Merge branch 'develop' into pr/yazeed/3008

freqtrade/optimize/backtesting.py

@@ -18,8 +18,10 @@ from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
-from freqtrade.optimize.optimize_reports import (show_backtest_results,
+from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
+                                                 show_backtest_results,
                                                  store_backtest_result)
+from freqtrade.pairlist.pairlistmanager import PairListManager
 from freqtrade.persistence import Trade
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.state import RunMode
@@ -63,11 +65,6 @@ class Backtesting:
         self.strategylist: List[IStrategy] = []
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
 
-        if config.get('fee'):
-            self.fee = config['fee']
-        else:
-            self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
-
         if self.config.get('runmode') != RunMode.HYPEROPT:
             self.dataprovider = DataProvider(self.config, self.exchange)
             IStrategy.dp = self.dataprovider
@@ -84,12 +81,31 @@ class Backtesting:
             self.strategylist.append(StrategyResolver.load_strategy(self.config))
             validate_config_consistency(self.config)
 
-        if "ticker_interval" not in self.config:
+        if "timeframe" not in self.config:
             raise OperationalException("Timeframe (ticker interval) needs to be set in either "
-                                       "configuration or as cli argument `--ticker-interval 5m`")
-        self.timeframe = str(self.config.get('ticker_interval'))
+                                       "configuration or as cli argument `--timeframe 5m`")
+        self.timeframe = str(self.config.get('timeframe'))
         self.timeframe_min = timeframe_to_minutes(self.timeframe)
 
+        self.pairlists = PairListManager(self.exchange, self.config)
+        if 'VolumePairList' in self.pairlists.name_list:
+            raise OperationalException("VolumePairList not allowed for backtesting.")
+
+        if len(self.strategylist) > 1 and 'PrecisionFilter' in self.pairlists.name_list:
+            raise OperationalException(
+                "PrecisionFilter not allowed for backtesting multiple strategies."
+            )
+
+        self.pairlists.refresh_pairlist()
+
+        if len(self.pairlists.whitelist) == 0:
+            raise OperationalException("No pair in whitelist.")
+
+        if config.get('fee', None) is not None:
+            self.fee = config['fee']
+        else:
+            self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
+
         # Get maximum required startup period
         self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
         # Load one (first) strategy
@@ -111,7 +127,7 @@ class Backtesting:
 
         data = history.load_data(
             datadir=self.config['datadir'],
-            pairs=self.config['exchange']['pair_whitelist'],
+            pairs=self.pairlists.whitelist,
             timeframe=self.timeframe,
             timerange=timerange,
             startup_candles=self.required_startup,
@@ -401,4 +417,5 @@ class Backtesting:
         if self.config.get('export', False):
             store_backtest_result(self.config['exportfilename'], all_results)
         # Show backtest results
-        show_backtest_results(self.config, data, all_results)
+        stats = generate_backtest_stats(self.config, data, all_results)
+        show_backtest_results(self.config, stats)

freqtrade/optimize/default_hyperopt_loss.py

@@ -42,8 +42,8 @@ class DefaultHyperOptLoss(IHyperOptLoss):
         * 0.25: Avoiding trade loss
         * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
         """
-        total_profit = results.profit_percent.sum()
-        trade_duration = results.trade_duration.mean()
+        total_profit = results['profit_percent'].sum()
+        trade_duration = results['trade_duration'].mean()
 
         trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
         profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)

freqtrade/optimize/hyperopt.py

@@ -12,7 +12,7 @@ from math import ceil
 from collections import OrderedDict
 from operator import itemgetter
 from pathlib import Path
-from pprint import pprint
+from pprint import pformat
 from typing import Any, Dict, List, Optional
 
 import rapidjson
@@ -49,9 +49,9 @@ logger = logging.getLogger(__name__)
 
