown_abs'] = \
round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
return tabular_data
def test_calculate_max_drawdown(testdatadir):
filename = testdatadir / "backtest-result_test.json"
bt_data = load_backtest_data(filename)
drawdown, h, low = calculate_max_drawdown(bt_data)
assert isinstance(drawdown, float)
assert pytest.approx(drawdown) == 0.21142322
assert isinstance(h, Timestamp)
assert isinstance(low, Timestamp)
assert h == Timestamp('2018-01-24 14:25:00', tz='UTC')
assert low == Timestamp('2018-01-30 04:45:00', tz='UTC')
with pytest.raises(ValueError, match='Trade dataframe empty.'):
drawdown, h, low = calculate_max_drawdown(DataFrame())
def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
timeframe: str) -> make_subplots:
"""
Add scatter points indicating max drawdown
"""
try:
max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
drawdown = go.Scatter(
x=[highdate, lowdate],
y=[
df_comb.loc[timeframe_to_prev_date(timeframe, highdate),
'cum_profit'],
df_comb.loc[timeframe_to_prev_date(timeframe, lowdate),
'cum_profit'],
],
mode='markers',
name=f"Max drawdown {max_drawdown:.2%}",
text=f"Max drawdown {max_drawdown:.2%}",
marker=dict(symbol='square-open',
size=9,
line=dict(width=2),
color='green'))
fig.add_trace(drawdown, row, 1)
except ValueError:
logger.warning("No trades found - not plotting max drawdown.")
return fig
def _max_drawdown(self, date_now: datetime) -> ProtectionReturn:
"""
Evaluate recent trades for drawdown ...
"""
look_back_until = date_now - timedelta(minutes=self._lookback_period)
trades = Trade.get_trades_proxy(is_open=False, close_date=look_back_until)
trades_df = pd.DataFrame([trade.to_json() for trade in trades])
if len(trades) < self._trade_limit:
# Not enough trades in the relevant period
return False, None, None
# Drawdown is always positive
try:
drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
except ValueError:
return False, None, None
if drawdown > self._max_allowed_drawdown:
self.log_once(
f"Trading stopped due to Max Drawdown {drawdown:.2f} > {self._max_allowed_drawdown}"
f" within {self.lookback_period_str}.", logger.info)
until = self.calculate_lock_end(trades, self._stop_duration)
return True, until, self._reason(drawdown)
return False, None, None
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Dict, processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any], *args,
**kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Calmar Ratio calculation.
"""
total_profit = backtest_stats["profit_total"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period * 100
# calculate max drawdown
try:
_, _, _, high_val, low_val = calculate_max_drawdown(
results, value_col="profit_abs")
max_drawdown = (high_val - low_val) / high_val
except ValueError:
max_drawdown = 0
if max_drawdown != 0:
calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
else:
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
calmar_ratio = -20.0
# print(expected_returns_mean, max_drawdown, calmar_ratio)
return -calmar_ratio
def hyperopt_loss_function(results: DataFrame, trade_count: int, *args,
**kwargs) -> float:
total_profit = results["profit_abs"].sum()
try:
max_drawdown_abs = calculate_max_drawdown(
results, value_col="profit_abs")[5]
except ValueError:
max_drawdown_abs = 0
return -1 * (total_profit * (1 - max_drawdown_abs * DRAWDOWN_MULT))
def test_calculate_max_drawdown(testdatadir):
filename = testdatadir / "backtest-result_new.json"
bt_data = load_backtest_data(filename)
_, hdate, lowdate, hval, lval, drawdown = calculate_max_drawdown(
bt_data, value_col="profit_abs")
assert isinstance(drawdown, float)
assert pytest.approx(drawdown) == 0.12071099
assert isinstance(hdate, Timestamp)
assert isinstance(lowdate, Timestamp)
assert isinstance(hval, float)
assert isinstance(lval, float)
assert hdate == Timestamp('2018-01-25 01:30:00', tz='UTC')
assert lowdate == Timestamp('2018-01-25 03:50:00', tz='UTC')
underwater = calculate_underwater(bt_data)
assert isinstance(underwater, DataFrame)
with pytest.raises(ValueError, match='Trade dataframe empty.'):
calculate_max_drawdown(DataFrame())
with pytest.raises(ValueError, match='Trade dataframe empty.'):
calculate_underwater(DataFrame())
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function.
Uses profit ratio weighted max_drawdown when drawdown is available.
