Merge remote-tracking branch 'upstream/develop' into hyperopt-trailing-space

freqtrade/configuration/arguments.py

@@ -37,7 +37,11 @@ ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
 ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one_column",
                    "print_csv", "base_currencies", "quote_currencies", "list_pairs_all"]
 
-ARGS_CREATE_USERDIR = ["user_data_dir"]
+ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
+
+ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
+
+ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
 
 ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
                       "timeframes", "erase"]
@@ -52,7 +56,7 @@ ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
 NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
                     "plot-dataframe", "plot-profit"]
 
-NO_CONF_ALLOWED = ["create-userdir", "list-exchanges"]
+NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
 
 
 class Arguments:
@@ -117,6 +121,7 @@ class Arguments:
         from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
         from freqtrade.utils import (start_create_userdir, start_download_data,
                                      start_list_exchanges, start_list_markets,
+                                     start_new_hyperopt, start_new_strategy,
                                      start_list_timeframes, start_trading)
         from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
 
@@ -158,6 +163,18 @@ class Arguments:
         create_userdir_cmd.set_defaults(func=start_create_userdir)
         self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
 
+        # add new-strategy subcommand
+        build_strategy_cmd = subparsers.add_parser('new-strategy',
+                                                   help="Create new strategy")
+        build_strategy_cmd.set_defaults(func=start_new_strategy)
+        self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
+
+        # add new-hyperopt subcommand
+        build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
+                                                   help="Create new hyperopt")
+        build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
+        self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
+
         # Add list-exchanges subcommand
         list_exchanges_cmd = subparsers.add_parser(
             'list-exchanges',

freqtrade/configuration/cli_options.py

@@ -62,6 +62,11 @@ AVAILABLE_CLI_OPTIONS = {
         help='Path to userdata directory.',
         metavar='PATH',
     ),
+    "reset": Arg(
+        '--reset',
+        help='Reset sample files to their original state.',
+        action='store_true',
+    ),
     # Main options
     "strategy": Arg(
         '-s', '--strategy',
@@ -333,6 +338,14 @@ AVAILABLE_CLI_OPTIONS = {
         help='Clean all existing data for the selected exchange/pairs/timeframes.',
         action='store_true',
     ),
+    # Templating options
+    "template": Arg(
+        '--template',
+        help='Use a template which is either `minimal` or '
+        '`full` (containing multiple sample indicators). Default: `%(default)s`.',
+        choices=['full', 'minimal'],
+        default='full',
+    ),
     # Plot dataframe
     "indicators1": Arg(
         '--indicators1',

freqtrade/configuration/config_validation.py

@@ -61,11 +61,16 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
     :param conf: Config in JSON format
     :return: Returns None if everything is ok, otherwise throw an OperationalException
     """
+
     # validating trailing stoploss
     _validate_trailing_stoploss(conf)
     _validate_edge(conf)
     _validate_whitelist(conf)
 
+    # validate configuration before returning
+    logger.info('Validating configuration ...')
+    validate_config_schema(conf)
+
 
 def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
 

freqtrade/configuration/configuration.py

@@ -9,8 +9,6 @@ from typing import Any, Callable, Dict, List, Optional
 
 from freqtrade import OperationalException, constants
 from freqtrade.configuration.check_exchange import check_exchange
-from freqtrade.configuration.config_validation import (validate_config_consistency,
-                                                       validate_config_schema)
 from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
 from freqtrade.configuration.directory_operations import (create_datadir,
                                                           create_userdata_dir)
@@ -84,10 +82,6 @@ class Configuration:
         if 'pairlists' not in config:
             config['pairlists'] = []
 
-        # validate configuration before returning
-        logger.info('Validating configuration ...')
-        validate_config_schema(config)
-
         return config
 
     def load_config(self) -> Dict[str, Any]:
@@ -118,8 +112,6 @@ class Configuration:
 
         process_temporary_deprecated_settings(config)
 
-        validate_config_consistency(config)
-
         return config
 
     def _process_logging_options(self, config: Dict[str, Any]) -> None:

freqtrade/configuration/deprecated_settings.py

@@ -58,6 +58,13 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
     process_deprecated_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
                                'experimental', 'ignore_roi_if_buy_signal')
 
+    if not config.get('pairlists') and not config.get('pairlists'):
+        config['pairlists'] = [{'method': 'StaticPairList'}]
+        logger.warning(
+            "DEPRECATED: "
+            "Pairlists must be defined explicitly in the future."
+            "Defaulting to StaticPairList for now.")
+
     if config.get('pairlist', {}).get("method") == 'VolumePairList':
         logger.warning(
             "DEPRECATED: "

freqtrade/configuration/directory_operations.py

@@ -1,8 +1,10 @@
 import logging
-from typing import Any, Dict, Optional
+import shutil
 from pathlib import Path
+from typing import Any, Dict, Optional
 
 from freqtrade import OperationalException
+from freqtrade.constants import USER_DATA_FILES
 
 logger = logging.getLogger(__name__)
 
@@ -31,7 +33,8 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
     :param create_dir: Create directory if it does not exist.
     :return: Path object containing the directory
     """
-    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "plot", "strategies", ]
+    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
+                "plot", "strategies", ]
     folder = Path(directory)
     if not folder.is_dir():
         if create_dir:
@@ -48,3 +51,26 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
         if not subfolder.is_dir():
             subfolder.mkdir(parents=False)
     return folder
+
+
+def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
+    """
+    Copy files from templates to User data directory.
+    :param directory: Directory to copy data to
+    :param overwrite: Overwrite existing sample files
+    """
+    if not directory.is_dir():
+        raise OperationalException(f"Directory `{directory}` does not exist.")
+    sourcedir = Path(__file__).parents[1] / "templates"
+    for source, target in USER_DATA_FILES.items():
+        targetdir = directory / target
+        if not targetdir.is_dir():
+            raise OperationalException(f"Directory `{targetdir}` does not exist.")
+        targetfile = targetdir / source
+        if targetfile.exists():
+            if not overwrite:
+                logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
+                continue
+            else:
+                logger.warning(f"File `{targetfile}` exists already, overwriting.")
+        shutil.copy(str(sourcedir / source), str(targetfile))

freqtrade/constants.py

@@ -6,7 +6,6 @@ bot constants
 DEFAULT_CONFIG = 'config.json'
 DEFAULT_EXCHANGE = 'bittrex'
 PROCESS_THROTTLE_SECS = 5  # sec
-DEFAULT_TICKER_INTERVAL = 5  # min
 HYPEROPT_EPOCH = 100  # epochs
 RETRY_TIMEOUT = 30  # sec
 DEFAULT_HYPEROPT_LOSS = 'DefaultHyperOptLoss'
@@ -22,6 +21,18 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'PrecisionFilter', 'P
 DRY_RUN_WALLET = 999.9
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
 