 INITIAL_POINTS = 30
 
-# Keep no more than 2*SKOPT_MODELS_MAX_NUM models
-# in the skopt models list
-SKOPT_MODELS_MAX_NUM = 10
+# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
+# in the skopt model queue, to optimize memory consumption
+SKOPT_MODEL_QUEUE_SIZE = 10
 
 MAX_LOSS = 100000  # just a big enough number to be bad result in loss optimization
 
@@ -75,8 +75,8 @@ class Hyperopt:
         self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
         self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
 
-        self.trials_file = (self.config['user_data_dir'] /
-                            'hyperopt_results' / 'hyperopt_results.pickle')
+        self.results_file = (self.config['user_data_dir'] /
+                             'hyperopt_results' / 'hyperopt_results.pickle')
         self.data_pickle_file = (self.config['user_data_dir'] /
                                  'hyperopt_results' / 'hyperopt_tickerdata.pkl')
         self.total_epochs = config.get('epochs', 0)
@@ -88,10 +88,10 @@ class Hyperopt:
         else:
             logger.info("Continuing on previous hyperopt results.")
 
-        self.num_trials_saved = 0
+        self.num_epochs_saved = 0
 
         # Previous evaluations
-        self.trials: List = []
+        self.epochs: List = []
 
         # Populate functions here (hasattr is slow so should not be run during "regular" operations)
         if hasattr(self.custom_hyperopt, 'populate_indicators'):
@@ -132,7 +132,7 @@ class Hyperopt:
         """
         Remove hyperopt pickle files to restart hyperopt.
         """
-        for f in [self.data_pickle_file, self.trials_file]:
+        for f in [self.data_pickle_file, self.results_file]:
             p = Path(f)
             if p.is_file():
                 logger.info(f"Removing `{p}`.")
@@ -151,27 +151,26 @@ class Hyperopt:
         # and the values are taken from the list of parameters.
         return {d.name: v for d, v in zip(dimensions, raw_params)}
 
-    def save_trials(self, final: bool = False) -> None:
+    def _save_results(self) -> None:
         """
-        Save hyperopt trials to file
+        Save hyperopt results to file
         """
-        num_trials = len(self.trials)
-        if num_trials > self.num_trials_saved:
-            logger.debug(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
-            dump(self.trials, self.trials_file)
-            self.num_trials_saved = num_trials
-        if final:
-            logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
-                        f"saved to '{self.trials_file}'.")
+        num_epochs = len(self.epochs)
+        if num_epochs > self.num_epochs_saved:
+            logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
+            dump(self.epochs, self.results_file)
+            self.num_epochs_saved = num_epochs
+            logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
+                         f"saved to '{self.results_file}'.")
 
     @staticmethod
-    def _read_trials(trials_file: Path) -> List:
+    def _read_results(results_file: Path) -> List:
         """
-        Read hyperopt trials file
+        Read hyperopt results from file
         """
-        logger.info("Reading Trials from '%s'", trials_file)
-        trials = load(trials_file)
-        return trials
+        logger.info("Reading epochs from '%s'", results_file)
+        data = load(results_file)
+        return data
 
     def _get_params_details(self, params: Dict) -> Dict:
         """
@@ -231,6 +230,9 @@ class Hyperopt:
             if space in ['buy', 'sell']:
                 result_dict.setdefault('params', {}).update(space_params)
             elif space == 'roi':
+                # TODO: get rid of OrderedDict when support for python 3.6 will be
+                # dropped (dicts keep the order as the language feature)
+
                 # Convert keys in min_roi dict to strings because
                 # rapidjson cannot dump dicts with integer keys...
                 # OrderedDict is used to keep the numeric order of the items
@@ -245,11 +247,24 @@ class Hyperopt:
     def _params_pretty_print(params, space: str, header: str) -> None:
         if space in params:
             space_params = Hyperopt._space_params(params, space, 5)
+            params_result = f"\n# {header}\n"
             if space == 'stoploss':
-                print(header, space_params.get('stoploss'))
+                params_result += f"stoploss = {space_params.get('stoploss')}"
+            elif space == 'roi':
+                # TODO: get rid of OrderedDict when support for python 3.6 will be
+                # dropped (dicts keep the order as the language feature)
+                minimal_roi_result = rapidjson.dumps(
+                    OrderedDict(
+                        (str(k), v) for k, v in space_params.items()
+                    ),
+                    default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
+                params_result += f"minimal_roi = {minimal_roi_result}"
             else:
-                print(header)
-                pprint(space_params, indent=4)
+                params_result += f"{space}_params = {pformat(space_params, indent=4)}"
+                params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
+
+            params_result = params_result.replace("\n", "\n    ")
+            print(params_result)
 
