Merge pull request #2879 from freqtrade/sortino_hyperopt_loss

Sortino hyperopt loss

docs/bot-usage.md

@@ -275,7 +275,7 @@ Check the corresponding [Data Downloading](data-download.md) section for more de
 ## Hyperopt commands
 
 To optimize your strategy, you can use hyperopt parameter hyperoptimization
-to find optimal parameter values for your stategy.
+to find optimal parameter values for your strategy.
 
 ```
 usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
@@ -323,7 +323,7 @@ optional arguments:
   --print-all           Print all results, not only the best ones.
   --no-color            Disable colorization of hyperopt results. May be
                         useful if you are redirecting output to a file.
-  --print-json          Print best result detailization in JSON format.
+  --print-json          Print best results in JSON format.
   -j JOBS, --job-workers JOBS
                         The number of concurrently running jobs for
                         hyperoptimization (hyperopt worker processes). If -1
@@ -341,10 +341,11 @@ optional arguments:
                         class (IHyperOptLoss). Different functions can
                         generate completely different results, since the
                         target for optimization is different. Built-in
-                        Hyperopt-loss-functions are: DefaultHyperOptLoss,
-                        OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
-                        SharpeHyperOptLossDaily.(default:
-                        `DefaultHyperOptLoss`).
+                        Hyperopt-loss-functions are:
+                        DefaultHyperOptLoss, OnlyProfitHyperOptLoss,
+                        SharpeHyperOptLoss, SharpeHyperOptLossDaily,
+                        SortinoHyperOptLoss, SortinoHyperOptLossDaily.
+                        (default: `DefaultHyperOptLoss`).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).

docs/hyperopt.md

@@ -31,9 +31,9 @@ This will create a new hyperopt file from a template, which will be located unde
 Depending on the space you want to optimize, only some of the below are required:
 
 * fill `buy_strategy_generator` - for buy signal optimization
-* fill `indicator_space` - for buy signal optimzation
+* fill `indicator_space` - for buy signal optimization
 * fill `sell_strategy_generator` - for sell signal optimization
-* fill `sell_indicator_space` - for sell signal optimzation
+* fill `sell_indicator_space` - for sell signal optimization
 
 !!! Note
     `populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
@@ -81,11 +81,11 @@ There are two places you need to change in your hyperopt file to add a new buy h
 There you have two different types of indicators: 1. `guards` and 2. `triggers`.
 
 1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
-2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower bollinger band".
+2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower Bollinger band".
 
 Hyperoptimization will, for each eval round, pick one trigger and possibly
 multiple guards. The constructed strategy will be something like
-"*buy exactly when close price touches lower bollinger band, BUT only if
+"*buy exactly when close price touches lower Bollinger band, BUT only if
 ADX > 10*".
 
 If you have updated the buy strategy, i.e. changed the contents of
@@ -172,7 +172,7 @@ So let's write the buy strategy using these values:
 Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
 with different value combinations. It will then use the given historical data and make
 buys based on the buy signals generated with the above function and based on the results
-it will end with telling you which paramter combination produced the best profits.
+it will end with telling you which parameter combination produced the best profits.
 
 The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
 When you want to test an indicator that isn't used by the bot currently, remember to
@@ -191,8 +191,10 @@ Currently, the following loss functions are builtin:
 
 * `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
 * `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
-* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
-* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on daily trade returns)
+* `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)
 
 Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 
@@ -272,7 +274,7 @@ In some situations, you may need to run Hyperopt (and Backtesting) with the
 By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
 open trade is allowed for every traded pair. The total number of trades open for all pairs
 is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
-some potential trades to be hidden (or masked) by previosly open trades.
+some potential trades to be hidden (or masked) by previously open trades.
 
 The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
 while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`

freqtrade/commands/cli_options.py

@@ -257,7 +257,8 @@ AVAILABLE_CLI_OPTIONS = {
         help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
         'Different functions can generate completely different results, '
         'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
-        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily.'
+        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, '
+        'SortinoHyperOptLoss, SortinoHyperOptLossDaily.'
         '(default: `%(default)s`).',
