diff --git a/docs/backtesting.md b/docs/backtesting.md index a0a304400..75225b654 100644 --- a/docs/backtesting.md +++ b/docs/backtesting.md @@ -287,45 +287,51 @@ A backtesting result will look like that: | ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 | | LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 | | TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 | -================ SUMMARY METRICS =============== -| Metric | Value | -|------------------------+---------------------| -| Backtesting from | 2019-01-01 00:00:00 | -| Backtesting to | 2019-05-01 00:00:00 | -| Max open trades | 3 | -| | | -| Total/Daily Avg Trades | 429 / 3.575 | -| Starting balance | 0.01000000 BTC | -| Final balance | 0.01762792 BTC | -| Absolute profit | 0.00762792 BTC | -| Total profit % | 76.2% | -| CAGR % | 460.87% | -| Trades per day | 3.575 | -| Avg. stake amount | 0.001 BTC | -| Total trade volume | 0.429 BTC | -| | | -| Best Pair | LSK/BTC 26.26% | -| Worst Pair | ZEC/BTC -10.18% | -| Best Trade | LSK/BTC 4.25% | -| Worst Trade | ZEC/BTC -10.25% | -| Best day | 0.00076 BTC | -| Worst day | -0.00036 BTC | -| Days win/draw/lose | 12 / 82 / 25 | -| Avg. Duration Winners | 4:23:00 | -| Avg. Duration Loser | 6:55:00 | -| Rejected Entry signals | 3089 | -| Entry/Exit Timeouts | 0 / 0 | -| | | -| Min balance | 0.00945123 BTC | -| Max balance | 0.01846651 BTC | -| Drawdown (Account) | 13.33% | -| Drawdown | 0.0015 BTC | -| Drawdown high | 0.0013 BTC | -| Drawdown low | -0.0002 BTC | -| Drawdown Start | 2019-02-15 14:10:00 | -| Drawdown End | 2019-04-11 18:15:00 | -| Market change | -5.88% | -=============================================== +================== SUMMARY METRICS ================== +| Metric | Value | +|-----------------------------+---------------------| +| Backtesting from | 2019-01-01 00:00:00 | +| Backtesting to | 2019-05-01 00:00:00 | +| Max open trades | 3 | +| | | +| Total/Daily Avg Trades | 429 / 3.575 | +| Starting balance | 0.01000000 BTC | +| Final balance | 0.01762792 BTC | +| Absolute profit | 0.00762792 BTC | +| Total profit % | 76.2% | +| CAGR % | 460.87% | +| Avg. stake amount | 0.001 BTC | +| Total trade volume | 0.429 BTC | +| | | +| Long / Short | 352 / 77 | +| Total profit Long % | 1250.58% | +| Total profit Short % | -15.02% | +| Absolute profit Long | 0.00838792 BTC | +| Absolute profit Short | -0.00076 BTC | +| | | +| Best Pair | LSK/BTC 26.26% | +| Worst Pair | ZEC/BTC -10.18% | +| Best Trade | LSK/BTC 4.25% | +| Worst Trade | ZEC/BTC -10.25% | +| Best day | 0.00076 BTC | +| Worst day | -0.00036 BTC | +| Days win/draw/lose | 12 / 82 / 25 | +| Avg. Duration Winners | 4:23:00 | +| Avg. Duration Loser | 6:55:00 | +| Rejected Entry signals | 3089 | +| Entry/Exit Timeouts | 0 / 0 | +| | | +| Min balance | 0.00945123 BTC | +| Max balance | 0.01846651 BTC | +| Max % of account underwater | 25.19% | +| Absolute Drawdown (Account) | 13.33% | +| Drawdown | 0.0015 BTC | +| Drawdown high | 0.0013 BTC | +| Drawdown low | -0.0002 BTC | +| Drawdown Start | 2019-02-15 14:10:00 | +| Drawdown End | 2019-04-11 18:15:00 | +| Market change | -5.88% | +===================================================== ``` ### Backtesting report table @@ -377,50 +383,51 @@ The last element of the backtest report is the summary metrics table. It contains some useful key metrics about performance of your strategy on backtesting data. ``` -================ SUMMARY METRICS =============== -| Metric | Value | -|------------------------+---------------------| -| Backtesting from | 2019-01-01 00:00:00 | -| Backtesting to | 2019-05-01 00:00:00 | -| Max open trades | 3 | -| | | -| Total/Daily Avg Trades | 429 / 3.575 | -| Starting balance | 0.01000000 BTC | -| Final balance | 0.01762792 BTC | -| Absolute profit | 0.00762792 BTC | -| Total profit % | 76.2% | -| CAGR % | 460.87% | -| Avg. stake amount | 0.001 BTC | -| Total trade volume | 0.429 BTC | -| | | -| Long / Short | 352 / 77 | -| Total profit Long % | 1250.58% | -| Total profit Short % | -15.02% | -| Absolute profit Long | 0.00838792 BTC | -| Absolute profit Short | -0.00076 BTC | -| | | -| Best Pair | LSK/BTC 26.26% | -| Worst Pair | ZEC/BTC -10.18% | -| Best Trade | LSK/BTC 4.25% | -| Worst Trade | ZEC/BTC -10.25% | -| Best day | 0.00076 BTC | -| Worst day | -0.00036 BTC | -| Days win/draw/lose | 12 / 82 / 25 | -| Avg. Duration Winners | 4:23:00 | -| Avg. Duration Loser | 6:55:00 | -| Rejected Entry signals | 3089 | -| Entry/Exit Timeouts | 0 / 0 | -| | | -| Min balance | 0.00945123 BTC | -| Max balance | 0.01846651 BTC | -| Drawdown (Account) | 13.33% | -| Drawdown | 0.0015 BTC | -| Drawdown high | 0.0013 BTC | -| Drawdown low | -0.0002 BTC | -| Drawdown Start | 2019-02-15 14:10:00 | -| Drawdown End | 2019-04-11 18:15:00 | -| Market change | -5.88% | -================================================ +================== SUMMARY METRICS ================== +| Metric | Value | +|-----------------------------+---------------------| +| Backtesting from | 2019-01-01 00:00:00 | +| Backtesting to | 2019-05-01 00:00:00 | +| Max open trades | 3 | +| | | +| Total/Daily Avg Trades | 429 / 3.575 | +| Starting balance | 0.01000000 BTC | +| Final balance | 0.01762792 BTC | +| Absolute profit | 0.00762792 BTC | +| Total profit % | 76.2% | +| CAGR % | 460.87% | +| Avg. stake amount | 0.001 BTC | +| Total trade volume | 0.429 BTC | +| | | +| Long / Short | 352 / 77 | +| Total profit Long % | 1250.58% | +| Total profit Short % | -15.02% | +| Absolute profit Long | 0.00838792 BTC | +| Absolute profit Short | -0.00076 BTC | +| | | +| Best Pair | LSK/BTC 26.26% | +| Worst Pair | ZEC/BTC -10.18% | +| Best Trade | LSK/BTC 4.25% | +| Worst Trade | ZEC/BTC -10.25% | +| Best day | 0.00076 BTC | +| Worst day | -0.00036 BTC | +| Days win/draw/lose | 12 / 82 / 25 | +| Avg. Duration Winners | 4:23:00 | +| Avg. Duration Loser | 6:55:00 | +| Rejected Entry signals | 3089 | +| Entry/Exit Timeouts | 0 / 0 | +| | | +| Min balance | 0.00945123 BTC | +| Max balance | 0.01846651 BTC | +| Max % of account underwater | 25.19% | +| Absolute Drawdown (Account) | 13.33% | +| Drawdown | 0.0015 BTC | +| Drawdown high | 0.0013 BTC | +| Drawdown low | -0.0002 BTC | +| Drawdown Start | 2019-02-15 14:10:00 | +| Drawdown End | 2019-04-11 18:15:00 | +| Market change | -5.88% | +===================================================== ``` @@ -441,7 +448,9 @@ It contains some useful key metrics about performance of your strategy on backte - `Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached. - `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used). - `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period. -- `Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as $(Absolute Drawdown) / (DrawdownHigh + startingBalance)$. +- `Max % of account underwater`: Maximum percentage your account has decreased from the top since the simulation started. +Calculated as the maximum of `(Max Balance - Current Balance) / (Max Balance)`. +- `Absolute Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`. - `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point. - `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). diff --git a/docs/hyperopt.md b/docs/hyperopt.md index 3f613a208..030d73f4b 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -116,7 +116,9 @@ optional arguments: ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, SortinoHyperOptLoss, SortinoHyperOptLossDaily, - CalmarHyperOptLoss, MaxDrawDownHyperOptLoss, ProfitDrawDownHyperOptLoss + CalmarHyperOptLoss, MaxDrawDownHyperOptLoss, + MaxDrawDownRelativeHyperOptLoss, + ProfitDrawDownHyperOptLoss --disable-param-export Disable automatic hyperopt parameter export. --ignore-missing-spaces, --ignore-unparameterized-spaces @@ -563,7 +565,8 @@ Currently, the following loss functions are builtin: * `SharpeHyperOptLossDaily` - optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation. * `SortinoHyperOptLoss` - optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation. * `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation. -* `MaxDrawDownHyperOptLoss` - Optimizes Maximum drawdown. +* `MaxDrawDownHyperOptLoss` - Optimizes Maximum absolute drawdown. +* `MaxDrawDownRelativeHyperOptLoss` - Optimizes both maximum absolute drawdown while also adjusting for maximum relative drawdown. * `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown. * `ProfitDrawDownHyperOptLoss` - Optimizes by max Profit & min Drawdown objective. `DRAWDOWN_MULT` variable within the hyperoptloss file can be adjusted to be stricter or more flexible on drawdown purposes. diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 0ceabe917..53cae8a8e 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -28,7 +28,8 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss', 'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily', 'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily', 'CalmarHyperOptLoss', - 'MaxDrawDownHyperOptLoss', 'ProfitDrawDownHyperOptLoss'] + 'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss', + 'ProfitDrawDownHyperOptLoss'] AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'AgeFilter', 'OffsetFilter', 'PerformanceFilter', 'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter', diff --git a/freqtrade/data/metrics.py b/freqtrade/data/metrics.py index 44d5ce6ec..c11a2df88 100644 --- a/freqtrade/data/metrics.py +++ b/freqtrade/data/metrics.py @@ -72,18 +72,28 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str, return df -def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str - ) -> pd.DataFrame: +def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str, + starting_balance: float) -> pd.DataFrame: max_drawdown_df = pd.DataFrame() max_drawdown_df['cumulative'] = profit_results[value_col].cumsum() max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax() max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value'] max_drawdown_df['date'] = profit_results.loc[:, date_col] + if starting_balance: + cumulative_balance = starting_balance + max_drawdown_df['cumulative'] + max_balance = starting_balance + max_drawdown_df['high_value'] + max_drawdown_df['drawdown_relative'] = ((max_balance - cumulative_balance) / max_balance) + else: + # NOTE: This is not completely accurate, + # but might good enough if starting_balance is not available + max_drawdown_df['drawdown_relative'] = ( + (max_drawdown_df['high_value'] - max_drawdown_df['cumulative']) + / max_drawdown_df['high_value']) return max_drawdown_df def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date', - value_col: str = 'profit_ratio' + value_col: str = 'profit_ratio', starting_balance: float = 0.0 ): """ Calculate max drawdown and the corresponding close dates @@ -97,13 +107,18 @@ def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date', if len(trades) == 0: raise ValueError("Trade dataframe empty.") profit_results = trades.sort_values(date_col).reset_index(drop=True) - max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col) + max_drawdown_df = _calc_drawdown_series( + profit_results, + date_col=date_col, + value_col=value_col, + starting_balance=starting_balance) return