     @staticmethod
     def _space_params(params, space: str, r: int = None) -> Dict:
@@ -304,8 +319,9 @@ class Hyperopt:
         trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
                           'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
         trials['is_profit'] = False
-        trials.loc[trials['is_initial_point'], 'Best'] = '*'
+        trials.loc[trials['is_initial_point'], 'Best'] = '*     '
         trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Trades'] = trials['Trades'].astype(str)
 
@@ -375,27 +391,35 @@ class Hyperopt:
 
         # Verification for overwrite
         if path.isfile(csv_file):
-            logger.error("CSV-File already exists!")
+            logger.error(f"CSV file already exists: {csv_file}")
             return
 
         try:
             io.open(csv_file, 'w+').close()
         except IOError:
-            logger.error("Filed to create CSV-File!")
+            logger.error(f"Failed to create CSV file: {csv_file}")
             return
 
         trials = json_normalize(results, max_level=1)
         trials['Best'] = ''
         trials['Stake currency'] = config['stake_currency']
-        trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
-                         'results_metrics.avg_profit', 'results_metrics.total_profit',
-                         'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
-                         'loss', 'is_initial_point', 'is_best']]
-        trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
-                          'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
+
+        base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
+                        'results_metrics.avg_profit', 'results_metrics.total_profit',
+                        'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
+                        'loss', 'is_initial_point', 'is_best']
+        param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
+        trials = trials[base_metrics + param_metrics]
+
+        base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
+                        'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
+        param_columns = list(results[0]['params_dict'].keys())
+        trials.columns = base_columns + param_columns
+
         trials['is_profit'] = False
         trials.loc[trials['is_initial_point'], 'Best'] = '*'
         trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Epoch'] = trials['Epoch'].astype(str)
         trials['Trades'] = trials['Trades'].astype(str)
@@ -418,7 +442,7 @@ class Hyperopt:
 
         trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
         trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
-        print("CSV-File created!")
+        logger.info(f"CSV file created: {csv_file}")
 
     def has_space(self, space: str) -> bool:
         """
@@ -570,43 +594,28 @@ class Hyperopt:
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
             random_state=self.random_state,
+            model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
         )
 
-    def fix_optimizer_models_list(self) -> None:
-        """
-        WORKAROUND: Since skopt is not actively supported, this resolves problems with skopt
-        memory usage, see also: https://github.com/scikit-optimize/scikit-optimize/pull/746
-
-        This may cease working when skopt updates if implementation of this intrinsic
-        part changes.
-        """
-        n = len(self.opt.models) - SKOPT_MODELS_MAX_NUM
-        # Keep no more than 2*SKOPT_MODELS_MAX_NUM models in the skopt models list,
-        # remove the old ones. These are actually of no use, the current model
-        # from the estimator is the only one used in the skopt optimizer.
-        # Freqtrade code also does not inspect details of the models.
-        if n >= SKOPT_MODELS_MAX_NUM:
-            logger.debug(f"Fixing skopt models list, removing {n} old items...")
-            del self.opt.models[0:n]
-
     def run_optimizer_parallel(self, parallel, asked, i) -> List:
         return parallel(delayed(
                         wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
 
