Merge pull request #2538 from freqtrade/strategy_template
new-strategy / new-hyperopt - from templates
This commit is contained in:
hroff-1902
GitHub
@@ -37,7 +37,11 @@ ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
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ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one_column",
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"print_csv", "base_currencies", "quote_currencies", "list_pairs_all"]
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ARGS_CREATE_USERDIR = ["user_data_dir"]
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ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
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ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
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ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
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ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
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"timeframes", "erase"]
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@@ -52,7 +56,7 @@ ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
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NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
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"plot-dataframe", "plot-profit"]
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NO_CONF_ALLOWED = ["create-userdir", "list-exchanges"]
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NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
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class Arguments:
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@@ -117,6 +121,7 @@ class Arguments:
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from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
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from freqtrade.utils import (start_create_userdir, start_download_data,
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start_list_exchanges, start_list_markets,
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start_new_hyperopt, start_new_strategy,
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start_list_timeframes, start_trading)
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from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
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@@ -158,6 +163,18 @@ class Arguments:
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create_userdir_cmd.set_defaults(func=start_create_userdir)
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self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
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# add new-strategy subcommand
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build_strategy_cmd = subparsers.add_parser('new-strategy',
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help="Create new strategy")
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build_strategy_cmd.set_defaults(func=start_new_strategy)
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self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
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# add new-hyperopt subcommand
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build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
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help="Create new hyperopt")
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build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
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self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
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# Add list-exchanges subcommand
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list_exchanges_cmd = subparsers.add_parser(
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'list-exchanges',
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@@ -62,6 +62,11 @@ AVAILABLE_CLI_OPTIONS = {
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help='Path to userdata directory.',
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metavar='PATH',
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),
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"reset": Arg(
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'--reset',
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help='Reset sample files to their original state.',
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action='store_true',
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),
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# Main options
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"strategy": Arg(
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'-s', '--strategy',
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@@ -334,6 +339,14 @@ AVAILABLE_CLI_OPTIONS = {
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help='Clean all existing data for the selected exchange/pairs/timeframes.',
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action='store_true',
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),
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# Templating options
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"template": Arg(
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'--template',
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help='Use a template which is either `minimal` or '
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'`full` (containing multiple sample indicators). Default: `%(default)s`.',
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choices=['full', 'minimal'],
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default='full',
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),
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# Plot dataframe
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"indicators1": Arg(
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'--indicators1',
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@@ -1,8 +1,10 @@
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import logging
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from typing import Any, Dict, Optional
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import shutil
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from pathlib import Path
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from typing import Any, Dict, Optional
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from freqtrade import OperationalException
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from freqtrade.constants import USER_DATA_FILES
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logger = logging.getLogger(__name__)
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@@ -31,7 +33,8 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
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:param create_dir: Create directory if it does not exist.
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:return: Path object containing the directory
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"""
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sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "plot", "strategies", ]
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sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
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"plot", "strategies", ]
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folder = Path(directory)
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if not folder.is_dir():
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if create_dir:
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@@ -48,3 +51,26 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
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if not subfolder.is_dir():
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subfolder.mkdir(parents=False)
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return folder
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def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
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"""
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Copy files from templates to User data directory.
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:param directory: Directory to copy data to
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:param overwrite: Overwrite existing sample files
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"""
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if not directory.is_dir():
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raise OperationalException(f"Directory `{directory}` does not exist.")
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sourcedir = Path(__file__).parents[1] / "templates"
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for source, target in USER_DATA_FILES.items():
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targetdir = directory / target
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if not targetdir.is_dir():
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raise OperationalException(f"Directory `{targetdir}` does not exist.")
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targetfile = targetdir / source
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if targetfile.exists():
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if not overwrite:
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logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
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continue
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else:
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logger.warning(f"File `{targetfile}` exists already, overwriting.")