         metavar='NAME',
         default=constants.DEFAULT_HYPEROPT_LOSS,

freqtrade/optimize/hyperopt_loss_sortino.py

@@ -0,0 +1,49 @@
+"""
+SortinoHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+from datetime import datetime
+
+from pandas import DataFrame
+import numpy as np
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SortinoHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sortino Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sortino Ratio calculation.
+        """
+        total_profit = results["profit_percent"]
+        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
+
+        results['downside_returns'] = 0
+        results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
+        down_stdev = np.std(results['downside_returns'])
+
+        if np.std(total_profit) != 0.0:
+            sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
+        else:
+            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+            sortino_ratio = -20.
+
+        # print(expected_returns_mean, down_stdev, sortino_ratio)
+        return -sortino_ratio

freqtrade/optimize/hyperopt_loss_sortino_daily.py

@@ -0,0 +1,70 @@
+"""
+SortinoHyperOptLossDaily
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+import math
+from datetime import datetime
+
+from pandas import DataFrame, date_range
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SortinoHyperOptLossDaily(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sortino Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sortino Ratio calculation.
+
+        Sortino Ratio calculated as described in
+        http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
+        """
+        resample_freq = '1D'
+        slippage_per_trade_ratio = 0.0005
+        days_in_year = 365
+        minimum_acceptable_return = 0.0
+
+        # apply slippage per trade to profit_percent
+        results.loc[:, 'profit_percent_after_slippage'] = \
+            results['profit_percent'] - slippage_per_trade_ratio
+
+        # create the index within the min_date and end max_date
+        t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
+                             normalize=True)
+
+        sum_daily = (
+            results.resample(resample_freq, on='close_time').agg(
+                {"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
+        )
+
+        total_profit = sum_daily["profit_percent_after_slippage"] - minimum_acceptable_return
+        expected_returns_mean = total_profit.mean()
+
+        sum_daily['downside_returns'] = 0
+        sum_daily.loc[total_profit < 0, 'downside_returns'] = total_profit
+        total_downside = sum_daily['downside_returns']
+        # Here total_downside contains min(0, P - MAR) values,
+        # where P = sum_daily["profit_percent_after_slippage"]
+        down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
+
+        if (down_stdev != 0.):
+            sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
+        else:
+            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+            sortino_ratio = -20.
+
+        # print(t_index, sum_daily, total_profit)
+        # print(minimum_acceptable_return, expected_returns_mean, down_stdev, sortino_ratio)
+        return -sortino_ratio

tests/optimize/test_hyperopt.py

@@ -369,6 +369,42 @@ def test_sharpe_loss_daily_prefers_higher_profits(default_conf, hyperopt_results
     assert under > correct
 
 
+def test_sortino_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
+    results_over = hyperopt_results.copy()
+    results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
+    results_under = hyperopt_results.copy()
+    results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
+
+    default_conf.update({'hyperopt_loss': 'SortinoHyperOptLoss'})
+    hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
+    correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
+                                        datetime(2019, 1, 1), datetime(2019, 5, 1))
+    over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
+                                     datetime(2019, 1, 1), datetime(2019, 5, 1))
+    under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
+                                      datetime(2019, 1, 1), datetime(2019, 5, 1))
+    assert over < correct
+    assert under > correct
+
+
+def test_sortino_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None:
+    results_over = hyperopt_results.copy()
+    results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
+    results_under = hyperopt_results.copy()
+    results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
+
+    default_conf.update({'hyperopt_loss': 'SortinoHyperOptLossDaily'})
+    hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
+    correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
+                                        datetime(2019, 1, 1), datetime(2019, 5, 1))
+    over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
+                                     datetime(2019, 1, 1), datetime(2019, 5, 1))
+    under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
+                                      datetime(2019, 1, 1), datetime(2019, 5, 1))
+    assert over < correct
+    assert under > correct
+
+
 def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
     results_over = hyperopt_results.copy()
     results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2