     @staticmethod
-    def load_previous_results(trials_file: Path) -> List:
+    def load_previous_results(results_file: Path) -> List:
         """
         Load data for epochs from the file if we have one
         """
-        trials: List = []
-        if trials_file.is_file() and trials_file.stat().st_size > 0:
-            trials = Hyperopt._read_trials(trials_file)
-            if trials[0].get('is_best') is None:
+        epochs: List = []
+        if results_file.is_file() and results_file.stat().st_size > 0:
+            epochs = Hyperopt._read_results(results_file)
+            # Detection of some old format, without 'is_best' field saved
+            if epochs[0].get('is_best') is None:
                 raise OperationalException(
                     "The file with Hyperopt results is incompatible with this version "
                     "of Freqtrade and cannot be loaded.")
-            logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
-        return trials
+            logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
+        return epochs
 
     def _set_random_state(self, random_state: Optional[int]) -> int:
         return random_state or random.randint(1, 2**16 - 1)
@@ -632,8 +641,9 @@ class Hyperopt:
 
         # We don't need exchange instance anymore while running hyperopt
         self.backtesting.exchange = None  # type: ignore
+        self.backtesting.pairlists = None  # type: ignore
 
-        self.trials = self.load_previous_results(self.trials_file)
+        self.epochs = self.load_previous_results(self.results_file)
 
         cpus = cpu_count()
         logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
@@ -669,7 +679,7 @@ class Hyperopt:
                         ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
                     ]
                 with progressbar.ProgressBar(
-                         maxval=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
+                         max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
                          widgets=widgets
                      ) as pbar:
                     EVALS = ceil(self.total_epochs / jobs)
@@ -682,7 +692,6 @@ class Hyperopt:
                         asked = self.opt.ask(n_points=current_jobs)
                         f_val = self.run_optimizer_parallel(parallel, asked, i)
                         self.opt.tell(asked, [v['loss'] for v in f_val])
-                        self.fix_optimizer_models_list()
 
                         # Calculate progressbar outputs
                         for j, val in enumerate(f_val):
@@ -703,23 +712,25 @@ class Hyperopt:
 
                             if is_best:
                                 self.current_best_loss = val['loss']
-                            self.trials.append(val)
+                            self.epochs.append(val)
 
                             # Save results after each best epoch and every 100 epochs
                             if is_best or current % 100 == 0:
-                                self.save_trials()
+                                self._save_results()
 
                             pbar.update(current)
 
         except KeyboardInterrupt:
             print('User interrupted..')
 
-        self.save_trials(final=True)
+        self._save_results()
+        logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
+                    f"saved to '{self.results_file}'.")
 
-        if self.trials:
-            sorted_trials = sorted(self.trials, key=itemgetter('loss'))
-            results = sorted_trials[0]
-            self.print_epoch_details(results, self.total_epochs, self.print_json)
+        if self.epochs:
+            sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
+            best_epoch = sorted_epochs[0]
+            self.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
         else:
             # This is printed when Ctrl+C is pressed quickly, before first epochs have
             # a chance to be evaluated.

freqtrade/optimize/hyperopt_interface.py

@@ -31,13 +31,15 @@ class IHyperOpt(ABC):
     Class attributes you can use:
         ticker_interval -> int: value of the ticker interval to use for the strategy
     """
-    ticker_interval: str
+    ticker_interval: str  # DEPRECATED
+    timeframe: str
 
     def __init__(self, config: dict) -> None:
         self.config = config
 
         # Assign ticker_interval to be used in hyperopt
-        IHyperOpt.ticker_interval = str(config['ticker_interval'])
+        IHyperOpt.ticker_interval = str(config['timeframe'])  # DEPRECATED
+        IHyperOpt.timeframe = str(config['timeframe'])
 
