Update drawdown calculation to account drawdown

This commit is contained in:
Matthias 2022-01-04 15:57:58 +01:00
parent 42579c0268
commit 7a2b50ce8b
4 changed files with 29 additions and 17 deletions

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@ -312,7 +312,7 @@ A backtesting result will look like that:
| | | | | |
| Min balance | 0.00945123 BTC | | Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC | | Max balance | 0.01846651 BTC |
| Drawdown | 50.63% | | Drawdown (Account) | 13.33% |
| Drawdown | 0.0015 BTC | | Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC | | Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC | | Drawdown low | -0.0002 BTC |
@ -399,7 +399,7 @@ It contains some useful key metrics about performance of your strategy on backte
| | | | | |
| Min balance | 0.00945123 BTC | | Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC | | Max balance | 0.01846651 BTC |
| Drawdown | 50.63% | | Drawdown (Account) | 13.33% |
| Drawdown | 0.0015 BTC | | Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC | | Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC | | Drawdown low | -0.0002 BTC |
@ -426,7 +426,8 @@ It contains some useful key metrics about performance of your strategy on backte
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades. - `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached. - `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period. - `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced). - `Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as $(Absolute Drawdown) / (DrawdownHigh + startingBalance)$.
- `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Low.
- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost. - `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
- `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command). - `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column. - `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.

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@ -392,15 +392,17 @@ def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date', def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio' value_col: str = 'profit_abs', starting_balance: float = 0
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]: ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
""" """
Calculate max drawdown and the corresponding close dates Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio) :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date') :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio') :param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown, :param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
high and low time and high and low value. :return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
with absolute max drawdown, high and low time and high and low value,
and the relative account drawdown
:raise: ValueError if trade-dataframe was found empty. :raise: ValueError if trade-dataframe was found empty.
""" """
if len(trades) == 0: if len(trades) == 0:
@ -416,7 +418,17 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin] high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
['high_value'].idxmax(), 'cumulative'] ['high_value'].idxmax(), 'cumulative']
low_val = max_drawdown_df.loc[idxmin, 'cumulative'] low_val = max_drawdown_df.loc[idxmin, 'cumulative']
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
return (
abs(min(max_drawdown_df['drawdown'])),
high_date,
low_date,
high_val,
low_val,
max_drawdown_rel
)
def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]: def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:

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@ -462,12 +462,11 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
} }
try: try:
max_drawdown, _, _, _, _ = calculate_max_drawdown( (drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
results, value_col='profit_ratio') max_drawdown) = calculate_max_drawdown(
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown( results, value_col='profit_abs', starting_balance=starting_balance)
results, value_col='profit_abs')
strat_stats.update({ strat_stats.update({
'max_drawdown': max_drawdown, 'max_drawdown_account': max_drawdown,
'max_drawdown_abs': drawdown_abs, 'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT), 'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
'drawdown_start_ts': drawdown_start.timestamp() * 1000, 'drawdown_start_ts': drawdown_start.timestamp() * 1000,
@ -486,7 +485,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
except ValueError: except ValueError:
strat_stats.update({ strat_stats.update({
'max_drawdown': 0.0, 'max_drawdown_account': 0.0,
'max_drawdown_abs': 0.0, 'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0, 'max_drawdown_low': 0.0,
'max_drawdown_high': 0.0, 'max_drawdown_high': 0.0,
@ -716,7 +715,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Max balance', round_coin_value(strat_results['csum_max'], ('Max balance', round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])), strat_results['stake_currency'])),
('Drawdown', f"{strat_results['max_drawdown']:.2%}"), ('Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}"),
('Drawdown', round_coin_value(strat_results['max_drawdown_abs'], ('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])), strat_results['stake_currency'])),
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'], ('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],

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@ -161,7 +161,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
Add scatter points indicating max drawdown Add scatter points indicating max drawdown
""" """
try: try:
max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades) _, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown(trades)
drawdown = go.Scatter( drawdown = go.Scatter(
x=[highdate, lowdate], x=[highdate, lowdate],