max_drawdown_df def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date', - value_col: str = 'profit_abs', starting_balance: float = 0 + value_col: str = 'profit_abs', starting_balance: float = 0, + relative: bool = False ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]: """ Calculate max drawdown and the corresponding close dates @@ -119,9 +134,15 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date' if len(trades) == 0: raise ValueError("Trade dataframe empty.") profit_results = trades.sort_values(date_col).reset_index(drop=True) - max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col) + max_drawdown_df = _calc_drawdown_series( + profit_results, + date_col=date_col, + value_col=value_col, + starting_balance=starting_balance + ) - idxmin = max_drawdown_df['drawdown'].idxmin() + idxmin = max_drawdown_df['drawdown_relative'].idxmax() if relative \ + else max_drawdown_df['drawdown'].idxmin() if idxmin == 0: raise ValueError("No losing trade, therefore no drawdown.") high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col] @@ -129,12 +150,10 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date' high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin] ['high_value'].idxmax(), 'cumulative'] low_val = max_drawdown_df.loc[idxmin, 'cumulative'] - max_drawdown_rel = 0.0 - if high_val + starting_balance != 0: - max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance) + max_drawdown_rel = max_drawdown_df.loc[idxmin, 'drawdown_relative'] return ( - abs(min(max_drawdown_df['drawdown'])), + abs(max_drawdown_df.loc[idxmin, 'drawdown']), high_date, low_date, high_val, diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py new file mode 100644 index 000000000..3182afb47 --- /dev/null +++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py @@ -0,0 +1,47 @@ +""" +MaxDrawDownRelativeHyperOptLoss + +This module defines the alternative HyperOptLoss class which can be used for +Hyperoptimization. +""" +from typing import Dict + +from pandas import DataFrame + +from freqtrade.data.metrics import calculate_underwater +from freqtrade.optimize.hyperopt import IHyperOptLoss + + +class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss): + + """ + Defines the loss function for hyperopt. + + This implementation optimizes for max draw down and profit + Less max drawdown more profit -> Lower return value + """ + + @staticmethod + def hyperopt_loss_function(results: DataFrame, config: Dict, + *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: + drawdown_df = calculate_underwater( + results, + value_col='profit_abs', + starting_balance=config['dry_run_wallet'] + ) + max_drawdown = abs(min(drawdown_df['drawdown'])) + relative_drawdown = max(drawdown_df['drawdown_relative']) + if max_drawdown == 0: + return -total_profit + return -total_profit / max_drawdown / relative_drawdown + except (Exception, ValueError): + return -total_profit diff --git a/freqtrade/optimize/hyperopt_loss_interface.py b/freqtrade/optimize/hyperopt_loss_interface.py index ac8239b75..8366dcc4f 100644 --- a/freqtrade/optimize/hyperopt_loss_interface.py +++ b/freqtrade/optimize/hyperopt_loss_interface.py @@ -19,11 +19,11 @@ class IHyperOptLoss(ABC): @staticmethod @abstractmethod - def hyperopt_loss_function(results: DataFrame, trade_count: int, + 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: + **kwargs) -> float: """ Objective function, returns smaller number for better results """ diff --git a/freqtrade/optimize/optimize_reports.py b/freqtrade/optimize/optimize_reports.py index 9c1a276a9..42db366a1 100644 --- a/freqtrade/optimize/optimize_reports.py +++ b/freqtrade/optimize/optimize_reports.py @@ -498,9 +498,12 @@ def