     @staticmethod
     def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
@@ -218,9 +220,10 @@ class IHyperOpt(ABC):
     # Why do I still need such shamanic mantras in modern python?
     def __getstate__(self):
         state = self.__dict__.copy()
-        state['ticker_interval'] = self.ticker_interval
+        state['timeframe'] = self.timeframe
         return state
 
     def __setstate__(self, state):
         self.__dict__.update(state)
-        IHyperOpt.ticker_interval = state['ticker_interval']
+        IHyperOpt.ticker_interval = state['timeframe']
+        IHyperOpt.timeframe = state['timeframe']

freqtrade/optimize/hyperopt_loss_interface.py

@@ -14,7 +14,7 @@ class IHyperOptLoss(ABC):
     Interface for freqtrade hyperopt Loss functions.
     Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
     """
-    ticker_interval: str
+    timeframe: str
 
     @staticmethod
     @abstractmethod

freqtrade/optimize/hyperopt_loss_onlyprofit.py

@@ -34,5 +34,5 @@ class OnlyProfitHyperOptLoss(IHyperOptLoss):
         """
         Objective function, returns smaller number for better results.
         """
-        total_profit = results.profit_percent.sum()
+        total_profit = results['profit_percent'].sum()
         return 1 - total_profit / EXPECTED_MAX_PROFIT

freqtrade/optimize/optimize_reports.py

@@ -1,7 +1,7 @@
 import logging
 from datetime import timedelta
 from pathlib import Path
-from typing import Dict
+from typing import Any, Dict, List
 
 from pandas import DataFrame
 from tabulate import tabulate
@@ -18,10 +18,7 @@ def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame
     :param all_results: Dict of Dataframes, one results dataframe per strategy
     """
     for strategy, results in all_results.items():
-        records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
-                    t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
-                    t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
-                   for index, t in results.iterrows()]
+        records = backtest_result_to_list(results)
 
         if records:
             filename = recordfilename
@@ -34,153 +31,141 @@ def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame
             file_dump_json(filename, records)
 
 
-def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
-                        results: DataFrame, skip_nan: bool = False) -> str:
+def backtest_result_to_list(results: DataFrame) -> List[List]:
     """
-    Generates and returns a text table for the given backtest data and the results dataframe
+    Converts a list of Backtest-results to list
+    :param results: Dataframe containing results for one strategy
+    :return: List of Lists containing the trades
+    """
+    return [[t.pair, t.profit_percent, t.open_time.timestamp(),
+             t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
+             t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value]
+            for index, t in results.iterrows()]
+
+
+def _get_line_floatfmt() -> List[str]:
+    """
+    Generate floatformat (goes in line with _generate_result_line())
+    """
+    return ['s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', 'd', 'd', 'd']
+
+
+def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
+    """
+    Generate header lines (goes in line with _generate_result_line())
+    """
+    return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
+            f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
+            'Wins', 'Draws', 'Losses']
+
+
+def _generate_result_line(result: DataFrame, max_open_trades: int, first_column: str) -> Dict:
+    """
+    Generate one result dict, with "first_column" as key.
+    """
+    return {
+        'key': first_column,
+        'trades': len(result),
+        'profit_mean': result['profit_percent'].mean(),
+        'profit_mean_pct': result['profit_percent'].mean() * 100.0,
+        'profit_sum': result['profit_percent'].sum(),
+        'profit_sum_pct': result['profit_percent'].sum() * 100.0,
+        'profit_total_abs': result['profit_abs'].sum(),
+        'profit_total_pct': result['profit_percent'].sum() * 100.0 / max_open_trades,
+        'duration_avg': str(timedelta(
+                            minutes=round(result['trade_duration'].mean()))
+                            ) if not result.empty else '0:00',
+        # 'duration_max': str(timedelta(
+        #                     minutes=round(result['trade_duration'].max()))
+        #                     ) if not result.empty else '0:00',
+        # 'duration_min': str(timedelta(
+        #                     minutes=round(result['trade_duration'].min()))
+        #                     ) if not result.empty else '0:00',
+        'wins': len(result[result['profit_abs'] > 0]),
+        'draws': len(result[result['profit_abs'] == 0]),
+        'losses': len(result[result['profit_abs'] < 0]),
+    }
+
+
+def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
+                          results: DataFrame, skip_nan: bool = False) -> List[Dict]:
+    """
+    Generates and returns a list  for the given backtest data and the results dataframe
     :param data: Dict of <pair: dataframe> containing data that was used during backtesting.
     :param stake_currency: stake-currency - used to correctly name headers
     :param max_open_trades: Maximum allowed open trades
     :param results: Dataframe containing the backtest results
     :param skip_nan: Print "left open" open trades
-    :return: pretty printed table with tabulate as string
+    :return: List of Dicts containing the metrics per pair
     """
 
