Revert "Merge branch 'plot_hyperopt_stats' into opt-ask-force-new-points"

This reverts commit 4eb9cc6e8b, reversing
changes made to a3b401a762.

.github/workflows/ci.yml

@@ -31,14 +31,14 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache_dependencies
-      uses: actions/cache@v3
+      uses: actions/cache@v2
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (linux)
-      uses: actions/cache@v3
+      uses: actions/cache@v2
       if: runner.os == 'Linux'
       with:
         path: ~/.cache/pip
@@ -126,14 +126,14 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache_dependencies
-      uses: actions/cache@v3
+      uses: actions/cache@v2
       id: cache
       with:
         path: ~/dependencies/
         key: ${{ runner.os }}-dependencies
 
     - name: pip cache (macOS)
-      uses: actions/cache@v3
+      uses: actions/cache@v2
       if: runner.os == 'macOS'
       with:
         path: ~/Library/Caches/pip
@@ -218,7 +218,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Pip cache (Windows)
-      uses: actions/cache@v3
+      uses: actions/cache@preview
       with:
         path: ~\AppData\Local\pip\Cache
         key: ${{ matrix.os }}-${{ matrix.python-version }}-pip

.github/workflows/docker_update_readme.yml

@@ -10,7 +10,7 @@ jobs:
     steps:
     - uses: actions/checkout@v3
     - name: Docker Hub Description
-      uses: peter-evans/dockerhub-description@v3
+      uses: peter-evans/dockerhub-description@v2.4.3
       env:
         DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
         DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}

docs/includes/pricing.md

@@ -51,9 +51,9 @@ When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Fre
 
 #### Buy price without Orderbook enabled
 
-The following section uses `side` as the configured `bid_strategy.price_side` (defaults to `"bid"`).
+The following section uses `side` as the configured `bid_strategy.price_side`.
 
-When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price based on `bid_strategy.ask_last_balance`..
+When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
 
 The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
 
@@ -88,9 +88,9 @@ When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Fr
 
 #### Sell price without Orderbook enabled
 
-The following section uses `side` as the configured `ask_strategy.price_side` (defaults to `"ask"`).
+When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
 
-When not using orderbook (`ask_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's above the `last` traded price from the ticker. Otherwise (when the `side` price is below the `last` price), it calculates a rate between `side` and `last` price based on `ask_strategy.bid_last_balance`.
+When not using orderbook (`ask_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
 
 The `ask_strategy.bid_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price.
 

docs/requirements-docs.txt

@@ -1,5 +1,4 @@
-mkdocs==1.3.0
+mkdocs==1.2.3
-mkdocs-material==8.2.8
+mkdocs-material==8.2.5
 mdx_truly_sane_lists==1.2
-pymdown-extensions==9.3
+pymdown-extensions==9.2
-jinja2==3.1.1

docs/strategy-advanced.md

@@ -146,7 +146,7 @@ def version(self) -> str:
 
 The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
 
-``` python title="user_data/strategies/myawesomestrategy.py"
+``` python
 class MyAwesomeStrategy(IStrategy):
     ...
     stoploss = 0.13
@@ -155,10 +155,6 @@ class MyAwesomeStrategy(IStrategy):
     # should be in any custom strategy...
     ...
 
-```
-
-``` python title="user_data/strategies/MyAwesomeStrategy2.py"
-from myawesomestrategy import MyAwesomeStrategy
 class MyAwesomeStrategy2(MyAwesomeStrategy):
     # Override something
     stoploss = 0.08
@@ -167,7 +163,16 @@ class MyAwesomeStrategy2(MyAwesomeStrategy):
 
 Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.
 
-While keeping the subclass in the same file is technically possible, it can lead to some problems with hyperopt parameter files, we therefore recommend to use separate strategy files, and import the parent strategy as shown above.
+!!! Note "Parent-strategy in different files"
+    If you have the parent-strategy in a different file, you'll need to add the following to the top of your "child"-file to ensure proper loading, otherwise freqtrade may not be able to load the parent strategy correctly.
+
+    ``` python
+    import sys
+    from pathlib import Path
+    sys.path.append(str(Path(__file__).parent))
+
+    from myawesomestrategy import MyAwesomeStrategy
+    ```
 
 ## Embedding Strategies
 

freqtrade/__init__.py

@@ -1,14 +1,27 @@
 """ Freqtrade bot """
 __version__ = 'develop'
 
-if 'dev' in __version__:
+if __version__ == 'develop':
+
     try:
         import subprocess
 