+USERPATH_HYPEROPTS = 'hyperopts'
+USERPATH_STRATEGY = 'strategies'
+
+# Soure files with destination directories within user-directory
+USER_DATA_FILES = {
+    'sample_strategy.py': USERPATH_STRATEGY,
+    'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
+    'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
+    'sample_hyperopt.py': USERPATH_HYPEROPTS,
+    'strategy_analysis_example.ipynb': 'notebooks',
+}
+
 TIMEFRAMES = [
     '1m', '3m', '5m', '15m', '30m',
     '1h', '2h', '4h', '6h', '8h', '12h',
@@ -54,13 +65,13 @@ MINIMAL_CONFIG = {
 CONF_SCHEMA = {
     'type': 'object',
     'properties': {
-        'max_open_trades': {'type': 'integer', 'minimum': -1},
+        'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
         'ticker_interval': {'type': 'string', 'enum': TIMEFRAMES},
         'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
         'stake_amount': {
-            "type": ["number", "string"],
-            "minimum": 0.0005,
-            "pattern": UNLIMITED_STAKE_AMOUNT
+            'type': ['number', 'string'],
+            'minimum': 0.0001,
+            'pattern': UNLIMITED_STAKE_AMOUNT
         },
         'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
         'dry_run': {'type': 'boolean'},
@@ -82,8 +93,8 @@ CONF_SCHEMA = {
         'unfilledtimeout': {
             'type': 'object',
             'properties': {
-                'buy': {'type': 'number', 'minimum': 3},
-                'sell': {'type': 'number', 'minimum': 10}
+                'buy': {'type': 'number', 'minimum': 1},
+                'sell': {'type': 'number', 'minimum': 1}
             }
         },
         'bid_strategy': {
@@ -95,7 +106,7 @@ CONF_SCHEMA = {
                     'maximum': 1,
                     'exclusiveMaximum': False,
                     'use_order_book': {'type': 'boolean'},
-                    'order_book_top': {'type': 'number', 'maximum': 20, 'minimum': 1},
+                    'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
                     'check_depth_of_market': {
                         'type': 'object',
                         'properties': {
@@ -111,8 +122,8 @@ CONF_SCHEMA = {
             'type': 'object',
             'properties': {
                 'use_order_book': {'type': 'boolean'},
-                'order_book_min': {'type': 'number', 'minimum': 1},
-                'order_book_max': {'type': 'number', 'minimum': 1, 'maximum': 50},
+                'order_book_min': {'type': 'integer', 'minimum': 1},
+                'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
                 'use_sell_signal': {'type': 'boolean'},
                 'sell_profit_only': {'type': 'boolean'},
                 'ignore_roi_if_buy_signal': {'type': 'boolean'}
@@ -185,8 +196,8 @@ CONF_SCHEMA = {
                 'listen_ip_address': {'format': 'ipv4'},
                 'listen_port': {
                     'type': 'integer',
-                    "minimum": 1024,
-                    "maximum": 65535
+                    'minimum': 1024,
+                    'maximum': 65535
                 },
                 'username': {'type': 'string'},
                 'password': {'type': 'string'},
@@ -199,7 +210,7 @@ CONF_SCHEMA = {
         'internals': {
             'type': 'object',
             'properties': {
-                'process_throttle_secs': {'type': 'number'},
+                'process_throttle_secs': {'type': 'integer'},
                 'interval': {'type': 'integer'},
                 'sd_notify': {'type': 'boolean'},
             }
@@ -241,32 +252,32 @@ CONF_SCHEMA = {
         'edge': {
             'type': 'object',
             'properties': {
-                "enabled": {'type': 'boolean'},
-                "process_throttle_secs": {'type': 'integer', 'minimum': 600},
-                "calculate_since_number_of_days": {'type': 'integer'},
-                "allowed_risk": {'type': 'number'},
-                "capital_available_percentage": {'type': 'number'},
-                "stoploss_range_min": {'type': 'number'},
-                "stoploss_range_max": {'type': 'number'},
-                "stoploss_range_step": {'type': 'number'},
-                "minimum_winrate": {'type': 'number'},
-                "minimum_expectancy": {'type': 'number'},
-                "min_trade_number": {'type': 'number'},
-                "max_trade_duration_minute": {'type': 'integer'},
-                "remove_pumps": {'type': 'boolean'}
+                'enabled': {'type': 'boolean'},
+                'process_throttle_secs': {'type': 'integer', 'minimum': 600},
+                'calculate_since_number_of_days': {'type': 'integer'},
+                'allowed_risk': {'type': 'number'},
+                'capital_available_percentage': {'type': 'number'},
+                'stoploss_range_min': {'type': 'number'},
+                'stoploss_range_max': {'type': 'number'},
+                'stoploss_range_step': {'type': 'number'},
+                'minimum_winrate': {'type': 'number'},
+                'minimum_expectancy': {'type': 'number'},
+                'min_trade_number': {'type': 'number'},
+                'max_trade_duration_minute': {'type': 'integer'},
+                'remove_pumps': {'type': 'boolean'}
             },
             'required': ['process_throttle_secs', 'allowed_risk', 'capital_available_percentage']
         }
     },
-    'anyOf': [
-        {'required': ['exchange']}
-    ],
     'required': [
+        'exchange',
         'max_open_trades',
         'stake_currency',
         'stake_amount',
         'dry_run',
         'bid_strategy',
         'unfilledtimeout',
+        'stoploss',
+        'minimal_roi',
     ]
 }

freqtrade/freqtradebot.py

@@ -266,7 +266,11 @@ class FreqtradeBot:
             amount_reserve_percent += self.strategy.stoploss
         # it should not be more than 50%
         amount_reserve_percent = max(amount_reserve_percent, 0.5)
-        return min(min_stake_amounts) / amount_reserve_percent
+
+        # The value returned should satisfy both limits: for amount (base currency) and
+        # for cost (quote, stake currency), so max() is used here.
+        # See also #2575 at github.
+        return max(min_stake_amounts) / amount_reserve_percent
 
     def create_trades(self) -> bool:
         """

freqtrade/misc.py

@@ -127,3 +127,16 @@ def round_dict(d, n):
 
 def plural(num, singular: str, plural: str = None) -> str:
     return singular if (num == 1 or num == -1) else plural or singular + 's'
+
+
+def render_template(templatefile: str, arguments: dict = {}):
+
+    from jinja2 import Environment, PackageLoader, select_autoescape
+
+    env = Environment(
+        loader=PackageLoader('freqtrade', 'templates'),
+        autoescape=select_autoescape(['html', 'xml'])
+    )
+    template = env.get_template(templatefile)
+
+    return template.render(**arguments)

freqtrade/optimize/backtesting.py

@@ -13,7 +13,8 @@ from pandas import DataFrame
 from tabulate import tabulate
 
 from freqtrade import OperationalException
-from freqtrade.configuration import TimeRange, remove_credentials
+from freqtrade.configuration import (TimeRange, remove_credentials,
+                                     validate_config_consistency)
 from freqtrade.data import history
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@@ -75,10 +76,12 @@ class Backtesting:
                 stratconf = deepcopy(self.config)
                 stratconf['strategy'] = strat
                 self.strategylist.append(StrategyResolver(stratconf).strategy)
+                validate_config_consistency(stratconf)
 
         else:
             # No strategy list specified, only one strategy
             self.strategylist.append(StrategyResolver(self.config).strategy)
+            validate_config_consistency(self.config)
 
         if "ticker_interval" not in self.config:
             raise OperationalException("Ticker-interval needs to be set in either configuration "

freqtrade/optimize/edge_cli.py

@@ -9,7 +9,8 @@ from typing import Any, Dict
 from tabulate import tabulate
 
 from freqtrade import constants
-from freqtrade.configuration import TimeRange, remove_credentials
+from freqtrade.configuration import (TimeRange, remove_credentials,
+                                     validate_config_consistency)
 from freqtrade.edge import Edge
 from freqtrade.exchange import Exchange
 from freqtrade.resolvers import StrategyResolver
@@ -35,6 +36,8 @@ class EdgeCli:
         self.exchange = Exchange(self.config)
         self.strategy = StrategyResolver(self.config).strategy
 
+        validate_config_consistency(self.config)
+
         self.edge = Edge(config, self.exchange, self.strategy)
         # Set refresh_pairs to false for edge-cli (it must be true for edge)
         self.edge._refresh_pairs = False

freqtrade/optimize/hyperopt.py

@@ -23,7 +23,7 @@ from skopt import Optimizer
 from skopt.space import Dimension
 
 from freqtrade.data.history import get_timeframe, trim_dataframe
-from freqtrade.misc import round_dict
+from freqtrade.misc import plural, round_dict
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
 from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F4
@@ -77,6 +77,8 @@ class Hyperopt:
         # Previous evaluations
         self.trials: List = []
 
+        self.num_trials_saved = 0
+
         # Populate functions here (hasattr is slow so should not be run during "regular" operations)
         if hasattr(self.custom_hyperopt, 'populate_indicators'):
             self.backtesting.strategy.advise_indicators = \
@@ -132,13 +134,18 @@ class Hyperopt:
         arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
         return arg_dict
 