-    floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
     tabular_data = []
-    headers = [
-        'Pair',
-        'Buys',
-        'Avg Profit %',
-        'Cum Profit %',
-        f'Tot Profit {stake_currency}',
-        'Tot Profit %',
-        'Avg Duration',
-        'Wins',
-        'Draws',
-        'Losses'
-    ]
+
     for pair in data:
-        result = results[results.pair == pair]
-        if skip_nan and result.profit_abs.isnull().all():
+        result = results[results['pair'] == pair]
+        if skip_nan and result['profit_abs'].isnull().all():
             continue
 
-        tabular_data.append([
-            pair,
-            len(result.index),
-            result.profit_percent.mean() * 100.0,
-            result.profit_percent.sum() * 100.0,
-            result.profit_abs.sum(),
-            result.profit_percent.sum() * 100.0 / max_open_trades,
-            str(timedelta(
-                minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
-            len(result[result.profit_abs > 0]),
-            len(result[result.profit_abs == 0]),
-            len(result[result.profit_abs < 0])
-        ])
+        tabular_data.append(_generate_result_line(result, max_open_trades, pair))
 
     # Append Total
-    tabular_data.append([
-        'TOTAL',
-        len(results.index),
-        results.profit_percent.mean() * 100.0,
-        results.profit_percent.sum() * 100.0,
-        results.profit_abs.sum(),
-        results.profit_percent.sum() * 100.0 / max_open_trades,
-        str(timedelta(
-            minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
-        len(results[results.profit_abs > 0]),
-        len(results[results.profit_abs == 0]),
-        len(results[results.profit_abs < 0])
-    ])
-    # Ignore type as floatfmt does allow tuples but mypy does not know that
-    return tabulate(tabular_data, headers=headers,
-                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
+    tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
+    return tabular_data
 