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shutil.copy(str(sourcedir / source), str(targetfile))
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@@ -22,6 +22,18 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'PrecisionFilter', 'P
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DRY_RUN_WALLET = 999.9
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MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
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USERPATH_HYPEROPTS = 'hyperopts'
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USERPATH_STRATEGY = 'strategies'
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# Soure files with destination directories within user-directory
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USER_DATA_FILES = {
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'sample_strategy.py': USERPATH_STRATEGY,
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'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
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'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
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'sample_hyperopt.py': USERPATH_HYPEROPTS,
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'strategy_analysis_example.ipynb': 'notebooks',
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}
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TIMEFRAMES = [
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'1m', '3m', '5m', '15m', '30m',
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'1h', '2h', '4h', '6h', '8h', '12h',
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@@ -127,3 +127,16 @@ def round_dict(d, n):
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def plural(num, singular: str, plural: str = None) -> str:
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return singular if (num == 1 or num == -1) else plural or singular + 's'
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def render_template(templatefile: str, arguments: dict = {}):
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from jinja2 import Environment, PackageLoader, select_autoescape
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env = Environment(
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loader=PackageLoader('freqtrade', 'templates'),
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autoescape=select_autoescape(['html', 'xml'])
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)
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template = env.get_template(templatefile)
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return template.render(**arguments)
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@@ -8,7 +8,7 @@ from pathlib import Path
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from typing import Optional, Dict
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from freqtrade import OperationalException
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from freqtrade.constants import DEFAULT_HYPEROPT_LOSS
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from freqtrade.constants import DEFAULT_HYPEROPT_LOSS, USERPATH_HYPEROPTS
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
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from freqtrade.resolvers import IResolver
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@@ -58,7 +58,7 @@ class HyperOptResolver(IResolver):
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current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
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abs_paths = self.build_search_paths(config, current_path=current_path,
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user_subdir='hyperopts', extra_dir=extra_dir)
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user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
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hyperopt = self._load_object(paths=abs_paths, object_type=IHyperOpt,
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object_name=hyperopt_name, kwargs={'config': config})
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@@ -110,7 +110,7 @@ class HyperOptLossResolver(IResolver):
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current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
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abs_paths = self.build_search_paths(config, current_path=current_path,
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user_subdir='hyperopts', extra_dir=extra_dir)
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user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
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hyperoptloss = self._load_object(paths=abs_paths, object_type=IHyperOptLoss,
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object_name=hyper_loss_name)
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@@ -129,7 +129,8 @@ class StrategyResolver(IResolver):
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current_path = Path(__file__).parent.parent.joinpath('strategy').resolve()
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abs_paths = self.build_search_paths(config, current_path=current_path,
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user_subdir='strategies', extra_dir=extra_dir)
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user_subdir=constants.USERPATH_STRATEGY,
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extra_dir=extra_dir)
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if ":" in strategy_name:
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logger.info("loading base64 encoded strategy")
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127
freqtrade/templates/base_hyperopt.py.j2
Normal file
127
freqtrade/templates/base_hyperopt.py.j2
Normal file
@@ -0,0 +1,127 @@
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# --- Do not remove these libs ---
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from functools import reduce
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from typing import Any, Callable, Dict, List
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import numpy as np # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from skopt.space import Categorical, Dimension, Integer, Real # noqa
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta # noqa
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class {{ hyperopt }}(IHyperOpt):
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"""
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This is a Hyperopt template to get you started.
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More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
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You should:
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- Add any lib you need to build your hyperopt.
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You must keep:
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- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
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The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
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copied in every custom hyperopt. However, you may override them if you need the
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'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
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Sample implementation of these methods can be found in
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https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
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"""
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@staticmethod
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the buy strategy parameters to be used by Hyperopt.
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"""
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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Buy strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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{{ buy_guards | indent(12) }}
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# TRIGGERS
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if 'trigger' in params:
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if params['trigger'] == 'bb_lower':
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conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
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if params['trigger'] == 'macd_cross_signal':
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conditions.append(qtpylib.crossed_above(
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dataframe['macd'], dataframe['macdsignal']
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))
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if params['trigger'] == 'sar_reversal':
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conditions.append(qtpylib.crossed_above(
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dataframe['close'], dataframe['sar']
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))
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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return populate_buy_trend
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@staticmethod
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def indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching buy strategy parameters.