generate_strategy_stats(pairlist: List[str], (drawdown_abs, drawdown_start, drawdown_end, high_val, low_val, max_drawdown) = calculate_max_drawdown( results, value_col='profit_abs', starting_balance=start_balance) + (_, _, _, _, _, max_relative_drawdown) = calculate_max_drawdown( + results, value_col='profit_abs', starting_balance=start_balance, relative=True) strat_stats.update({ 'max_drawdown': max_drawdown_legacy, # Deprecated - do not use 'max_drawdown_account': max_drawdown, + 'max_relative_drawdown': max_relative_drawdown, 'max_drawdown_abs': drawdown_abs, 'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT), 'drawdown_start_ts': drawdown_start.timestamp() * 1000, @@ -521,6 +524,7 @@ def generate_strategy_stats(pairlist: List[str], strat_stats.update({ 'max_drawdown': 0.0, 'max_drawdown_account': 0.0, + 'max_relative_drawdown': 0.0, 'max_drawdown_abs': 0.0, 'max_drawdown_low': 0.0, 'max_drawdown_high': 0.0, @@ -729,6 +733,26 @@ def text_table_add_metrics(strat_results: Dict) -> str: strat_results['stake_currency'])), ] if strat_results.get('trade_count_short', 0) > 0 else [] + drawdown_metrics = [] + if 'max_relative_drawdown' in strat_results: + # Compatibility to show old hyperopt results + drawdown_metrics.append( + ('Max % of account underwater', f"{strat_results['max_relative_drawdown']:.2%}") + ) + drawdown_metrics.extend([ + ('Absolute Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}") + if 'max_drawdown_account' in strat_results else ( + 'Drawdown', f"{strat_results['max_drawdown']:.2%}"), + ('Absolute Drawdown', round_coin_value(strat_results['max_drawdown_abs'], + strat_results['stake_currency'])), + ('Drawdown high', round_coin_value(strat_results['max_drawdown_high'], + strat_results['stake_currency'])), + ('Drawdown low', round_coin_value(strat_results['max_drawdown_low'], + strat_results['stake_currency'])), + ('Drawdown Start', strat_results['drawdown_start']), + ('Drawdown End', strat_results['drawdown_end']), + ]) + # Newly added fields should be ignored if they are missing in strat_results. hyperopt-show # command stores these results and newer version of freqtrade must be able to handle old # results with missing new fields. @@ -784,18 +808,7 @@ def text_table_add_metrics(strat_results: Dict) -> str: ('Max balance', round_coin_value(strat_results['csum_max'], strat_results['stake_currency'])), - # Compatibility to show old hyperopt results - ('Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}") - if 'max_drawdown_account' in strat_results else ( - 'Drawdown', f"{strat_results['max_drawdown']:.2%}"), - ('Drawdown', round_coin_value(strat_results['max_drawdown_abs'], - strat_results['stake_currency'])), - ('Drawdown high', round_coin_value(strat_results['max_drawdown_high'], - strat_results['stake_currency'])), - ('Drawdown low', round_coin_value(strat_results['max_drawdown_low'], - strat_results['stake_currency'])), - ('Drawdown Start', strat_results['drawdown_start']), - ('Drawdown End', strat_results['drawdown_end']), + *drawdown_metrics, ('Market change', f"{strat_results['market_change']:.2%}"), ] diff --git a/freqtrade/plot/plotting.py b/freqtrade/plot/plotting.py index 773577d7b..37758d05f 100644 --- a/freqtrade/plot/plotting.py +++ b/freqtrade/plot/plotting.py @@ -159,12 +159,15 @@ def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_sub def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame, - timeframe: str) -> make_subplots: + timeframe: str, starting_balance: float) -> make_subplots: """ Add scatter points indicating max drawdown """ try: - _, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown(trades) + _, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown( + trades, + starting_balance=starting_balance + ) drawdown = go.Scatter( x=[highdate, lowdate], @@ -189,22 +192,37 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame, return fig -def add_underwater(fig, row, trades: pd.DataFrame) -> make_subplots: +def add_underwater(fig, row, trades: pd.DataFrame, starting_balance: float) -> make_subplots: """ - Add underwater plot + Add underwater plots """ try: - underwater = calculate_underwater(trades, value_col="profit_abs") + underwater = calculate_underwater( + trades, + value_col="profit_abs", + starting_balance=starting_balance + ) - underwater = go.Scatter( + underwater_plot = go.Scatter( x=underwater['date'], y=underwater['drawdown'], name="Underwater Plot", fill='tozeroy', fillcolor='#cc362b', - line={'color': '#cc362b'}, + line={'color': '#cc362b'} ) - fig.add_trace(underwater, row, 1) + + underwater_plot_relative = go.Scatter( + x=underwater['date'], + y=(-underwater['drawdown_relative']), + name="Underwater Plot (%)", + fill='tozeroy', + fillcolor='green', + line={'color': 'green'} + ) + + fig.add_trace(underwater_plot, row, 1) + fig.add_trace(underwater_plot_relative, row + 1, 1) except ValueError: logger.warning("No trades found - not plotting underwater plot") return fig @@ -507,7 +525,8 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame], - trades: pd.DataFrame, timeframe: str, stake_currency: str) -> go.Figure: + trades: pd.DataFrame, timeframe: str, stake_currency: str, + starting_balance: float) -> go.Figure: # Combine close-values for all pairs, rename columns to "pair" try: df_comb = combine_dataframes_with_mean(data, "close") @@ -531,8 +550,8 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame], name='Avg close price', ) - fig = make_subplots(rows=5, cols=1, shared_xaxes=True, - row_heights=[1, 1, 1, 0.5, 1], + fig = make_subplots(rows=6, cols=1, shared_xaxes=True, + row_heights=[1, 1, 1, 0.5, 0.75, 0.75], vertical_spacing=0.05, subplot_titles=[ "AVG Close Price", @@ -540,6 +559,7 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame], "Profit per pair", "Parallelism", "Underwater", + "Relative Drawdown", ]) fig['layout'].update(title="Freqtrade Profit plot") fig['layout']['yaxis1'].update(title='Price') @@ -547,14 +567,16 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame], fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}') fig['layout']['yaxis4'].update(title='Trade count') fig['layout']['yaxis5'].update(title='Underwater Plot') + fig['layout']['yaxis6'].update(title='Underwater Plot Relative (%)', tickformat=',.2%') fig['layout']['xaxis']['rangeslider'].update(visible=False) fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"]) fig.add_trace(avgclose, 1, 1) fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit') - fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe) + fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe, starting_balance) fig = add_parallelism(fig, 4, trades, timeframe) - fig = add_underwater(fig, 5, trades) + # Two rows consumed + fig = add_underwater(fig, 5, trades, starting_balance) for pair in pairs: profit_col = f'cum_profit_{pair}' @@ -670,7 +692,8 @@ def plot_profit(config: Dict[str, Any]) -> None: # this could be useful to gauge the overall market trend fig = generate_profit_graph(plot_elements['pairs'], plot_elements['ohlcv'], trades, config['timeframe'], - config.get('stake_currency', '')) + config.get('stake_currency', ''), + config.get('available_capital', config['dry_run_wallet'])) store_plot_file(fig, filename='freqtrade-profit-plot.html', directory=config['user_data_dir'] / 'plot', auto_open=config.get('plot_auto_open', False)) diff --git a/tests/data/test_btanalysis.py b/tests/data/test_btanalysis.py index