-    def save_trials(self) -> None:
+    def save_trials(self, final: bool = False) -> None:
         """
         Save hyperopt trials to file
         """
-        if self.trials:
-            logger.info("Saving %d evaluations to '%s'", len(self.trials), self.trials_file)
+        num_trials = len(self.trials)
+        if num_trials > self.num_trials_saved:
+            logger.info(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}'.")
 
     def read_trials(self) -> List:
         """
@@ -153,6 +160,12 @@ class Hyperopt:
         """
         Display Best hyperopt result
         """
+        # This is printed when Ctrl+C is pressed quickly, before first epochs have
+        # a chance to be evaluated.
+        if not self.trials:
+            print("No epochs evaluated yet, no best result.")
+            return
+
         results = sorted(self.trials, key=itemgetter('loss'))
         best_result = results[0]
         params = best_result['params']
@@ -209,12 +222,20 @@ class Hyperopt:
                 print('Trailing stop:')
                 pprint(self.space_params(params, 'trailing', 5), indent=4)
 
+    def is_best(self, results) -> bool:
+        return results['loss'] < self.current_best_loss
+
     def log_results(self, results) -> None:
         """
         Log results if it is better than any previous evaluation
         """
         print_all = self.config.get('print_all', False)
-        is_best_loss = results['loss'] < self.current_best_loss
+        is_best_loss = self.is_best(results)
+
+        if not print_all:
+            print('.', end='' if results['current_epoch'] % 100 != 0 else None)  # type: ignore
+            sys.stdout.flush()
+
         if print_all or is_best_loss:
             if is_best_loss:
                 self.current_best_loss = results['loss']
@@ -229,13 +250,9 @@ class Hyperopt:
                 print(log_str)
             else:
                 print(f'\n{log_str}')
-        else:
-            print('.', end='')
-            sys.stdout.flush()
 
     def format_results_logstring(self, results) -> str:
-        # Output human-friendly index here (starting from 1)
-        current = results['current_epoch'] + 1
+        current = results['current_epoch']
         total = self.total_epochs
         res = results['results_explanation']
         loss = results['loss']
@@ -460,15 +477,19 @@ class Hyperopt:
                     self.opt.tell(asked, [v['loss'] for v in f_val])
                     self.fix_optimizer_models_list()
                     for j in range(jobs):
-                        current = i * jobs + j
+                        # Use human-friendly index here (starting from 1)
+                        current = i * jobs + j + 1
                         val = f_val[j]
                         val['current_epoch'] = current
-                        val['is_initial_point'] = current < INITIAL_POINTS
+                        val['is_initial_point'] = current <= INITIAL_POINTS
+                        logger.debug(f"Optimizer epoch evaluated: {val}")
+                        is_best = self.is_best(val)
                         self.log_results(val)
                         self.trials.append(val)
-                        logger.debug(f"Optimizer epoch evaluated: {val}")
+                        if is_best or current % 100 == 0:
+                            self.save_trials()
         except KeyboardInterrupt:
             print('User interrupted..')
 
-        self.save_trials()
+        self.save_trials(final=True)
         self.log_trials_result()

freqtrade/resolvers/hyperopt_resolver.py

@@ -8,7 +8,7 @@ from pathlib import Path
 from typing import Optional, Dict
 
 from freqtrade import OperationalException
-from freqtrade.constants import DEFAULT_HYPEROPT_LOSS
+from freqtrade.constants import DEFAULT_HYPEROPT_LOSS, USERPATH_HYPEROPTS
 from freqtrade.optimize.hyperopt_interface import IHyperOpt
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
 from freqtrade.resolvers import IResolver
@@ -58,7 +58,7 @@ class HyperOptResolver(IResolver):
         current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
 
         abs_paths = self.build_search_paths(config, current_path=current_path,
-                                            user_subdir='hyperopts', extra_dir=extra_dir)
+                                            user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
 
         hyperopt = self._load_object(paths=abs_paths, object_type=IHyperOpt,
                                      object_name=hyperopt_name, kwargs={'config': config})
@@ -110,7 +110,7 @@ class HyperOptLossResolver(IResolver):
         current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
 
         abs_paths = self.build_search_paths(config, current_path=current_path,
-                                            user_subdir='hyperopts', extra_dir=extra_dir)
+                                            user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
 
         hyperoptloss = self._load_object(paths=abs_paths, object_type=IHyperOptLoss,
                                          object_name=hyper_loss_name)

freqtrade/resolvers/strategy_resolver.py

@@ -129,7 +129,8 @@ class StrategyResolver(IResolver):
         current_path = Path(__file__).parent.parent.joinpath('strategy').resolve()
 
         abs_paths = self.build_search_paths(config, current_path=current_path,
-                                            user_subdir='strategies', extra_dir=extra_dir)
+                                            user_subdir=constants.USERPATH_STRATEGY,
+                                            extra_dir=extra_dir)
 
         if ":" in strategy_name:
             logger.info("loading base64 encoded strategy")

freqtrade/rpc/api_server.py

@@ -312,7 +312,7 @@ class ApiServer(RPC):
         logger.info("LocalRPC - Profit Command Called")
 
         stats = self._rpc_trade_statistics(self._config['stake_currency'],
-                                           self._config['fiat_display_currency']
+                                           self._config.get('fiat_display_currency')
                                            )
 
         return self.rest_dump(stats)
@@ -354,7 +354,8 @@ class ApiServer(RPC):
 
         Returns the current status of the trades in json format
         """
-        results = self._rpc_balance(self._config.get('fiat_display_currency', ''))
+        results = self._rpc_balance(self._config['stake_currency'],
+                                    self._config.get('fiat_display_currency', ''))
         return self.rest_dump(results)
 
     @require_login

freqtrade/rpc/rpc.py

@@ -297,34 +297,42 @@ class RPC:
             'best_rate': round(bp_rate * 100, 2),
         }
 
-    def _rpc_balance(self, fiat_display_currency: str) -> Dict:
+    def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
         """ Returns current account balance per crypto """
         output = []
         total = 0.0
-        for coin, balance in self._freqtrade.exchange.get_balances().items():
-            if not balance['total']:
+        try:
+            tickers = self._freqtrade.exchange.get_tickers()
+        except (TemporaryError, DependencyException):
+            raise RPCException('Error getting current tickers.')
+
+        for coin, balance in self._freqtrade.wallets.get_all_balances().items():
+            if not balance.total:
                 continue
 
-            if coin == 'BTC':
+            est_stake: float = 0
+            if coin == stake_currency:
                 rate = 1.0
+                est_stake = balance.total
             else:
                 try:
-                    pair = self._freqtrade.exchange.get_valid_pair_combination(coin, "BTC")
-                    if pair.startswith("BTC"):
-                        rate = 1.0 / self._freqtrade.get_sell_rate(pair, False)
-                    else:
-                        rate = self._freqtrade.get_sell_rate(pair, False)
+                    pair = self._freqtrade.exchange.get_valid_pair_combination(coin, stake_currency)
+                    rate = tickers.get(pair, {}).get('bid', None)
+                    if rate:
+                        if pair.startswith(stake_currency):
+                            rate = 1.0 / rate
+                        est_stake = rate * balance.total
                 except (TemporaryError, DependencyException):
                     logger.warning(f" Could not get rate for pair {coin}.")
                     continue
-            est_btc: float = rate * balance['total']
-            total = total + est_btc
+            total = total + (est_stake or 0)
             output.append({
                 'currency': coin,
-                'free': balance['free'] if balance['free'] is not None else 0,
-                'balance': balance['total'] if balance['total'] is not None else 0,
-                'used': balance['used'] if balance['used'] is not None else 0,
-                'est_btc': est_btc,
+                'free': balance.free if balance.free is not None else 0,
+                'balance': balance.total if balance.total is not None else 0,
+                'used': balance.used if balance.used is not None else 0,
+                'est_stake': est_stake or 0,
+                'stake': stake_currency,
             })
         if total == 0.0:
             if self._freqtrade.config.get('dry_run', False):