-        __version__ = __version__ + '-' + subprocess.check_output(
+        __version__ = 'develop-' + subprocess.check_output(
             ['git', 'log', '--format="%h"', '-n 1'],
             stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
 
+        # from datetime import datetime
+        # last_release = subprocess.check_output(
+        #     ['git', 'tag']
+        # ).decode('utf-8').split()[-1].split(".")
+        # # Releases are in the format "2020.1" - we increment the latest version for dev.
+        # prefix = f"{last_release[0]}.{int(last_release[1]) + 1}"
+        # dev_version = int(datetime.now().timestamp() // 1000)
+        # __version__ = f"{prefix}.dev{dev_version}"
+
+        #  subprocess.check_output(
+        #     ['git', 'log', '--format="%h"', '-n 1'],
+        #     stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
     except Exception:  # pragma: no cover
         # git not available, ignore
         try:

freqtrade/optimize/hyperopt.py

@@ -32,24 +32,20 @@ from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F4
 from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
 from freqtrade.optimize.optimize_reports import generate_strategy_stats
 from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
-import matplotlib.pyplot as plt
+
-import numpy as np
-import random
 
 # Suppress scikit-learn FutureWarnings from skopt
 with warnings.catch_warnings():
     warnings.filterwarnings("ignore", category=FutureWarning)
     from skopt import Optimizer
     from skopt.space import Dimension
-    from sklearn.model_selection import cross_val_score  
-    from skopt.plots import plot_convergence, plot_regret, plot_evaluations, plot_objective 
 
 progressbar.streams.wrap_stderr()
 progressbar.streams.wrap_stdout()
 logger = logging.getLogger(__name__)
 
 
-INITIAL_POINTS = 32
+INITIAL_POINTS = 30
 
 # Keep no more than SKOPT_MODEL_QUEUE_SIZE models
 # in the skopt model queue, to optimize memory consumption
@@ -414,35 +410,6 @@ class Hyperopt:
         # Store non-trimmed data - will be trimmed after signal generation.
         dump(preprocessed, self.data_pickle_file)
 
-    def get_asked_points(self, n_points: int) -> List[List[Any]]:
-        '''
-        Enforce points returned from `self.opt.ask` have not been already evaluated
-
-        Steps:
-        1. Try to get points using `self.opt.ask` first
-        2. Discard the points that have already been evaluated
-        3. Retry using `self.opt.ask` up to 3 times
-        4. If still some points are missing in respect to `n_points`, random sample some points
-        5. Repeat until at least `n_points` points in the `asked_non_tried` list
-        6. Return a list with length truncated at `n_points`
-        '''
-        i = 0
-        asked_non_tried: List[List[Any]] = []
-        while i < 100 and len(asked_non_tried) < n_points:
-            if i < 3:
-                self.opt.cache_ = {}
-                asked = self.opt.ask(n_points=n_points * 5)
-            else:
-                asked = self.opt.space.rvs(n_samples=n_points * 5)
-            asked_non_tried += [x for x in asked
-                                if x not in self.opt.Xi
-                                and x not in asked_non_tried]
-            i += 1
-        if asked_non_tried:
-            return asked_non_tried[:min(len(asked_non_tried), n_points)]
-        else:
-            return self.opt.ask(n_points=n_points)
-
     def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
         '''
         Enforce points returned from `self.opt.ask` have not been already evaluated
@@ -548,13 +515,7 @@ class Hyperopt:
 
                         asked, is_random = self.get_asked_points(n_points=current_jobs)
                         f_val = self.run_optimizer_parallel(parallel, asked, i)
-                        res = self.opt.tell(asked, [v['loss'] for v in f_val])
+                        self.opt.tell(asked, [v['loss'] for v in f_val])
-
-                        self.plot_optimizer(res, path='user_data/scripts', convergence=False, regret=False, r2=False, objective=True, jobs=jobs)
-
-                        if res.models and hasattr(res.models[-1], "kernel_"):
-                            print(f'kernel: {res.models[-1].kernel_}') 
-                        print(datetime.now())
 