Otherwise directly optimizes profit ratio.
"""
total_profit = results['profit_abs'].sum()
try:
max_drawdown = calculate_max_drawdown(results, value_col='profit_abs')
except ValueError:
# No losing trade, therefore no drawdown.
return -total_profit
return -total_profit / max_drawdown[0]
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results.
"""
profit_threshold = 0
if IGNORE_SMALL_PROFITS:
profit_threshold = SMALL_PROFITS_THRESHOLD
total_profit = results['profit_ratio'].sum()
total_win = len(results[(results['profit_ratio'] > profit_threshold)])
total_lose = len(results[(results['profit_ratio'] <= 0)])
average_profit = results['profit_ratio'].mean() * 100
sortino_ratio = sortino_daily(results, trade_count, min_date, max_date)
trade_duration = results['trade_duration'].mean()
max_drawdown = 100
try:
max_drawdown, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
except:
pass
if total_lose == 0:
total_lose = 1
profit_loss = (1 - total_profit / EXPECTED_MAX_PROFIT) * TOTAL_PROFIT_WEIGHT
win_lose_loss = (1 - (total_win / total_lose)) * WIN_LOSS_WEIGHT
average_profit_loss = 1 - (min(average_profit, AVERAGE_PROFIT_THRESHOLD) * AVERAGE_PROFIT_WEIGHT)
sortino_ratio_loss = SORTINO_WEIGHT * sortino_ratio
drawdown_loss = max_drawdown * DRAWDOWN_WEIGHT
duration_loss = DURATION_WEIGHT * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = profit_loss + win_lose_loss + average_profit_loss + sortino_ratio_loss + drawdown_loss + duration_loss
return result
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
min_date: Arrow, max_date: Arrow
) -> Dict[str, Any]:
"""
:param btdata: Backtest data
:param all_results: backtest result - dictionary in the form:
{ Strategy: {'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
"""
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,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
results=results.loc[results['open_at_end']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = {
'trades': results.to_dict(orient='records'),
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_percent'].sum(),
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.int_timestamp * 1000,
'backtest_end': max_date.datetime,
'backtest_end_ts': max_date.int_timestamp * 1000,
'backtest_days': backtest_days,
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'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),
'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'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats
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,
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end,
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'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,
})
strategy_results = generate_strategy_metrics(all_results=all_results)
result['strategy_comparison'] = strategy_results
return result
def generate_strategy_stats(pairlist: List[str], strategy: str,
content: Dict[str, Any], min_date: datetime,
max_date: datetime,
market_change: float) -> Dict[str, Any]:
"""
:param pairlist: List of pairs to backtest
:param strategy: Strategy name
:param content: Backtest result data in the format:
{'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:param market_change: float indicating the market change
:return: Dictionary containing results per strategy and a strategy summary.
"""
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
return {}
config = content['config']
max_open_trades = min(config['max_open_trades'], len(pairlist))
start_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(pairlist,
stake_currency=stake_currency,
starting_balance=start_balance,
results=results,
skip_nan=False)
enter_tag_results = generate_tag_metrics("enter_tag",
starting_balance=start_balance,
results=results,
skip_nan=False)
exit_reason_stats = generate_exit_reason_stats(
max_open_trades=max_open_trades, results=results)
left_open_results = generate_pair_metrics(
pairlist,
stake_currency=stake_currency,
starting_balance=start_balance,
results=results.loc[results['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
trade_stats = generate_trading_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
if not results.empty:
results['open_timestamp'] = results['open_date'].view(int64) // 1e6
results['close_timestamp'] = results['close_date'].view(int64) // 1e6
backtest_days = (max_date - min_date).days or 1
strat_stats = {
'trades':
results.to_dict(orient='records'),
'locks': [lock.to_json() for lock in content['locks']],
'best_pair':
best_pair,
'worst_pair':
worst_pair,
'results_per_pair':
pair_results,
'results_per_enter_tag':
enter_tag_results,
'exit_reason_summary':
exit_reason_stats,
'left_open_trades':
left_open_results,
# 'days_breakdown_stats': days_breakdown_stats,
'total_trades':
len(results),