freqtrade/rpc/telegram.py

@@ -325,15 +325,16 @@ class Telegram(RPC):
     def _balance(self, update: Update, context: CallbackContext) -> None:
         """ Handler for /balance """
         try:
-            result = self._rpc_balance(self._config.get('fiat_display_currency', ''))
+            result = self._rpc_balance(self._config['stake_currency'],
+                                       self._config.get('fiat_display_currency', ''))
             output = ''
             for currency in result['currencies']:
-                if currency['est_btc'] > 0.0001:
+                if currency['est_stake'] > 0.0001:
                     curr_output = "*{currency}:*\n" \
                             "\t`Available: {free: .8f}`\n" \
                             "\t`Balance: {balance: .8f}`\n" \
                             "\t`Pending: {used: .8f}`\n" \
-                            "\t`Est. BTC: {est_btc: .8f}`\n".format(**currency)
+                            "\t`Est. {stake}: {est_stake: .8f}`\n".format(**currency)
                 else:
                     curr_output = "*{currency}:* not showing <1$ amount \n".format(**currency)
 

freqtrade/templates/base_hyperopt.py.j2

@@ -0,0 +1,127 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class {{ hyperopt }}(IHyperOpt):
+    """
+    This is a Hyperopt template to get you started.
+
+    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
+
+    You should:
+    - Add any lib you need to build your hyperopt.
+
+    You must keep:
+    - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
+
+    The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
+    copied in every custom hyperopt. However, you may override them if you need the
+    'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
+    Sample implementation of these methods can be found in
+    https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
+    """
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            # GUARDS AND TRENDS
+            {{ buy_guards | indent(12) }}
+
+            # TRIGGERS
+            if 'trigger' in params:
+                if params['trigger'] == 'bb_lower':
+                    conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
+                if params['trigger'] == 'macd_cross_signal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['macd'], dataframe['macdsignal']
+                    ))
+                if params['trigger'] == 'sar_reversal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['close'], dataframe['sar']
+                    ))
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching buy strategy parameters.
+        """
+        return [
+            {{ buy_space | indent(12) }}
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by Hyperopt.
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Sell strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            # GUARDS AND TRENDS
+            {{ sell_guards | indent(12) }}
+
+            # TRIGGERS
+            if 'sell-trigger' in params:
+                if params['sell-trigger'] == 'sell-bb_upper':
+                    conditions.append(dataframe['close'] > dataframe['bb_upperband'])
+                if params['sell-trigger'] == 'sell-macd_cross_signal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['macdsignal'], dataframe['macd']
+                    ))
+                if params['sell-trigger'] == 'sell-sar_reversal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['sar'], dataframe['close']
+                    ))
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'sell'] = 1
+
+            return dataframe
+
+        return populate_sell_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters.
+        """
+        return [
+            {{ sell_space | indent(12) }}
+        ]

freqtrade/templates/base_strategy.py.j2

@@ -0,0 +1,138 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+
+from freqtrade.strategy.interface import IStrategy
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class {{ strategy }}(IStrategy):
+    """
+    This is a strategy template to get you started.
+    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
+
+    You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
+    populate_sell_trend, hyperopt_space, buy_strategy_generator
+    """
+    # Strategy interface version - allow new iterations of the strategy interface.
+    # Check the documentation or the Sample strategy to get the latest version.
+    INTERFACE_VERSION = 2
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi".
+    minimal_roi = {
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy.
+    # This attribute will be overridden if the config file contains "stoploss".
+    stoploss = -0.10
+
+    # Trailing stoploss
+    trailing_stop = False
+    # trailing_stop_positive = 0.01
+    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
+
+    # Optimal ticker interval for the strategy.
+    ticker_interval = '5m'
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = False
+
+    # These values can be overridden in the "ask_strategy" section in the config.
+    use_sell_signal = True
+    sell_profit_only = False
+    ignore_roi_if_buy_signal = False
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
+    # Optional order type mapping.
+    order_types = {
+        'buy': 'limit',
+        'sell': 'limit',
+        'stoploss': 'market',
+        'stoploss_on_exchange': False
+    }
+
+    # Optional order time in force.
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc'
+    }
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+        {{ indicators | indent(8) }}
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                {{ buy_trend | indent(16) }}
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                {{ sell_trend | indent(16) }}
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'sell'] = 1
+        return dataframe

freqtrade/templates/sample_hyperopt.py

@@ -0,0 +1,196 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class SampleHyperOpt(IHyperOpt):
+    """
+    This is a sample Hyperopt to inspire you.
+    Feel free to customize it.
+
+    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
+
+    You should:
+    - Rename the class name to some unique name.
+    - Add any methods you want to build your hyperopt.
+    - Add any lib you need to build your hyperopt.
+
+    You must keep:
+    - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
+
+    The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
+    copied in every custom hyperopt. However, you may override them if you need the
+    'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
+    Sample implementation of these methods can be found in
+    https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
+    """
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            # GUARDS AND TRENDS
+            if 'mfi-enabled' in params and params['mfi-enabled']:
+                conditions.append(dataframe['mfi'] < params['mfi-value'])
+            if 'fastd-enabled' in params and params['fastd-enabled']:
+                conditions.append(dataframe['fastd'] < params['fastd-value'])
+            if 'adx-enabled' in params and params['adx-enabled']:
+                conditions.append(dataframe['adx'] > params['adx-value'])
+            if 'rsi-enabled' in params and params['rsi-enabled']:
+                conditions.append(dataframe['rsi'] < params['rsi-value'])
+
+            # TRIGGERS
+            if 'trigger' in params:
+                if params['trigger'] == 'bb_lower':
+                    conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
+                if params['trigger'] == 'macd_cross_signal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['macd'], dataframe['macdsignal']
+                    ))
+                if params['trigger'] == 'sar_reversal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['close'], dataframe['sar']
+                    ))
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching buy strategy parameters.
+        """
+        return [
+            Integer(10, 25, name='mfi-value'),
+            Integer(15, 45, name='fastd-value'),
+            Integer(20, 50, name='adx-value'),
+            Integer(20, 40, name='rsi-value'),
+            Categorical([True, False], name='mfi-enabled'),
+            Categorical([True, False], name='fastd-enabled'),
+            Categorical([True, False], name='adx-enabled'),
+            Categorical([True, False], name='rsi-enabled'),
+            Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by Hyperopt.
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Sell strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            # GUARDS AND TRENDS
+            if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
+                conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
+            if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
+                conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
+            if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
+                conditions.append(dataframe['adx'] < params['sell-adx-value'])
+            if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
+                conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
+
+            # TRIGGERS
+            if 'sell-trigger' in params:
+                if params['sell-trigger'] == 'sell-bb_upper':
+                    conditions.append(dataframe['close'] > dataframe['bb_upperband'])
+                if params['sell-trigger'] == 'sell-macd_cross_signal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['macdsignal'], dataframe['macd']
+                    ))
+                if params['sell-trigger'] == 'sell-sar_reversal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['sar'], dataframe['close']
+                    ))
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'sell'] = 1
+
+            return dataframe
+
+        return populate_sell_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters.
+        """
+        return [
+            Integer(75, 100, name='sell-mfi-value'),
+            Integer(50, 100, name='sell-fastd-value'),
+            Integer(50, 100, name='sell-adx-value'),
+            Integer(60, 100, name='sell-rsi-value'),
+            Categorical([True, False], name='sell-mfi-enabled'),
+            Categorical([True, False], name='sell-fastd-enabled'),
+            Categorical([True, False], name='sell-adx-enabled'),
+            Categorical([True, False], name='sell-rsi-enabled'),
+            Categorical(['sell-bb_upper',
+                         'sell-macd_cross_signal',
+                         'sell-sar_reversal'], name='sell-trigger')
+        ]
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators. Should be a copy of same method from strategy.
+        Must align to populate_indicators in this file.
+        Only used when --spaces does not include buy space.
+        """
+        dataframe.loc[
+            (
+                (dataframe['close'] < dataframe['bb_lowerband']) &
+                (dataframe['mfi'] < 16) &
+                (dataframe['adx'] > 25) &
+                (dataframe['rsi'] < 21)
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators. Should be a copy of same method from strategy.
+        Must align to populate_indicators in this file.
+        Only used when --spaces does not include sell space.
+        """
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(
+                    dataframe['macdsignal'], dataframe['macd']
+                )) &
+                (dataframe['fastd'] > 54)
+            ),
+            'sell'] = 1
+
+        return dataframe