 
-def generate_text_table_sell_reason(stake_currency: str, max_open_trades: int,
-                                    results: DataFrame) -> str:
+def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
     """
     Generate small table outlining Backtest results
-    :param stake_currency: Stakecurrency used
     :param max_open_trades: Max_open_trades parameter
-    :param results: Dataframe containing the backtest results
-    :return: pretty printed table with tabulate as string
+    :param results: Dataframe containing the backtest result for one strategy
+    :return: List of Dicts containing the metrics per Sell reason
     """
     tabular_data = []
-    headers = [
-        "Sell Reason",
-        "Sells",
-        "Wins",
-        "Draws",
-        "Losses",
-        "Avg Profit %",
-        "Cum Profit %",
-        f"Tot Profit {stake_currency}",
-        "Tot Profit %",
-    ]
+
     for reason, count in results['sell_reason'].value_counts().iteritems():
         result = results.loc[results['sell_reason'] == reason]
-        wins = len(result[result['profit_abs'] > 0])
-        draws = len(result[result['profit_abs'] == 0])
-        loss = len(result[result['profit_abs'] < 0])
-        profit_mean = round(result['profit_percent'].mean() * 100.0, 2)
-        profit_sum = round(result["profit_percent"].sum() * 100.0, 2)
-        profit_tot = result['profit_abs'].sum()
+
+        profit_mean = result['profit_percent'].mean()
+        profit_sum = result["profit_percent"].sum()
         profit_percent_tot = round(result['profit_percent'].sum() * 100.0 / max_open_trades, 2)
+
         tabular_data.append(
-            [
-                reason.value,
-                count,
-                wins,
-                draws,
-                loss,
-                profit_mean,
-                profit_sum,
-                profit_tot,
-                profit_percent_tot,
-            ]
+            {
+                'sell_reason': reason.value,
+                'trades': count,
+                'wins': len(result[result['profit_abs'] > 0]),
+                'draws': len(result[result['profit_abs'] == 0]),
+                'losses': len(result[result['profit_abs'] < 0]),
+                'profit_mean': profit_mean,
+                'profit_mean_pct': round(profit_mean * 100, 2),
+                'profit_sum': profit_sum,
+                'profit_sum_pct': round(profit_sum * 100, 2),
+                'profit_total_abs': result['profit_abs'].sum(),
+                'profit_pct_total': profit_percent_tot,
+            }
         )
-    return tabulate(tabular_data, headers=headers, tablefmt="orgtbl", stralign="right")
+    return tabular_data
 
 
-def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
-                                 all_results: Dict) -> str:
+def generate_strategy_metrics(stake_currency: str, max_open_trades: int,
+                              all_results: Dict) -> List[Dict]:
     """
-    Generate summary table per strategy
+    Generate summary per strategy
     :param stake_currency: stake-currency - used to correctly name headers
     :param max_open_trades: Maximum allowed open trades used for backtest
     :param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
-    :return: pretty printed table with tabulate as string
+    :return: List of Dicts containing the metrics per Strategy
     """
 
-    floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
     tabular_data = []
-    headers = ['Strategy', 'Buys', 'Avg Profit %', 'Cum Profit %',
-               f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
-               'Wins', 'Draws', 'Losses']
     for strategy, results in all_results.items():
-        tabular_data.append([
-            strategy,
-            len(results.index),
-            results.profit_percent.mean() * 100.0,
-            results.profit_percent.sum() * 100.0,
-            results.profit_abs.sum(),
-            results.profit_percent.sum() * 100.0 / max_open_trades,
-            str(timedelta(
-                minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
-            len(results[results.profit_abs > 0]),
-            len(results[results.profit_abs == 0]),
-            len(results[results.profit_abs < 0])
-        ])
-    # Ignore type as floatfmt does allow tuples but mypy does not know that
-    return tabulate(tabular_data, headers=headers,
-                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
+        tabular_data.append(_generate_result_line(results, max_open_trades, strategy))
+    return tabular_data
 
 
 def generate_edge_table(results: dict) -> str:
 
-    floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
+    floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
     tabular_data = []
     headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
                'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
@@ -204,40 +189,145 @@ def generate_edge_table(results: dict) -> str:
                     floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
 
 
-def show_backtest_results(config: Dict, btdata: Dict[str, DataFrame],
-                          all_results: Dict[str, DataFrame]):
+def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
+                            all_results: Dict[str, DataFrame]) -> Dict[str, Any]:
+    """
+    :param config: Configuration object used for backtest
+    :param btdata: Backtest data
+    :param all_results: backtest result - dictionary with { Strategy: results}.
+    :return:
+    Dictionary containing results per strategy and a stratgy summary.
+    """
+    stake_currency = config['stake_currency']
+    max_open_trades = config['max_open_trades']
+    result: Dict[str, Any] = {'strategy': {}}
     for strategy, results in all_results.items():
 
