Merge pull request #6563 from italodamato/opt-ask-force-new-points

Optimize only new points

freqtrade/optimize/hyperopt.py

@@ -10,7 +10,7 @@ import warnings
 from datetime import datetime, timezone
 from math import ceil
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple
 
 import progressbar
 import rapidjson
@@ -409,6 +409,51 @@ class Hyperopt:
         # Store non-trimmed data - will be trimmed after signal generation.
         dump(preprocessed, self.data_pickle_file)
 
+    def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
+        '''
+        Enforce points returned from `self.opt.ask` have not been already evaluated
+
+        Steps:
+        1. Try to get points using `self.opt.ask` first
+        2. Discard the points that have already been evaluated
+        3. Retry using `self.opt.ask` up to 3 times
+        4. If still some points are missing in respect to `n_points`, random sample some points
+        5. Repeat until at least `n_points` points in the `asked_non_tried` list
+        6. Return a list with length truncated at `n_points`
+        '''
+        def unique_list(a_list):
+            new_list = []
+            for item in a_list:
+                if item not in new_list:
+                    new_list.append(item)
+            return new_list
+        i = 0
+        asked_non_tried: List[List[Any]] = []
+        is_random: List[bool] = []
+        while i < 5 and len(asked_non_tried) < n_points:
+            if i < 3:
+                self.opt.cache_ = {}
+                asked = unique_list(self.opt.ask(n_points=n_points * 5))
+                is_random = [False for _ in range(len(asked))]
+            else:
+                asked = unique_list(self.opt.space.rvs(n_samples=n_points * 5))
+                is_random = [True for _ in range(len(asked))]
+            is_random += [rand for x, rand in zip(asked, is_random)
+                          if x not in self.opt.Xi
+                          and x not in asked_non_tried]
+            asked_non_tried += [x for x in asked
+                                if x not in self.opt.Xi
+                                and x not in asked_non_tried]
+            i += 1
+
+        if asked_non_tried:
+            return (
+                asked_non_tried[:min(len(asked_non_tried), n_points)],
+                is_random[:min(len(asked_non_tried), n_points)]
+            )
+        else:
+            return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
+
     def start(self) -> None:
         self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
         logger.info(f"Using optimizer random state: {self.random_state}")
@@ -473,7 +518,7 @@ class Hyperopt:
                         n_rest = (i + 1) * jobs - self.total_epochs
                         current_jobs = jobs - n_rest if n_rest > 0 else jobs
 
-                        asked = self.opt.ask(n_points=current_jobs)
+                        asked, is_random = self.get_asked_points(n_points=current_jobs)
                         f_val = self.run_optimizer_parallel(parallel, asked, i)
                         self.opt.tell(asked, [v['loss'] for v in f_val])
 
@@ -492,6 +537,7 @@ class Hyperopt:
                             # evaluations can take different time. Here they are aligned in the
                             # order they will be shown to the user.
                             val['is_best'] = is_best
+                            val['is_random'] = is_random[j]
                             self.print_results(val)
 
                             if is_best:

freqtrade/optimize/hyperopt_tools.py

@@ -310,6 +310,8 @@ class HyperoptTools():
         if not has_drawdown:
             # Ensure compatibility with older versions of hyperopt results
             trials['results_metrics.max_drawdown_account'] = None
+        if 'is_random' not in trials.columns:
+            trials['is_random'] = False
 
         # New mode, using backtest result for metrics
         trials['results_metrics.winsdrawslosses'] = trials.apply(
@@ -322,12 +324,12 @@ class HyperoptTools():
                          'results_metrics.profit_total', 'results_metrics.holding_avg',
                          'results_metrics.max_drawdown',
                          'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
-                         'loss', 'is_initial_point', 'is_best']]
+                         'loss', 'is_initial_point', 'is_random', 'is_best']]
 
         trials.columns = [
             'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
             'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
-            'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
+            'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_random', 'is_best'
             ]
 
         return trials
@@ -349,9 +351,11 @@ class HyperoptTools():
         trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
 