freqtrade/templates/sample_hyperopt_advanced.py

@@ -0,0 +1,271 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class AdvancedSampleHyperOpt(IHyperOpt):
+    """
+    This is a sample hyperopt to inspire you.
+    Feel free to customize it.
+
+    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
+
+    You should:
+    - Rename the class name to some unique name.
+    - Add any methods you want to build your hyperopt.
+    - Add any lib you need to build your hyperopt.
+
+    You must keep:
+    - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
+
+    The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
+    copied in every custom hyperopt. However, you may override them if you need the
+    'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
+
+    This sample illustrates how to override these methods.
+    """
+    @staticmethod
+    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
+        """
+        dataframe['adx'] = ta.ADX(dataframe)
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['mfi'] = ta.MFI(dataframe)
+        dataframe['rsi'] = ta.RSI(dataframe)
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe['sar'] = ta.SAR(dataframe)
+        return dataframe
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by hyperopt
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use
+            """
+            conditions = []
+            # GUARDS AND TRENDS
+            if 'mfi-enabled' in params and params['mfi-enabled']:
+                conditions.append(dataframe['mfi'] < params['mfi-value'])
+            if 'fastd-enabled' in params and params['fastd-enabled']:
+                conditions.append(dataframe['fastd'] < params['fastd-value'])
+            if 'adx-enabled' in params and params['adx-enabled']:
+                conditions.append(dataframe['adx'] > params['adx-value'])
+            if 'rsi-enabled' in params and params['rsi-enabled']:
+                conditions.append(dataframe['rsi'] < params['rsi-value'])
+
+            # TRIGGERS
+            if 'trigger' in params:
+                if params['trigger'] == 'bb_lower':
+                    conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
+                if params['trigger'] == 'macd_cross_signal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['macd'], dataframe['macdsignal']
+                    ))
+                if params['trigger'] == 'sar_reversal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['close'], dataframe['sar']
+                    ))
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching strategy parameters
+        """
+        return [
+            Integer(10, 25, name='mfi-value'),
+            Integer(15, 45, name='fastd-value'),
+            Integer(20, 50, name='adx-value'),
+            Integer(20, 40, name='rsi-value'),
+            Categorical([True, False], name='mfi-enabled'),
+            Categorical([True, False], name='fastd-enabled'),
+            Categorical([True, False], name='adx-enabled'),
+            Categorical([True, False], name='rsi-enabled'),
+            Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by hyperopt
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Sell strategy Hyperopt will build and use
+            """
+            # print(params)
+            conditions = []
+            # GUARDS AND TRENDS
+            if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
+                conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
+            if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
+                conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
+            if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
+                conditions.append(dataframe['adx'] < params['sell-adx-value'])
+            if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
+                conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
+
+            # TRIGGERS
+            if 'sell-trigger' in params:
+                if params['sell-trigger'] == 'sell-bb_upper':
+                    conditions.append(dataframe['close'] > dataframe['bb_upperband'])
+                if params['sell-trigger'] == 'sell-macd_cross_signal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['macdsignal'], dataframe['macd']
+                    ))
+                if params['sell-trigger'] == 'sell-sar_reversal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['sar'], dataframe['close']
+                    ))
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'sell'] = 1
+
+            return dataframe
+
+        return populate_sell_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters
+        """
+        return [
+            Integer(75, 100, name='sell-mfi-value'),
+            Integer(50, 100, name='sell-fastd-value'),
+            Integer(50, 100, name='sell-adx-value'),
+            Integer(60, 100, name='sell-rsi-value'),
+            Categorical([True, False], name='sell-mfi-enabled'),
+            Categorical([True, False], name='sell-fastd-enabled'),
+            Categorical([True, False], name='sell-adx-enabled'),
+            Categorical([True, False], name='sell-rsi-enabled'),
+            Categorical(['sell-bb_upper',
+                         'sell-macd_cross_signal',
+                         'sell-sar_reversal'], name='sell-trigger')
+        ]
+
+    @staticmethod
+    def generate_roi_table(params: Dict) -> Dict[int, float]:
+        """
+        Generate the ROI table that will be used by Hyperopt
+
+        This implementation generates the default legacy Freqtrade ROI tables.
+
+        Change it if you need different number of steps in the generated
+        ROI tables or other structure of the ROI tables.
+
+        Please keep it aligned with parameters in the 'roi' optimization
+        hyperspace defined by the roi_space method.
+        """
+        roi_table = {}
+        roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
+        roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
+        roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
+        roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
+
+        return roi_table
+
+    @staticmethod
+    def roi_space() -> List[Dimension]:
+        """
+        Values to search for each ROI steps
+
+        Override it if you need some different ranges for the parameters in the
+        'roi' optimization hyperspace.
+
+        Please keep it aligned with the implementation of the
+        generate_roi_table method.
+        """
+        return [
+            Integer(10, 120, name='roi_t1'),
+            Integer(10, 60, name='roi_t2'),
+            Integer(10, 40, name='roi_t3'),
+            Real(0.01, 0.04, name='roi_p1'),
+            Real(0.01, 0.07, name='roi_p2'),
+            Real(0.01, 0.20, name='roi_p3'),
+        ]
+
+    @staticmethod
+    def stoploss_space() -> List[Dimension]:
+        """
+        Stoploss Value to search
+
+        Override it if you need some different range for the parameter in the
+        'stoploss' optimization hyperspace.
+        """
+        return [
+            Real(-0.5, -0.02, name='stoploss'),
+        ]
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators.
+        Can be a copy of the corresponding method from the strategy,
+        or will be loaded from the strategy.
+        Must align to populate_indicators used (either from this File, or from the strategy)
+        Only used when --spaces does not include buy
+        """
+        dataframe.loc[
+            (
+                (dataframe['close'] < dataframe['bb_lowerband']) &
+                (dataframe['mfi'] < 16) &
+                (dataframe['adx'] > 25) &
+                (dataframe['rsi'] < 21)
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators.
+        Can be a copy of the corresponding method from the strategy,
+        or will be loaded from the strategy.
+        Must align to populate_indicators used (either from this File, or from the strategy)
+        Only used when --spaces does not include sell
+        """
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(
+                    dataframe['macdsignal'], dataframe['macd']
+                )) &
+                (dataframe['fastd'] > 54)
+            ),
+            'sell'] = 1
+        return dataframe

freqtrade/templates/sample_hyperopt_loss.py

@@ -0,0 +1,47 @@
+from math import exp
+from datetime import datetime
+
+from pandas import DataFrame
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+# Define some constants:
+
+# set TARGET_TRADES to suit your number concurrent trades so its realistic
+# to the number of days
+TARGET_TRADES = 600
+# This is assumed to be expected avg profit * expected trade count.
+# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
+# self.expected_max_profit = 3.85
+# Check that the reported Σ% values do not exceed this!
+# Note, this is ratio. 3.85 stated above means 385Σ%.
+EXPECTED_MAX_PROFIT = 3.0
+
+# max average trade duration in minutes
+# if eval ends with higher value, we consider it a failed eval
+MAX_ACCEPTED_TRADE_DURATION = 300
+
+
+class SampleHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the default loss function for hyperopt
+    This is intended to give you some inspiration for your own loss function.
+
+    The Function needs to return a number (float) - which becomes for better backtest results.
+    """
+
+    @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 better results
+        """
+        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)
+        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
+        result = trade_loss + profit_loss + duration_loss
+        return result