f9f49e280..4157bd899 100644 --- a/tests/data/test_btanalysis.py +++ b/tests/data/test_btanalysis.py @@ -376,3 +376,38 @@ def test_calculate_max_drawdown2(): 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') + + +@pytest.mark.parametrize('profits,relative,highd,lowd,result,result_rel', [ + ([0.0, -500.0, 500.0, 10000.0, -1000.0], False, 3, 4, 1000.0, 0.090909), + ([0.0, -500.0, 500.0, 10000.0, -1000.0], True, 0, 1, 500.0, 0.5), + +]) +def test_calculate_max_drawdown_abs(profits, relative, highd, lowd, result, result_rel): + """ + Test case from issue https://github.com/freqtrade/freqtrade/issues/6655 + [1000, 500, 1000, 11000, 10000] # absolute results + [1000, 50%, 0%, 0%, ~9%] # Relative drawdowns + """ + init_date = Arrow(2020, 1, 1) + dates = [init_date.shift(days=i) for i in range(len(profits))] + df = DataFrame(zip(profits, dates), columns=['profit_abs', 'open_date']) + # sort by profit and reset index + df = df.sort_values('profit_abs').reset_index(drop=True) + df1 = df.copy() + drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown( + df, date_col='open_date', starting_balance=1000, relative=relative) + # Ensure df has not been altered. + assert df.equals(df1) + + assert isinstance(drawdown, float) + assert isinstance(drawdown_rel, float) + assert hdate == init_date.shift(days=highd) + assert ldate == init_date.shift(days=lowd) + + # High must be before low + assert hdate < ldate + # High value must be higher than low value + assert hval > lval + assert drawdown == result + assert pytest.approx(drawdown_rel) == result_rel diff --git a/tests/optimize/test_hyperoptloss.py b/tests/optimize/test_hyperoptloss.py index 4ec80ef49..be1c313f6 100644 --- a/tests/optimize/test_hyperoptloss.py +++ b/tests/optimize/test_hyperoptloss.py @@ -85,6 +85,7 @@ def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) -> "SharpeHyperOptLoss", "SharpeHyperOptLossDaily", "MaxDrawDownHyperOptLoss", + "MaxDrawDownRelativeHyperOptLoss", "CalmarHyperOptLoss", "ProfitDrawDownHyperOptLoss", diff --git a/tests/test_plotting.py b/tests/test_plotting.py index 65df2d84c..9ee7a75c6 100644 --- a/tests/test_plotting.py +++ b/tests/test_plotting.py @@ -332,7 +332,13 @@ def test_generate_profit_graph(testdatadir): trades = trades[trades['pair'].isin(pairs)] - fig = generate_profit_graph(pairs, data, trades, timeframe="5m", stake_currency='BTC') + fig = generate_profit_graph( + pairs, + data, + trades, + timeframe="5m", + stake_currency='BTC', + starting_balance=0) assert isinstance(fig, go.Figure) assert fig.layout.title.text == "Freqtrade Profit plot" @@ -341,7 +347,7 @@ def test_generate_profit_graph(testdatadir): assert fig.layout.yaxis3.title.text == "Profit BTC" figure = fig.layout.figure - assert len(figure.data) == 7 + assert len(figure.data) == 8 avgclose = find_trace_in_fig_data(figure.data, "Avg close price") assert isinstance(avgclose, go.Scatter) @@ -356,6 +362,9 @@ def test_generate_profit_graph(testdatadir): underwater = find_trace_in_fig_data(figure.data, "Underwater Plot") assert isinstance(underwater, go.Scatter) + underwater_relative = find_trace_in_fig_data(figure.data, "Underwater Plot (%)") + assert isinstance(underwater_relative, go.Scatter) + for pair in pairs: profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}") assert isinstance(profit_pair, go.Scatter) @@ -363,7 +372,7 @@ def test_generate_profit_graph(testdatadir): with pytest.raises(OperationalException, match=r"No trades found.*"): # Pair cannot be empty - so it's an empty dataframe. generate_profit_graph(pairs, data, trades.loc[trades['pair'].isnull()], timeframe="5m", - stake_currency='BTC') + stake_currency='BTC', starting_balance=0) def test_start_plot_dataframe(mocker):