+        pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
+                                             max_open_trades=max_open_trades,
+                                             results=results, skip_nan=False)
+        sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
+                                                       results=results)
+        left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
+                                                  max_open_trades=max_open_trades,
+                                                  results=results.loc[results['open_at_end']],
+                                                  skip_nan=True)
+        strat_stats = {
+            'trades': backtest_result_to_list(results),
+            'results_per_pair': pair_results,
+            'sell_reason_summary': sell_reason_stats,
+            'left_open_trades': left_open_results,
+            }
+        result['strategy'][strategy] = strat_stats
+
+    strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
+                                                 max_open_trades=max_open_trades,
+                                                 all_results=all_results)
+
+    result['strategy_comparison'] = strategy_results
+
+    return result
+
+
+###
+# Start output section
+###
+
+def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
+    """
+    Generates and returns a text table for the given backtest data and the results dataframe
+    :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
+    :param stake_currency: stake-currency - used to correctly name headers
+    :return: pretty printed table with tabulate as string
+    """
+
+    headers = _get_line_header('Pair', stake_currency)
+    floatfmt = _get_line_floatfmt()
+    output = [[
+        t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
+        t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
+    ] for t in pair_results]
+    # Ignore type as floatfmt does allow tuples but mypy does not know that
+    return tabulate(output, headers=headers,
+                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
+
+
+def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
+    """
+    Generate small table outlining Backtest results
+    :param sell_reason_stats: Sell reason metrics
+    :param stake_currency: Stakecurrency used
+    :return: pretty printed table with tabulate as string
+    """
+    headers = [
+        'Sell Reason',
+        'Sells',
+        'Wins',
+        'Draws',
+        'Losses',
+        'Avg Profit %',
+        'Cum Profit %',
+        f'Tot Profit {stake_currency}',
+        'Tot Profit %',
+    ]
+
+    output = [[
+        t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
+        t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_pct_total'],
+    ] for t in sell_reason_stats]
+    return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
+
+
+def text_table_strategy(strategy_results, stake_currency: str) -> str:
+    """
+    Generate summary table per strategy
+    :param stake_currency: stake-currency - used to correctly name headers
+    :param max_open_trades: Maximum allowed open trades used for backtest
+    :param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
+    :return: pretty printed table with tabulate as string
+    """
+    floatfmt = _get_line_floatfmt()
+    headers = _get_line_header('Strategy', stake_currency)
+
+    output = [[
+        t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
+        t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
+    ] for t in strategy_results]
+    # Ignore type as floatfmt does allow tuples but mypy does not know that
+    return tabulate(output, headers=headers,
+                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
+
+
+def show_backtest_results(config: Dict, backtest_stats: Dict):
+    stake_currency = config['stake_currency']
+
+    for strategy, results in backtest_stats['strategy'].items():
+
+        # Print results
         print(f"Result for strategy {strategy}")
-        table = generate_text_table(btdata, stake_currency=config['stake_currency'],
-                                    max_open_trades=config['max_open_trades'],
-                                    results=results)
+        table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
         if isinstance(table, str):
             print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
         print(table)
 
-        table = generate_text_table_sell_reason(stake_currency=config['stake_currency'],
-                                                max_open_trades=config['max_open_trades'],
-                                                results=results)
+        table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
+                                       stake_currency=stake_currency)
         if isinstance(table, str):
             print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
         print(table)
 
-        table = generate_text_table(btdata,
-                                    stake_currency=config['stake_currency'],
-                                    max_open_trades=config['max_open_trades'],
-                                    results=results.loc[results.open_at_end], skip_nan=True)
+        table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
         if isinstance(table, str):
             print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
         print(table)
         if isinstance(table, str):
             print('=' * len(table.splitlines()[0]))
         print()
-    if len(all_results) > 1:
+
+    if len(backtest_stats['strategy']) > 1:
         # Print Strategy summary table
-        table = generate_text_table_strategy(config['stake_currency'],
-                                             config['max_open_trades'],
-                                             all_results=all_results)
+
+        table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency)
         print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
         print(table)
         print('=' * len(table.splitlines()[0]))