freqtrade/templates/sample_strategy.py

@@ -0,0 +1,303 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+
+from freqtrade.strategy.interface import IStrategy
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+# This class is a sample. Feel free to customize it.
+class SampleStrategy(IStrategy):
+    """
+    This is a sample strategy to inspire you.
+    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
+
+    You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
+    populate_sell_trend, hyperopt_space, buy_strategy_generator
+    """
+    # Strategy interface version - allow new iterations of the strategy interface.
+    # Check the documentation or the Sample strategy to get the latest version.
+    INTERFACE_VERSION = 2
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi".
+    minimal_roi = {
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy.
+    # This attribute will be overridden if the config file contains "stoploss".
+    stoploss = -0.10
+
+    # Trailing stoploss
+    trailing_stop = False
+    # trailing_stop_positive = 0.01
+    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
+
+    # Optimal ticker interval for the strategy.
+    ticker_interval = '5m'
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = False
+
+    # These values can be overridden in the "ask_strategy" section in the config.
+    use_sell_signal = True
+    sell_profit_only = False
+    ignore_roi_if_buy_signal = False
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
+    # Optional order type mapping.
+    order_types = {
+        'buy': 'limit',
+        'sell': 'limit',
+        'stoploss': 'market',
+        'stoploss_on_exchange': False
+    }
+
+    # Optional order time in force.
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc'
+    }
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+        # Momentum Indicators
+        # ------------------------------------
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        # # Aroon, Aroon Oscillator
+        # aroon = ta.AROON(dataframe)
+        # dataframe['aroonup'] = aroon['aroonup']
+        # dataframe['aroondown'] = aroon['aroondown']
+        # dataframe['aroonosc'] = ta.AROONOSC(dataframe)
+
+        # # Awesome oscillator
+        # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+        # dataframe['cci'] = ta.CCI(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # MFI
+        dataframe['mfi'] = ta.MFI(dataframe)
+
+        # # Minus Directional Indicator / Movement
+        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # # Plus Directional Indicator / Movement
+        # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        # dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # # ROC
+        # dataframe['roc'] = ta.ROC(dataframe)
+
+        # # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        # rsi = 0.1 * (dataframe['rsi'] - 50)
+        # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
+
+        # # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # # Stoch
+        # stoch = ta.STOCH(dataframe)
+        # dataframe['slowd'] = stoch['slowd']
+        # dataframe['slowk'] = stoch['slowk']
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # # Stoch RSI
+        # stoch_rsi = ta.STOCHRSI(dataframe)
+        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        # dataframe['fastk_rsi'] = stoch_rsi['fastk']
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+
+        # # EMA - Exponential Moving Average
+        # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # # SMA - Simple Moving Average
+        # dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+
+        # SAR Parabol
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe['htsine'] = hilbert['sine']
+        dataframe['htleadsine'] = hilbert['leadsine']
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        # # Hammer: values [0, 100]
+        # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # # Inverted Hammer: values [0, 100]
+        # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # # Dragonfly Doji: values [0, 100]
+        # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # # Piercing Line: values [0, 100]
+        # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # # Morningstar: values [0, 100]
+        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # # Three White Soldiers: values [0, 100]
+        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        # # Hanging Man: values [0, 100]
+        # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # # Shooting Star: values [0, 100]
+        # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # # Gravestone Doji: values [0, 100]
+        # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # # Dark Cloud Cover: values [0, 100]
+        # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # # Evening Doji Star: values [0, 100]
+        # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # # Evening Star: values [0, 100]
+        # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        # # Three Line Strike: values [0, -100, 100]
+        # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # # Spinning Top: values [0, -100, 100]
+        # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # # Engulfing: values [0, -100, 100]
+        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # # Harami: values [0, -100, 100]
+        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # # Three Outside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # # Three Inside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+
+        # # Chart type
+        # # ------------------------------------
+        # # Heikinashi stategy
+        # heikinashi = qtpylib.heikinashi(dataframe)
+        # dataframe['ha_open'] = heikinashi['open']
+        # dataframe['ha_close'] = heikinashi['close']
+        # dataframe['ha_high'] = heikinashi['high']
+        # dataframe['ha_low'] = heikinashi['low']
+
+        # Retrieve best bid and best ask from the orderbook
+        # ------------------------------------
+        """
+        # first check if dataprovider is available
+        if self.dp:
+            if self.dp.runmode in ('live', 'dry_run'):
+                ob = self.dp.orderbook(metadata['pair'], 1)
+                dataframe['best_bid'] = ob['bids'][0][0]
+                dataframe['best_ask'] = ob['asks'][0][0]
+        """
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+                (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
+                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'sell'] = 1
+        return dataframe

freqtrade/templates/strategy_analysis_example.ipynb

@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Strategy analysis example\n",
+    "\n",
+    "Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "# Customize these according to your needs.\n",
+    "\n",
+    "# Define some constants\n",
+    "timeframe = \"5m\"\n",
+    "# Name of the strategy class\n",
+    "strategy_name = 'SampleStrategy'\n",
+    "# Path to user data\n",
+    "user_data_dir = Path('user_data')\n",
+    "# Location of the strategy\n",
+    "strategy_location = user_data_dir / 'strategies'\n",
+    "# Location of the data\n",
+    "data_location = Path(user_data_dir, 'data', 'binance')\n",
+    "# Pair to analyze - Only use one pair here\n",
+    "pair = \"BTC_USDT\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data using values set above\n",
+    "from freqtrade.data.history import load_pair_history\n",
+    "\n",
+    "candles = load_pair_history(datadir=data_location,\n",
+    "                            timeframe=timeframe,\n",
+    "                            pair=pair)\n",
+    "\n",
+    "# Confirm success\n",
+    "print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
+    "candles.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load and run strategy\n",
+    "* Rerun each time the strategy file is changed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load strategy using values set above\n",
+    "from freqtrade.resolvers import StrategyResolver\n",
+    "strategy = StrategyResolver({'strategy': strategy_name,\n",
+    "                            'user_data_dir': user_data_dir,\n",
+    "                            'strategy_path': strategy_location}).strategy\n",
+    "\n",
+    "# Generate buy/sell signals using strategy\n",
+    "df = strategy.analyze_ticker(candles, {'pair': pair})\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display the trade details\n",
+    "\n",
+    "* Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
+    "* Some possible problems\n",
+    "    * Columns with NaN values at the end of the dataframe\n",
+    "    * Columns used in `crossed*()` functions with completely different units\n",
+    "* Comparison with full backtest\n",
+    "    * having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.\n",
+    "    * Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple \"buy\" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot (\"max_open_trades\") is still available), or on one of the middle signals, as soon as a \"slot\" becomes available.  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Report results\n",
+    "print(f\"Generated {df['buy'].sum()} buy signals\")\n",
+    "data = df.set_index('date', drop=False)\n",
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load existing objects into a Jupyter notebook\n",
+    "\n",
+    "The following cells assume that you have already generated data using the cli.  \n",
+    "They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load backtest results to pandas dataframe\n",
+    "\n",
+    "Analyze a trades dataframe (also used below for plotting)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.data.btanalysis import load_backtest_data\n",
+    "\n",
+    "# Load backtest results\n",
+    "trades = load_backtest_data(user_data_dir / \"backtest_results/backtest-result.json\")\n",
+    "\n",
+    "# Show value-counts per pair\n",
+    "trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load live trading results into a pandas dataframe\n",
+    "\n",
+    "In case you did already some trading and want to analyze your performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.data.btanalysis import load_trades_from_db\n",
+    "\n",
+    "# Fetch trades from database\n",
+    "trades = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
+    "\n",
+    "# Display results\n",
+    "trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyze the loaded trades for trade parallelism\n",
+    "This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.\n",
+    "\n",
+    "`analyze_trade_parallelism()` returns a timeseries dataframe with an \"open_trades\" column, specifying the number of open trades for each candle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.data.btanalysis import analyze_trade_parallelism\n",
+    "\n",
+    "# Analyze the above\n",
+    "parallel_trades = analyze_trade_parallelism(trades, '5m')\n",
+    "\n",
+    "\n",
+    "parallel_trades.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot results\n",
+    "\n",
+    "Freqtrade offers interactive plotting capabilities based on plotly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.plot.plotting import  generate_candlestick_graph\n",
+    "# Limit graph period to keep plotly quick and reactive\n",
+    "\n",
+    "data_red = data['2019-06-01':'2019-06-10']\n",
+    "# Generate candlestick graph\n",
+    "graph = generate_candlestick_graph(pair=pair,\n",
+    "                                   data=data_red,\n",
+    "                                   trades=trades,\n",
+    "                                   indicators1=['sma20', 'ema50', 'ema55'],\n",
+    "                                   indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']\n",
+    "                                  )\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show graph inline\n",
+    "# graph.show()\n",
+    "\n",
+    "# Render graph in a seperate window\n",
+    "graph.show(renderer=\"browser\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data."
+   ]
+  }
+ ],
+ "metadata": {
+  "file_extension": ".py",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "mimetype": "text/x-python",
+  "name": "python",
+  "npconvert_exporter": "python",
+  "pygments_lexer": "ipython3",
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  },
+  "version": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