                         # Calculate progressbar outputs
                         for j, val in enumerate(f_val):
@@ -600,47 +561,3 @@ class Hyperopt:
             # This is printed when Ctrl+C is pressed quickly, before first epochs have
             # a chance to be evaluated.
             print("No epochs evaluated yet, no best result.")
-
-    def plot_r2(self, res, ax, jobs):    
-        if len(res.x_iters) < 10:
-            return                  
-
-        if not hasattr(self, 'r2_list'):
-            self.r2_list = []
-
-        model = res.models[-1]
-        model.criterion = 'squared_error'
-
-        r2 = cross_val_score(model, X=res.x_iters, y=res.func_vals, scoring='r2', cv=5, n_jobs=jobs).mean()
-        r2 = r2 if r2 > -5 else -5
-        self.r2_list.append(r2)
-            
-        ax.plot(range(INITIAL_POINTS, INITIAL_POINTS + jobs * len(self.r2_list), jobs), self.r2_list, label='R2', marker=".", markersize=12, lw=2)
-
-    def plot_optimizer(self, res, path, jobs, convergence=True, regret=True, evaluations=True, objective=True, r2=True):
-        path = Path(path)
-        if convergence:
-            ax = plot_convergence(res)
-            ax.flatten()[0].figure.savefig(path / 'convergence.png')
-
-        if regret:
-            ax = plot_regret(res)
-            ax.flatten()[0].figure.savefig(path / 'regret.png')
-
-        if evaluations:
-#             print('evaluations')
-            ax = plot_evaluations(res)
-            ax.flatten()[0].figure.savefig(path / 'evaluations.png')
-
-        if objective and res.models:
-#             print('objective')
-            ax = plot_objective(res, sample_source='result', n_samples=50, n_points=10)
-            ax.flatten()[0].figure.savefig(path / 'objective.png')
-
-        if r2 and res.models:
-            fig, ax = plt.subplots()
-            ax.set_ylabel('R2')
-            ax.set_xlabel('Epoch')
-            ax.set_title('R2')        
-            ax = self.plot_r2(res, ax, jobs)
-            fig.savefig(path / 'r2.png')

freqtrade/resolvers/iresolver.py

@@ -6,7 +6,6 @@ This module load custom objects
 import importlib.util
 import inspect
 import logging
-import sys
 from pathlib import Path
 from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
 
@@ -16,22 +15,6 @@ from freqtrade.exceptions import OperationalException
 logger = logging.getLogger(__name__)
 
 
-class PathModifier:
-    def __init__(self, path: Path):
-        self.path = path
-
-    def __enter__(self):
-        """Inject path to allow importing with relative imports."""
-        sys.path.insert(0, str(self.path))
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        """Undo insertion of local path."""
-        str_path = str(self.path)
-        if str_path in sys.path:
-            sys.path.remove(str_path)
-
-
 class IResolver:
     """
     This class contains all the logic to load custom classes
@@ -74,9 +57,7 @@ class IResolver:
 
         # Generate spec based on absolute path
         # Pass object_name as first argument to have logging print a reasonable name.
-        with PathModifier(module_path.parent):
+        spec = importlib.util.spec_from_file_location(object_name or "", str(module_path))
-            module_name = module_path.stem or ""
-            spec = importlib.util.spec_from_file_location(module_name, str(module_path))
         if not spec:
             return iter([None])
 
@@ -94,11 +75,8 @@ class IResolver:
             name, obj in inspect.getmembers(
                 module, inspect.isclass) if ((object_name is None or object_name == name)
                                              and issubclass(obj, cls.object_type)
-                                                 and obj is not cls.object_type
+                                             and obj is not cls.object_type)
-                                                 and obj.__module__ == module_name
         )
-            )
-            # The __module__ check ensures we only use strategies that are defined in this folder.
         return valid_objects_gen
 
     @classmethod

requirements-dev.txt

@@ -6,9 +6,9 @@
 coveralls==3.3.1
 flake8==4.0.1
 flake8-tidy-imports==4.6.0
-mypy==0.942
+mypy==0.940
-pytest==7.1.1
+pytest==7.1.0
-pytest-asyncio==0.18.3
+pytest-asyncio==0.18.2
 pytest-cov==3.0.0
 pytest-mock==3.7.0
 pytest-random-order==1.0.4
@@ -22,8 +22,8 @@ nbconvert==6.4.4
 # mypy types
 types-cachetools==5.0.0
 types-filelock==3.2.5
-types-requests==2.27.15
+types-requests==2.27.12
-types-tabulate==0.8.6
+types-tabulate==0.8.5
 
