added daily sharpe ratio hyperopt loss method, ty @djacky (#2826)

* more consistent backtesting tables and labels * added rounding to Tot Profit % on Sell Reasosn table to be consistent with other percentiles on table. * added daily sharpe ratio hyperopt loss method, ty @djacky * removed commented code * removed unused profit_abs * added proper slippage to each trade * replaced use of old value total_profit * Align quotes in same area * added daily sharpe ratio test and modified hyperopt_loss_sharpe_daily * fixed some more line alignments * updated docs to include SharpeHyperOptLossDaily * Update dockerfile to 3.8.1 * Run tests against 3.8 * added daily sharpe ratio hyperopt loss method, ty @djacky * removed commented code * removed unused profit_abs * added proper slippage to each trade * replaced use of old value total_profit * added daily sharpe ratio test and modified hyperopt_loss_sharpe_daily * updated docs to include SharpeHyperOptLossDaily * docs fixes * missed one fix * fixed standard deviation line * fixed to bracket notation * fixed to bracket notation * fixed syntax error * better readability, kept np.sqrt(365) which results in annualized sharpe ratio * fixed method arguments indentation * updated commented out debug print line * renamed after slippage profit_percent so it wont affect _calculate_results_metrics() * Reworked to fill leading and trailing days * No need for np; make flake happy * Fix risk free rate Co-authored-by: Matthias <xmatthias@outlook.com> Co-authored-by: hroff-1902 <47309513+hroff-1902@users.noreply.github.com>
2020-02-06 06:49:08 +01:00
parent b5ee4f17cb
commit 9639ffb140
5 changed files with 110 additions and 21 deletions
--- a/freqtrade/optimize/hyperopt_loss_sharpe_daily.py
+++ b/freqtrade/optimize/hyperopt_loss_sharpe_daily.py
@@ -0,0 +1,61 @@
+"""
+SharpeHyperOptLossDaily
+
+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 SharpeHyperOptLossDaily(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sharpe 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 Sharpe Ratio calculation.
+        """
+        resample_freq = '1D'
+        slippage_per_trade_ratio = 0.0005
+        days_in_year = 365
+        annual_risk_free_rate = 0.0
+        risk_free_rate = annual_risk_free_rate / days_in_year
+
+        # 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)
+
+        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"] - risk_free_rate
+        expected_returns_mean = total_profit.mean()
+        up_stdev = total_profit.std()
+
+        if (up_stdev != 0.):
+            sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
+        else:
+            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+            sharp_ratio = -20.
+
+        # print(t_index, sum_daily, total_profit)
+        # print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
+        return -sharp_ratio