freqtrade/templates/subtemplates/buy_trend_full.j2

@@ -0,0 +1,3 @@
+(qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+(dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
+(dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising

freqtrade/templates/subtemplates/buy_trend_minimal.j2

@@ -0,0 +1 @@
+(qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30

freqtrade/templates/subtemplates/hyperopt_buy_guards_full.j2

@@ -0,0 +1,8 @@
+if params.get('mfi-enabled'):
+    conditions.append(dataframe['mfi'] < params['mfi-value'])
+if params.get('fastd-enabled'):
+    conditions.append(dataframe['fastd'] < params['fastd-value'])
+if params.get('adx-enabled'):
+    conditions.append(dataframe['adx'] > params['adx-value'])
+if params.get('rsi-enabled'):
+    conditions.append(dataframe['rsi'] < params['rsi-value'])

freqtrade/templates/subtemplates/hyperopt_buy_guards_minimal.j2

@@ -0,0 +1,2 @@
+if params.get('rsi-enabled'):
+    conditions.append(dataframe['rsi'] < params['rsi-value'])

freqtrade/templates/subtemplates/hyperopt_buy_space_full.j2

@@ -0,0 +1,9 @@
+Integer(10, 25, name='mfi-value'),
+Integer(15, 45, name='fastd-value'),
+Integer(20, 50, name='adx-value'),
+Integer(20, 40, name='rsi-value'),
+Categorical([True, False], name='mfi-enabled'),
+Categorical([True, False], name='fastd-enabled'),
+Categorical([True, False], name='adx-enabled'),
+Categorical([True, False], name='rsi-enabled'),
+Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

freqtrade/templates/subtemplates/hyperopt_buy_space_minimal.j2

@@ -0,0 +1,3 @@
+Integer(20, 40, name='rsi-value'),
+Categorical([True, False], name='rsi-enabled'),
+Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

freqtrade/templates/subtemplates/hyperopt_sell_guards_full.j2

@@ -0,0 +1,8 @@
+if params.get('sell-mfi-enabled'):
+    conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
+if params.get('sell-fastd-enabled'):
+    conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
+if params.get('sell-adx-enabled'):
+    conditions.append(dataframe['adx'] < params['sell-adx-value'])
+if params.get('sell-rsi-enabled'):
+    conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

freqtrade/templates/subtemplates/hyperopt_sell_guards_minimal.j2

@@ -0,0 +1,2 @@
+if params.get('sell-rsi-enabled'):
+    conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

freqtrade/templates/subtemplates/hyperopt_sell_space_full.j2

@@ -0,0 +1,11 @@
+Integer(75, 100, name='sell-mfi-value'),
+Integer(50, 100, name='sell-fastd-value'),
+Integer(50, 100, name='sell-adx-value'),
+Integer(60, 100, name='sell-rsi-value'),
+Categorical([True, False], name='sell-mfi-enabled'),
+Categorical([True, False], name='sell-fastd-enabled'),
+Categorical([True, False], name='sell-adx-enabled'),
+Categorical([True, False], name='sell-rsi-enabled'),
+Categorical(['sell-bb_upper',
+             'sell-macd_cross_signal',
+             'sell-sar_reversal'], name='sell-trigger')

freqtrade/templates/subtemplates/hyperopt_sell_space_minimal.j2

@@ -0,0 +1,5 @@
+Integer(60, 100, name='sell-rsi-value'),
+Categorical([True, False], name='sell-rsi-enabled'),
+Categorical(['sell-bb_upper',
+             'sell-macd_cross_signal',
+             'sell-sar_reversal'], name='sell-trigger')

freqtrade/templates/subtemplates/indicators_full.j2

@@ -0,0 +1,161 @@
+
+# Momentum Indicators
+# ------------------------------------
+
+# RSI
+dataframe['rsi'] = ta.RSI(dataframe)
+
+# ADX
+dataframe['adx'] = ta.ADX(dataframe)
+
+# # Aroon, Aroon Oscillator
+# aroon = ta.AROON(dataframe)
+# dataframe['aroonup'] = aroon['aroonup']
+# dataframe['aroondown'] = aroon['aroondown']
+# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
+
+# # Awesome oscillator
+# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+# dataframe['cci'] = ta.CCI(dataframe)
+
+# MACD
+macd = ta.MACD(dataframe)
+dataframe['macd'] = macd['macd']
+dataframe['macdsignal'] = macd['macdsignal']
+dataframe['macdhist'] = macd['macdhist']
+
+# MFI
+dataframe['mfi'] = ta.MFI(dataframe)
+
+# # Minus Directional Indicator / Movement
+# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+# # Plus Directional Indicator / Movement
+# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+# # ROC
+# dataframe['roc'] = ta.ROC(dataframe)
+
+# # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+# rsi = 0.1 * (dataframe['rsi'] - 50)
+# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
+
+# # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+# # Stoch
+# stoch = ta.STOCH(dataframe)
+# dataframe['slowd'] = stoch['slowd']
+# dataframe['slowk'] = stoch['slowk']
+
+# Stoch fast
+stoch_fast = ta.STOCHF(dataframe)
+dataframe['fastd'] = stoch_fast['fastd']
+dataframe['fastk'] = stoch_fast['fastk']
+
+# # Stoch RSI
+# stoch_rsi = ta.STOCHRSI(dataframe)
+# dataframe['fastd_rsi'] = stoch_rsi['fastd']
+# dataframe['fastk_rsi'] = stoch_rsi['fastk']
+
+# Overlap Studies
+# ------------------------------------
+
+# Bollinger bands
+bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+dataframe['bb_lowerband'] = bollinger['lower']
+dataframe['bb_middleband'] = bollinger['mid']
+dataframe['bb_upperband'] = bollinger['upper']
+
+# # EMA - Exponential Moving Average
+# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+# # SMA - Simple Moving Average
+# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+
+# SAR Parabol
+dataframe['sar'] = ta.SAR(dataframe)
+
+# TEMA - Triple Exponential Moving Average
+dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+# Cycle Indicator
+# ------------------------------------
+# Hilbert Transform Indicator - SineWave
+hilbert = ta.HT_SINE(dataframe)
+dataframe['htsine'] = hilbert['sine']
+dataframe['htleadsine'] = hilbert['leadsine']
+
+# Pattern Recognition - Bullish candlestick patterns
+# ------------------------------------
+# # Hammer: values [0, 100]
+# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+# # Inverted Hammer: values [0, 100]
+# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+# # Dragonfly Doji: values [0, 100]
+# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+# # Piercing Line: values [0, 100]
+# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+# # Morningstar: values [0, 100]
+# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+# # Three White Soldiers: values [0, 100]
+# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+
+# Pattern Recognition - Bearish candlestick patterns
+# ------------------------------------
+# # Hanging Man: values [0, 100]
+# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+# # Shooting Star: values [0, 100]
+# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+# # Gravestone Doji: values [0, 100]
+# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+# # Dark Cloud Cover: values [0, 100]
+# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+# # Evening Doji Star: values [0, 100]
+# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+# # Evening Star: values [0, 100]
+# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+
+# Pattern Recognition - Bullish/Bearish candlestick patterns
+# ------------------------------------
+# # Three Line Strike: values [0, -100, 100]
+# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+# # Spinning Top: values [0, -100, 100]
+# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+# # Engulfing: values [0, -100, 100]
+# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+# # Harami: values [0, -100, 100]
+# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+# # Three Outside Up/Down: values [0, -100, 100]
+# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+# # Three Inside Up/Down: values [0, -100, 100]
+# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+
+# # Chart type
+# # ------------------------------------
+# # Heikinashi stategy
+# heikinashi = qtpylib.heikinashi(dataframe)
+# dataframe['ha_open'] = heikinashi['open']
+# dataframe['ha_close'] = heikinashi['close']
+# dataframe['ha_high'] = heikinashi['high']
+# dataframe['ha_low'] = heikinashi['low']
+
+# Retrieve best bid and best ask from the orderbook
+# ------------------------------------
+"""
+# first check if dataprovider is available
+if self.dp:
+    if self.dp.runmode in ('live', 'dry_run'):
+        ob = self.dp.orderbook(metadata['pair'], 1)
+        dataframe['best_bid'] = ob['bids'][0][0]
+        dataframe['best_ask'] = ob['asks'][0][0]
+"""