 # Extensions to datetime library
-types-python-dateutil==2.8.10
+types-python-dateutil==2.8.9

requirements-hyperopt.txt

@@ -8,4 +8,3 @@ scikit-optimize==0.9.0
 filelock==3.6.0
 joblib==1.1.0
 progressbar2==4.0.0
-matplotlib

requirements.txt

@@ -2,22 +2,22 @@ numpy==1.22.3
 pandas==1.4.1
 pandas-ta==0.3.14b
 
-ccxt==1.77.36
+ccxt==1.76.5
 # Pin cryptography for now due to rust build errors with piwheels
-cryptography==36.0.2
+cryptography==36.0.1
 aiohttp==3.8.1
 SQLAlchemy==1.4.32
 python-telegram-bot==13.11
 arrow==1.2.2
 cachetools==4.2.2
 requests==2.27.1
-urllib3==1.26.9
+urllib3==1.26.8
 jsonschema==4.4.0
 TA-Lib==0.4.24
 technical==1.3.0
 tabulate==0.8.9
 pycoingecko==2.2.0
-jinja2==3.1.1
+jinja2==3.0.3
 tables==3.7.0
 blosc==1.10.6
 

tests/conftest.py

@@ -1019,8 +1019,8 @@ def limit_buy_order_open():
         'type': 'limit',
         'side': 'buy',
         'symbol': 'mocked',
-        'timestamp': arrow.utcnow().int_timestamp * 1000,
         'datetime': arrow.utcnow().isoformat(),
+        'timestamp': arrow.utcnow().int_timestamp,
         'price': 0.00001099,
         'amount': 90.99181073,
         'filled': 0.0,
@@ -1046,7 +1046,6 @@ def market_buy_order():
         'type': 'market',
         'side': 'buy',
         'symbol': 'mocked',
-        'timestamp': arrow.utcnow().int_timestamp * 1000,
         'datetime': arrow.utcnow().isoformat(),
         'price': 0.00004099,
         'amount': 91.99181073,
@@ -1063,7 +1062,6 @@ def market_sell_order():
         'type': 'market',
         'side': 'sell',
         'symbol': 'mocked',
-        'timestamp': arrow.utcnow().int_timestamp * 1000,
         'datetime': arrow.utcnow().isoformat(),
         'price': 0.00004173,
         'amount': 91.99181073,
@@ -1080,8 +1078,7 @@ def limit_buy_order_old():
         'type': 'limit',
         'side': 'buy',
         'symbol': 'mocked',
-        'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
+        'datetime': str(arrow.utcnow().shift(minutes=-601).datetime),
-        'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
         'price': 0.00001099,
         'amount': 90.99181073,
         'filled': 0.0,
@@ -1097,7 +1094,6 @@ def limit_sell_order_old():
         'type': 'limit',
         'side': 'sell',
         'symbol': 'ETH/BTC',
-        'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
         'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
         'price': 0.00001099,
         'amount': 90.99181073,
@@ -1114,7 +1110,6 @@ def limit_buy_order_old_partial():
         'type': 'limit',
         'side': 'buy',
         'symbol': 'ETH/BTC',
-        'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
         'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
         'price': 0.00001099,
         'amount': 90.99181073,
@@ -1144,7 +1139,7 @@ def limit_buy_order_canceled_empty(request):
             'info': {},
             'id': '1234512345',
             'clientOrderId': None,
-            'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
+            'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp,
             'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
             'lastTradeTimestamp': None,
             'symbol': 'LTC/USDT',
@@ -1165,7 +1160,7 @@ def limit_buy_order_canceled_empty(request):
             'info': {},
             'id': 'AZNPFF-4AC4N-7MKTAT',
             'clientOrderId': None,
-            'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
+            'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp,
             'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
             'lastTradeTimestamp': None,
             'status': 'canceled',
@@ -1186,7 +1181,7 @@ def limit_buy_order_canceled_empty(request):
             'info': {},
             'id': '1234512345',
             'clientOrderId': 'alb1234123',
-            'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
+            'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp,
             'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
             'lastTradeTimestamp': None,
             'symbol': 'LTC/USDT',
@@ -1207,7 +1202,7 @@ def limit_buy_order_canceled_empty(request):
             'info': {},
             'id': '1234512345',
             'clientOrderId': 'alb1234123',
-            'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
+            'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp,
             'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
             'lastTradeTimestamp': None,
             'symbol': 'LTC/USDT',
@@ -1233,7 +1228,7 @@ def limit_sell_order_open():
         'side': 'sell',
         'symbol': 'mocked',
         'datetime': arrow.utcnow().isoformat(),
-        'timestamp': arrow.utcnow().int_timestamp * 1000,
+        'timestamp': arrow.utcnow().int_timestamp,
         'price': 0.00001173,
         'amount': 90.99181073,
         'filled': 0.0,
@@ -1399,7 +1394,7 @@ def tickers():
         'BLK/BTC': {
             'symbol': 'BLK/BTC',
             'timestamp': 1522014806072,
-            'datetime': '2018-03-25T21:53:26.072Z',
+            'datetime': '2018-03-25T21:53:26.720Z',
             'high': 0.007745,
             'low': 0.007512,
             'bid': 0.007729,
@@ -1895,8 +1890,7 @@ def buy_order_fee():
         'type': 'limit',
         'side': 'buy',
         'symbol': 'mocked',
-        'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
+        'datetime': str(arrow.utcnow().shift(minutes=-601).datetime),
-        'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
         'price': 0.245441,
         'amount': 8.0,
         'cost': 1.963528,
@@ -2205,7 +2199,7 @@ def limit_buy_order_usdt_open():
         'side': 'buy',
         'symbol': 'mocked',
         'datetime': arrow.utcnow().isoformat(),
-        'timestamp': arrow.utcnow().int_timestamp * 1000,
+        'timestamp': arrow.utcnow().int_timestamp,
         'price': 2.00,
         'amount': 30.0,
         'filled': 0.0,
@@ -2232,7 +2226,7 @@ def limit_sell_order_usdt_open():
         'side': 'sell',
         'symbol': 'mocked',
         'datetime': arrow.utcnow().isoformat(),
-        'timestamp': arrow.utcnow().int_timestamp * 1000,
+        'timestamp': arrow.utcnow().int_timestamp,
         'price': 2.20,
         'amount': 30.0,
         'filled': 0.0,
@@ -2257,7 +2251,6 @@ def market_buy_order_usdt():
         'type': 'market',
         'side': 'buy',
         'symbol': 'mocked',
-        'timestamp': arrow.utcnow().int_timestamp * 1000,
         'datetime': arrow.utcnow().isoformat(),
         'price': 2.00,
         'amount': 30.0,
@@ -2314,7 +2307,6 @@ def market_sell_order_usdt():
         'type': 'market',
         'side': 'sell',
         'symbol': 'mocked',
-        'timestamp': arrow.utcnow().int_timestamp * 1000,
         'datetime': arrow.utcnow().isoformat(),
         'price': 2.20,
         'amount': 30.0,