'trade_count_long':
len(results.loc[~results['is_short']]),
'trade_count_short':
len(results.loc[results['is_short']]),
'total_volume':
float(results['stake_amount'].sum()),
'avg_stake_amount':
results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean':
results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_median':
results['profit_ratio'].median() if len(results) > 0 else 0,
'profit_total':
results['profit_abs'].sum() / start_balance,
'profit_total_long':
results.loc[~results['is_short'], 'profit_abs'].sum() / start_balance,
'profit_total_short':
results.loc[results['is_short'], 'profit_abs'].sum() / start_balance,
'profit_total_abs':
results['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(),
'backtest_start':
min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts':
int(min_date.timestamp() * 1000),
'backtest_end':
max_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_end_ts':
int(max_date.timestamp() * 1000),
'backtest_days':
backtest_days,
'backtest_run_start_ts':
content['backtest_start_time'],
'backtest_run_end_ts':
content['backtest_end_time'],
'trades_per_day':
round(len(results) / backtest_days, 2),
'market_change':
market_change,
'pairlist':
pairlist,
'stake_amount':
config['stake_amount'],
'stake_currency':
config['stake_currency'],
'stake_currency_decimals':
decimals_per_coin(config['stake_currency']),
'starting_balance':
start_balance,
'dry_run_wallet':
start_balance,
'final_balance':
content['final_balance'],
'rejected_signals':
content['rejected_signals'],
'timedout_entry_orders':
content['timedout_entry_orders'],
'timedout_exit_orders':
content['timedout_exit_orders'],
'max_open_trades':
max_open_trades,
'max_open_trades_setting':
(config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe':
config['timeframe'],
'timeframe_detail':
config.get('timeframe_detail', ''),
'timerange':
config.get('timerange', ''),
'enable_protections':
config.get('enable_protections', False),
'strategy_name':
strategy,
# 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),
'use_custom_stoploss':
config.get('use_custom_stoploss', False),
'minimal_roi':
config['minimal_roi'],
'use_exit_signal':
config['use_exit_signal'],
'exit_profit_only':
config['exit_profit_only'],
'exit_profit_offset':
config['exit_profit_offset'],
'ignore_roi_if_entry_signal':
config['ignore_roi_if_entry_signal'],
**daily_stats,
**trade_stats
}
try:
max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
(drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
max_drawdown) = calculate_max_drawdown(results,
value_col='profit_abs',
starting_balance=start_balance)
strat_stats.update({
'max_drawdown':
max_drawdown_legacy, # Deprecated - do not use
'max_drawdown_account':
max_drawdown,
'max_drawdown_abs':
drawdown_abs,
'drawdown_start':
drawdown_start.strftime(DATETIME_PRINT_FORMAT),
'drawdown_start_ts':
drawdown_start.timestamp() * 1000,
'drawdown_end':
drawdown_end.strftime(DATETIME_PRINT_FORMAT),
'drawdown_end_ts':
drawdown_end.timestamp() * 1000,
'max_drawdown_low':
low_val,
'max_drawdown_high':
high_val,
})
csum_min, csum_max = calculate_csum(results, start_balance)
strat_stats.update({'csum_min': csum_min, 'csum_max': csum_max})
except ValueError:
strat_stats.update({
'max_drawdown':
0.0,
'max_drawdown_account':
0.0,
'max_drawdown_abs':
0.0,
'max_drawdown_low':
0.0,
'max_drawdown_high':
0.0,
'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,
'csum_min':
0,
'csum_max':
0
})
return strat_stats
def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame], min_date: Arrow,
max_date: Arrow) -> Dict[str, Any]:
"""
:param config: Configuration object used for backtest
:param btdata: Backtest data
:param all_results: backtest result - dictionary with { Strategy: results}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
"""
stake_currency = config['stake_currency']
max_open_trades = config['max_open_trades']
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, results in all_results.items():
pair_results = generate_pair_metrics(btdata,
stake_currency=stake_currency,
max_open_trades=max_open_trades,
results=results,
skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(
max_open_trades=max_open_trades, results=results)
left_open_results = generate_pair_metrics(
btdata,
stake_currency=stake_currency,
max_open_trades=max_open_trades,
results=results.loc[results['open_at_end']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = {
'trades':
results.to_dict(orient='records'),
'results_per_pair':
pair_results,
'sell_reason_summary':
sell_reason_stats,
'left_open_trades':
left_open_results,
'total_trades':
len(results),
'profit_mean':
results['profit_percent'].mean() if len(results) > 0 else 0,
'profit_total':
results['profit_percent'].sum(),
'profit_total_abs':
results['profit_abs'].sum(),