freqtrade/templates/subtemplates/indicators_minimal.j2

@@ -0,0 +1,17 @@
+
+# Momentum Indicators
+# ------------------------------------
+
+# RSI
+dataframe['rsi'] = ta.RSI(dataframe)
+
+# Retrieve best bid and best ask from the orderbook
+# ------------------------------------
+"""
+# first check if dataprovider is available
+if self.dp:
+    if self.dp.runmode in ('live', 'dry_run'):
+        ob = self.dp.orderbook(metadata['pair'], 1)
+        dataframe['best_bid'] = ob['bids'][0][0]
+        dataframe['best_ask'] = ob['asks'][0][0]
+"""

freqtrade/templates/subtemplates/sell_trend_full.j2

@@ -0,0 +1,3 @@
+(qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+(dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
+(dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling

freqtrade/templates/subtemplates/sell_trend_minimal.j2

@@ -0,0 +1 @@
+(qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70

freqtrade/utils.py

@@ -1,3 +1,4 @@
+import csv
 import logging
 import sys
 from collections import OrderedDict
@@ -5,19 +6,21 @@ from pathlib import Path
 from typing import Any, Dict, List
 
 import arrow
-import csv
 import rapidjson
 from tabulate import tabulate
 
 from freqtrade import OperationalException
-from freqtrade.configuration import Configuration, TimeRange, remove_credentials
-from freqtrade.configuration.directory_operations import create_userdata_dir
+from freqtrade.configuration import (Configuration, TimeRange,
+                                     remove_credentials)
+from freqtrade.configuration.directory_operations import (copy_sample_files,
+                                                          create_userdata_dir)
+from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGY
 from freqtrade.data.history import (convert_trades_to_ohlcv,
                                     refresh_backtest_ohlcv_data,
                                     refresh_backtest_trades_data)
-from freqtrade.exchange import (available_exchanges, ccxt_exchanges, market_is_active,
-                                symbol_is_pair)
-from freqtrade.misc import plural
+from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
+                                market_is_active, symbol_is_pair)
+from freqtrade.misc import plural, render_template
 from freqtrade.resolvers import ExchangeResolver
 from freqtrade.state import RunMode
 
@@ -81,12 +84,95 @@ def start_create_userdir(args: Dict[str, Any]) -> None:
     :return: None
     """
     if "user_data_dir" in args and args["user_data_dir"]:
-        create_userdata_dir(args["user_data_dir"], create_dir=True)
+        userdir = create_userdata_dir(args["user_data_dir"], create_dir=True)
+        copy_sample_files(userdir, overwrite=args["reset"])
     else:
         logger.warning("`create-userdir` requires --userdir to be set.")
         sys.exit(1)
 
 
+def deploy_new_strategy(strategy_name, strategy_path: Path, subtemplate: str):
+    """
+    Deploy new strategy from template to strategy_path
+    """
+    indicators = render_template(templatefile=f"subtemplates/indicators_{subtemplate}.j2",)
+    buy_trend = render_template(templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",)
+    sell_trend = render_template(templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",)
+
+    strategy_text = render_template(templatefile='base_strategy.py.j2',
+                                    arguments={"strategy": strategy_name,
+                                               "indicators": indicators,
+                                               "buy_trend": buy_trend,
+                                               "sell_trend": sell_trend,
+                                               })
+
+    logger.info(f"Writing strategy to `{strategy_path}`.")
+    strategy_path.write_text(strategy_text)
+
+
+def start_new_strategy(args: Dict[str, Any]) -> None:
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if "strategy" in args and args["strategy"]:
+        if args["strategy"] == "DefaultStrategy":
+            raise OperationalException("DefaultStrategy is not allowed as name.")
+
+        new_path = config['user_data_dir'] / USERPATH_STRATEGY / (args["strategy"] + ".py")
+
+        if new_path.exists():
+            raise OperationalException(f"`{new_path}` already exists. "
+                                       "Please choose another Strategy Name.")
+
+        deploy_new_strategy(args['strategy'], new_path, args['template'])
+
+    else:
+        raise OperationalException("`new-strategy` requires --strategy to be set.")
+
+
+def deploy_new_hyperopt(hyperopt_name, hyperopt_path: Path, subtemplate: str):
+    """
+    Deploys a new hyperopt template to hyperopt_path
+    """
+    buy_guards = render_template(
+        templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",)
+    sell_guards = render_template(
+        templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",)
+    buy_space = render_template(
+        templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",)
+    sell_space = render_template(
+        templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",)
+
+    strategy_text = render_template(templatefile='base_hyperopt.py.j2',
+                                    arguments={"hyperopt": hyperopt_name,
+                                               "buy_guards": buy_guards,
+                                               "sell_guards": sell_guards,
+                                               "buy_space": buy_space,
+                                               "sell_space": sell_space,
+                                               })
+
+    logger.info(f"Writing hyperopt to `{hyperopt_path}`.")
+    hyperopt_path.write_text(strategy_text)
+
+
+def start_new_hyperopt(args: Dict[str, Any]) -> None:
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if "hyperopt" in args and args["hyperopt"]:
+        if args["hyperopt"] == "DefaultHyperopt":
+            raise OperationalException("DefaultHyperopt is not allowed as name.")
+
+        new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args["hyperopt"] + ".py")
+
+        if new_path.exists():
+            raise OperationalException(f"`{new_path}` already exists. "
+                                       "Please choose another Strategy Name.")
+        deploy_new_hyperopt(args['hyperopt'], new_path, args['template'])
+    else:
+        raise OperationalException("`new-hyperopt` requires --hyperopt to be set.")
+
+
 def start_download_data(args: Dict[str, Any]) -> None:
     """
     Download data (former download_backtest_data.py script)

freqtrade/wallets.py

@@ -2,7 +2,7 @@
 """ Wallet """
 
 import logging
-from typing import Dict, NamedTuple
+from typing import Dict, NamedTuple, Any
 from freqtrade.exchange import Exchange
 from freqtrade import constants
 
@@ -72,3 +72,6 @@ class Wallets:
             )
 
         logger.info('Wallets synced.')
+
+    def get_all_balances(self) -> Dict[str, Any]:
+        return self._wallets