merged lev-freqtradebot with lev-strat

freqtrade/optimize/backtesting.py

@@ -157,7 +157,7 @@ class Backtesting:
         self.strategy: IStrategy = strategy
         strategy.dp = self.dataprovider
         # Attach Wallets to Strategy baseclass
-        IStrategy.wallets = self.wallets
+        strategy.wallets = self.wallets
         # Set stoploss_on_exchange to false for backtesting,
         # since a "perfect" stoploss-sell is assumed anyway
         # And the regular "stoploss" function would not apply to that case

freqtrade/optimize/edge_cli.py

@@ -8,6 +8,7 @@ from typing import Any, Dict
 
 from freqtrade import constants
 from freqtrade.configuration import TimeRange, validate_config_consistency
+from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
 from freqtrade.optimize.optimize_reports import generate_edge_table
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@@ -33,6 +34,7 @@ class EdgeCli:
         self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
         self.strategy = StrategyResolver.load_strategy(self.config)
+        self.strategy.dp = DataProvider(config, None)
 
         validate_config_consistency(self.config)
 

freqtrade/optimize/hyperopt.py

@@ -45,7 +45,7 @@ progressbar.streams.wrap_stdout()
 logger = logging.getLogger(__name__)
 
 
-INITIAL_POINTS = 30
+INITIAL_POINTS = 5
 
 # Keep no more than SKOPT_MODEL_QUEUE_SIZE models
 # in the skopt model queue, to optimize memory consumption
@@ -241,7 +241,7 @@ class Hyperopt:
 
         if HyperoptTools.has_space(self.config, 'buy'):
             logger.debug("Hyperopt has 'buy' space")
-            self.buy_space = self.custom_hyperopt.indicator_space()
+            self.buy_space = self.custom_hyperopt.buy_indicator_space()
 
         if HyperoptTools.has_space(self.config, 'sell'):
             logger.debug("Hyperopt has 'sell' space")
@@ -365,10 +365,20 @@ class Hyperopt:
         }
 
     def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
+        estimator = self.custom_hyperopt.generate_estimator()
+
+        acq_optimizer = "sampling"
+        if isinstance(estimator, str):
+            if estimator not in ("GP", "RF", "ET", "GBRT"):
+                raise OperationalException(f"Estimator {estimator} not supported.")
+            else:
+                acq_optimizer = "auto"
+
+        logger.info(f"Using estimator {estimator}.")
         return Optimizer(
             dimensions,
-            base_estimator="ET",
-            acq_optimizer="auto",
+            base_estimator=estimator,
+            acq_optimizer=acq_optimizer,
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
             random_state=self.random_state,

freqtrade/optimize/hyperopt_auto.py

@@ -12,7 +12,7 @@ from freqtrade.exceptions import OperationalException
 with suppress(ImportError):
     from skopt.space import Dimension
 
-from freqtrade.optimize.hyperopt_interface import IHyperOpt
+from freqtrade.optimize.hyperopt_interface import EstimatorType, IHyperOpt
 
 
 def _format_exception_message(space: str) -> str:
@@ -56,7 +56,7 @@ class HyperOptAuto(IHyperOpt):
         else:
             _format_exception_message(category)
 
-    def indicator_space(self) -> List['Dimension']:
+    def buy_indicator_space(self) -> List['Dimension']:
         return self._get_indicator_space('buy')
 
     def sell_indicator_space(self) -> List['Dimension']:
@@ -79,3 +79,6 @@ class HyperOptAuto(IHyperOpt):
 
     def trailing_space(self) -> List['Dimension']:
         return self._get_func('trailing_space')()
+
+    def generate_estimator(self) -> EstimatorType:
+        return self._get_func('generate_estimator')()

freqtrade/optimize/hyperopt_interface.py

@@ -5,8 +5,9 @@ This module defines the interface to apply for hyperopt
 import logging
 import math
 from abc import ABC
-from typing import Dict, List
+from typing import Dict, List, Union
 
+from sklearn.base import RegressorMixin
 from skopt.space import Categorical, Dimension, Integer
 
 from freqtrade.exchange import timeframe_to_minutes
@@ -17,6 +18,8 @@ from freqtrade.strategy import IStrategy
 
 logger = logging.getLogger(__name__)
 
+EstimatorType = Union[RegressorMixin, str]
+
 
 class IHyperOpt(ABC):
     """
@@ -37,6 +40,14 @@ class IHyperOpt(ABC):
         IHyperOpt.ticker_interval = str(config['timeframe'])  # DEPRECATED
         IHyperOpt.timeframe = str(config['timeframe'])
 
+    def generate_estimator(self) -> EstimatorType:
+        """
+        Return base_estimator.
+        Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
+        inheriting from RegressorMixin (from sklearn).
+        """
+        return 'ET'
+
     def generate_roi_table(self, params: Dict) -> Dict[int, float]:
         """
         Create a ROI table.

freqtrade/optimize/hyperopt_tools.py

@@ -7,6 +7,7 @@ from pathlib import Path
 from typing import Any, Dict, Iterator, List, Optional, Tuple
 
 import numpy as np
+import pandas as pd
 import rapidjson
 import tabulate
 from colorama import Fore, Style
@@ -298,8 +299,8 @@ class HyperoptTools():
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def prepare_trials_columns(trials, legacy_mode: bool, has_drawdown: bool) -> str:
-
+    def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
+                               has_drawdown: bool) -> pd.DataFrame:
         trials['Best'] = ''
 
         if 'results_metrics.winsdrawslosses' not in trials.columns:
@@ -435,8 +436,7 @@ class HyperoptTools():
         return table
 
     @staticmethod
-    def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
-                        csv_file: str) -> None:
+    def export_csv_file(config: dict, results: list, csv_file: str) -> None:
         """
         Log result to csv-file
         """