tests/exchange/test_exchange.py

@@ -1098,7 +1098,7 @@ def test_create_order(default_conf, mocker, side, ordertype, rate, marketprice,
     exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
 
     order = exchange.create_order(
-        pair='ETH/BTC', ordertype=ordertype, side=side, amount=1, rate=rate)
+        pair='ETH/BTC', ordertype=ordertype, side=side, amount=1, rate=200)
 
     assert 'id' in order
     assert 'info' in order

tests/strategy/strats/hyperoptable_strategy.py

@@ -1,13 +1,14 @@
 # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
 
+import talib.abstract as ta
 from pandas import DataFrame
-from strategy_test_v2 import StrategyTestV2
 
 import freqtrade.vendor.qtpylib.indicators as qtpylib
-from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, RealParameter
+from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
+                                RealParameter)
 
 
-class HyperoptableStrategy(StrategyTestV2):
+class HyperoptableStrategy(IStrategy):
     """
     Default Strategy provided by freqtrade bot.
     Please do not modify this strategy, it's  intended for internal use only.
@@ -15,6 +16,38 @@ class HyperoptableStrategy(StrategyTestV2):
     or strategy repository https://github.com/freqtrade/freqtrade-strategies
     for samples and inspiration.
     """
+    INTERFACE_VERSION = 2
+
+    # Minimal ROI designed for the strategy
+    minimal_roi = {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.10
+
+    # Optimal ticker interval for the strategy
+    timeframe = '5m'
+
+    # Optional order type mapping
+    order_types = {
+        'buy': 'limit',
+        'sell': 'limit',
+        'stoploss': 'limit',
+        'stoploss_on_exchange': False
+    }
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
+    # Optional time in force for orders
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc',
+    }
 
     buy_params = {
         'buy_rsi': 35,
@@ -58,6 +91,55 @@ class HyperoptableStrategy(StrategyTestV2):
         """
         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: Dataframe with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+        # Momentum Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # Minus Directional Indicator / Movement
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # Plus Directional Indicator / Movement
+        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # 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['ema10'] = ta.EMA(dataframe, timeperiod=10)
+
+        return dataframe
+
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         Based on TA indicators, populates the buy signal for the given dataframe

tests/strategy/strats/strategy_test_v2.py

@@ -7,7 +7,7 @@ from pandas import DataFrame
 
 import freqtrade.vendor.qtpylib.indicators as qtpylib
 from freqtrade.persistence import Trade
-from freqtrade.strategy import IStrategy
+from freqtrade.strategy.interface import IStrategy
 
 
 class StrategyTestV2(IStrategy):