Merge pull request #5678 from sidml/develop

Hyperopt loss function using max drawdown

docs/hyperopt.md

@@ -60,7 +60,7 @@ optional arguments:
                         Specify what timerange of data to use.
   --data-format-ohlcv {json,jsongz,hdf5}
                         Storage format for downloaded candle (OHLCV) data.
-                        (default: `None`).
+                        (default: `json`).
   --max-open-trades INT
                         Override the value of the `max_open_trades`
                         configuration setting.
@@ -114,7 +114,8 @@ optional arguments:
                         Hyperopt-loss-functions are:
                         ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
                         SharpeHyperOptLoss, SharpeHyperOptLossDaily,
-                        SortinoHyperOptLoss, SortinoHyperOptLossDaily
+                        SortinoHyperOptLoss, SortinoHyperOptLossDaily,
+                        MaxDrawDownHyperOptLoss
   --disable-param-export
                         Disable automatic hyperopt parameter export.
 
@@ -512,12 +513,13 @@ This class should be in its own file within the `user_data/hyperopts/` directory
 
 Currently, the following loss functions are builtin:
 
-* `ShortTradeDurHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function) - Mostly for short trade duration and avoiding losses.
-* `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
-* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
-* `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)
+* `ShortTradeDurHyperOptLoss` - (default legacy Freqtrade hyperoptimization loss function) - Mostly for short trade duration and avoiding losses.
+* `OnlyProfitHyperOptLoss` - takes only amount of profit into consideration.
+* `SharpeHyperOptLoss` - optimizes Sharpe Ratio calculated on trade returns relative to standard deviation.
+* `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.
 
 Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 

freqtrade/constants.py

@@ -24,7 +24,8 @@ ORDERTYPE_POSSIBILITIES = ['limit', 'market']
 ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
 HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
-                         'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily']
+                         'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
+                         'MaxDrawDownHyperOptLoss']
 AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
                        'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
                        'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',

freqtrade/optimize/hyperopt_loss_max_drawdown.py

@@ -0,0 +1,41 @@
+"""
+MaxDrawDownHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+from datetime import datetime
+
+from pandas import DataFrame
+
+from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class MaxDrawDownHyperOptLoss(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, 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]

tests/optimize/test_hyperoptloss.py

@@ -84,13 +84,14 @@ def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) ->
     "SortinoHyperOptLossDaily",
     "SharpeHyperOptLoss",
     "SharpeHyperOptLossDaily",
+    "MaxDrawDownHyperOptLoss",
 ])
 def test_loss_functions_better_profits(default_conf, hyperopt_results, lossfunction) -> None:
     results_over = hyperopt_results.copy()
-    results_over['profit_abs'] = hyperopt_results['profit_abs'] * 2
+    results_over['profit_abs'] = hyperopt_results['profit_abs'] * 2 + 0.2
     results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
     results_under = hyperopt_results.copy()
-    results_under['profit_abs'] = hyperopt_results['profit_abs'] / 2
+    results_under['profit_abs'] = hyperopt_results['profit_abs'] / 2 - 0.2
     results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
 
     default_conf.update({'hyperopt_loss': lossfunction})