'backtest_start':
min_date.datetime,
'backtest_start_ts':
min_date.timestamp * 1000,
'backtest_end':
max_date.datetime,
'backtest_end_ts':
max_date.timestamp * 1000,
'backtest_days':
backtest_days,
'trades_per_day':
round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change':
market_change,
'pairlist':
list(btdata.keys()),
'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),
'timeframe':
config['timeframe'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats
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,
'drawdown_start_ts':
drawdown_start.timestamp() * 1000,
'drawdown_end':
drawdown_end,
'drawdown_end_ts':
drawdown_end.timestamp() * 1000,
})
except ValueError:
strat_stats.update({
'max_drawdown':
0.0,
'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,
})
strategy_results = generate_strategy_metrics(
stake_currency=stake_currency,
max_open_trades=max_open_trades,
all_results=all_results)
result['strategy_comparison'] = strategy_results
return result
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame,
Dict]]],
min_date: Arrow,
max_date: Arrow) -> Dict[str, Any]:
"""
:param btdata: Backtest data
:param all_results: backtest result - dictionary in the form:
{ Strategy: {'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
"""
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 = min(config['max_open_trades'], len(btdata.keys()))
starting_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata,
stake_currency=stake_currency,
starting_balance=starting_balance,
results=results,
skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(
max_open_trades=max_open_trades, results=results)
left_open_results = generate_pair_metrics(
btdata,
stake_currency=stake_currency,
starting_balance=starting_balance,
results=results.loc[results['is_open']],
skip_nan=True)
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
backtest_days = (max_date - min_date).days
strat_stats = {
'trades':
results.to_dict(orient='records'),
'locks': [lock.to_json() for lock in content['locks']],
'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,
'total_trades':
len(results),
'total_volume':
float(results['stake_amount'].sum()),
'avg_stake_amount':
results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean':
results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_total':
results['profit_abs'].sum() / starting_balance,
'profit_total_abs':
results['profit_abs'].sum(),
'backtest_start':
min_date.datetime,
'backtest_start_ts':
min_date.int_timestamp * 1000,
'backtest_end':
max_date.datetime,
'backtest_end_ts':
max_date.int_timestamp * 1000,
'backtest_days':
backtest_days,
'backtest_run_start_ts':
content['backtest_start_time'],
'backtest_run_end_ts':
content['backtest_end_time'],
'trades_per_day':
round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change':
market_change,
'pairlist':
list(btdata.keys()),
'stake_amount':
config['stake_amount'],
'stake_currency':
config['stake_currency'],
'stake_currency_decimals':
decimals_per_coin(config['stake_currency']),
'starting_balance':
starting_balance,
'dry_run_wallet':
starting_balance,
'final_balance':
content['final_balance'],
'max_open_trades':
max_open_trades,
'max_open_trades_setting':
(config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe':
config['timeframe'],
'timerange':
config.get('timerange', ''),
'enable_protections':
config.get('enable_protections', False),
'strategy_name':
strategy,
# 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),
'use_custom_stoploss':
config.get('use_custom_stoploss', False),
'minimal_roi':
config['minimal_roi'],
'use_sell_signal':
config['ask_strategy']['use_sell_signal'],
'sell_profit_only':
config['ask_strategy']['sell_profit_only'],
'sell_profit_offset':
config['ask_strategy']['sell_profit_offset'],
'ignore_roi_if_buy_signal':
config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats
try:
max_drawdown, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
results, value_col='profit_abs')
strat_stats.update({
'max_drawdown':
max_drawdown,
'max_drawdown_abs':
drawdown_abs,
'drawdown_start':
drawdown_start,
'drawdown_start_ts':
drawdown_start.timestamp() * 1000,
'drawdown_end':
drawdown_end,
'drawdown_end_ts':
drawdown_end.timestamp() * 1000,
'max_drawdown_low':
low_val,
'max_drawdown_high':
high_val,
})
csum_min, csum_max = calculate_csum(results, starting_balance)
strat_stats.update({'csum_min': csum_min, 'csum_max': csum_max})
except ValueError:
strat_stats.update({
'max_drawdown':
0.0,
'max_drawdown_abs':
0.0,
'max_drawdown_low':
0.0,
'max_drawdown_high':
0.0,
'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,
'csum_min':
0,
'csum_max':
0
})
strategy_results = generate_strategy_metrics(all_results=all_results)
result['strategy_comparison'] = strategy_results
return result