         trials['is_profit'] = False
-        trials.loc[trials['is_initial_point'], 'Best'] = '*     '
+        trials.loc[trials['is_initial_point'] | trials['is_random'], 'Best'] = '*     '
         trials.loc[trials['is_best'], 'Best'] = 'Best'
-        trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
+        trials.loc[
+            (trials['is_initial_point'] | trials['is_random']) & trials['is_best'],
+            'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Trades'] = trials['Trades'].astype(str)
         # perc_multi = 1 if legacy_mode else 100
@@ -407,7 +411,7 @@ class HyperoptTools():
                         trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
                                                            str(trials.loc[i][j]), Style.RESET_ALL)
 
-        trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
+        trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit', 'is_random'])
         if remove_header > 0:
             table = tabulate.tabulate(
                 trials.to_dict(orient='list'), tablefmt='orgtbl',

tests/conftest.py

@@ -2672,6 +2672,7 @@ def saved_hyperopt_results():
             'total_profit': -0.00125625,
             'current_epoch': 1,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': True,
 
         }, {
@@ -2688,6 +2689,7 @@ def saved_hyperopt_results():
             'total_profit': 6.185e-05,
             'current_epoch': 2,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': False
         }, {
             'loss': 14.241196856510731,
@@ -2698,6 +2700,7 @@ def saved_hyperopt_results():
             'total_profit': -0.13639474,
             'current_epoch': 3,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': False
         }, {
             'loss': 100000,
@@ -2705,7 +2708,7 @@ def saved_hyperopt_results():
             'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}},  # noqa: E501
             'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()},  # noqa: E501
             'results_explanation': '     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.',  # noqa: E501
-            'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_best': False
+            'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_random': False, 'is_best': False  # noqa: E501
         }, {
             'loss': 0.22195522184191518,
             'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014},   # noqa: E501
@@ -2715,6 +2718,7 @@ def saved_hyperopt_results():
             'total_profit': -0.002480140000000001,
             'current_epoch': 5,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': True
         }, {
             'loss': 0.545315889154162,
@@ -2725,6 +2729,7 @@ def saved_hyperopt_results():
             'total_profit': -0.0041773,
             'current_epoch': 6,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': False
         }, {
             'loss': 4.713497421432944,
@@ -2737,6 +2742,7 @@ def saved_hyperopt_results():
             'total_profit': -0.06339929,
             'current_epoch': 7,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': False
         }, {
             'loss': 20.0,  # noqa: E501
@@ -2747,6 +2753,7 @@ def saved_hyperopt_results():
             'total_profit': 0.0,
             'current_epoch': 8,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': False
         }, {
             'loss': 2.4731817780991223,
@@ -2757,6 +2764,7 @@ def saved_hyperopt_results():
             'total_profit': -0.044050070000000004,  # noqa: E501
             'current_epoch': 9,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': False
         }, {
             'loss': -0.2604606005845212,  # noqa: E501
@@ -2767,6 +2775,7 @@ def saved_hyperopt_results():
             'total_profit': 0.00021629,
             'current_epoch': 10,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': True
         }, {
             'loss': 4.876465945994304,  # noqa: E501
@@ -2778,6 +2787,7 @@ def saved_hyperopt_results():
             'total_profit': -0.07436117,
             'current_epoch': 11,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': False
         }, {
             'loss': 100000,
@@ -2788,6 +2798,7 @@ def saved_hyperopt_results():
             'total_profit': 0,
             'current_epoch': 12,
             'is_initial_point': True,
+            'is_random': False,
             'is_best': False
             }
     ]

tests/optimize/test_hyperopt.py

@@ -41,6 +41,7 @@ def generate_result_metrics():
         'max_drawdown_abs': 0.001,
         'loss': 0.001,
         'is_initial_point': 0.001,
+        'is_random': False,
         'is_best': 1,
     }
 
@@ -247,6 +248,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
             'total_profit': 0,
             'current_epoch': 2,  # This starts from 1 (in a human-friendly manner)
             'is_initial_point': False,
+            'is_random': False,
             'is_best': True
         }
     )