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"""
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return [
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{{ buy_space | indent(12) }}
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]
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@staticmethod
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def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the sell strategy parameters to be used by Hyperopt.
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"""
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def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Sell strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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{{ sell_guards | indent(12) }}
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# TRIGGERS
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if 'sell-trigger' in params:
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if params['sell-trigger'] == 'sell-bb_upper':
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conditions.append(dataframe['close'] > dataframe['bb_upperband'])
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if params['sell-trigger'] == 'sell-macd_cross_signal':
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conditions.append(qtpylib.crossed_above(
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dataframe['macdsignal'], dataframe['macd']
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))
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if params['sell-trigger'] == 'sell-sar_reversal':
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conditions.append(qtpylib.crossed_above(
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dataframe['sar'], dataframe['close']
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))
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell'] = 1
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return dataframe
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return populate_sell_trend
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@staticmethod
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def sell_indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching sell strategy parameters.
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"""
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return [
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{{ sell_space | indent(12) }}
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]
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138
freqtrade/templates/base_strategy.py.j2
Normal file
138
freqtrade/templates/base_strategy.py.j2
Normal file
@@ -0,0 +1,138 @@
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# --- Do not remove these libs ---
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import numpy as np # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from freqtrade.strategy.interface import IStrategy
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class {{ strategy }}(IStrategy):
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"""
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This is a strategy template to get you started.
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More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
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You can:
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:return: a Dataframe with all mandatory indicators for the strategies
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- Rename the class name (Do not forget to update class_name)
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- Add any methods you want to build your strategy
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- Add any lib you need to build your strategy
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You must keep:
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- the lib in the section "Do not remove these libs"
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- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
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populate_sell_trend, hyperopt_space, buy_strategy_generator
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"""
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# Strategy interface version - allow new iterations of the strategy interface.
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# Check the documentation or the Sample strategy to get the latest version.
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INTERFACE_VERSION = 2
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi".
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minimal_roi = {
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"60": 0.01,
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"30": 0.02,
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"0": 0.04
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}
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# Optimal stoploss designed for the strategy.
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# This attribute will be overridden if the config file contains "stoploss".
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stoploss = -0.10
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# Trailing stoploss
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trailing_stop = False
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# trailing_stop_positive = 0.01
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# trailing_stop_positive_offset = 0.0 # Disabled / not configured
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# Optimal ticker interval for the strategy.
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ticker_interval = '5m'
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = False
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# These values can be overridden in the "ask_strategy" section in the config.
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use_sell_signal = True
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sell_profit_only = False
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ignore_roi_if_buy_signal = False
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 20
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# Optional order type mapping.
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order_types = {
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'buy': 'limit',
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'sell': 'limit',
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'stoploss': 'market',
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'stoploss_on_exchange': False
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}
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# Optional order time in force.
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order_time_in_force = {
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'buy': 'gtc',
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'sell': 'gtc'
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}
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def informative_pairs(self):
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return []
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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{{ indicators | indent(8) }}
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
|
||||
:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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||||
"""
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dataframe.loc[
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(
|
||||
{{ 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
|
||||
196
freqtrade/templates/sample_hyperopt.py
Normal file
196
freqtrade/templates/sample_hyperopt.py
Normal file
@@ -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
|
||||
271
freqtrade/templates/sample_hyperopt_advanced.py
Normal file
271
freqtrade/templates/sample_hyperopt_advanced.py
Normal file
@@ -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
|
||||
47
freqtrade/templates/sample_hyperopt_loss.py
Normal file
47
freqtrade/templates/sample_hyperopt_loss.py
Normal file
@@ -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
|
||||
303
freqtrade/templates/sample_strategy.py
Normal file
303
freqtrade/templates/sample_strategy.py
Normal file
@@ -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
|
||||
315
freqtrade/templates/strategy_analysis_example.ipynb
Normal file
315
freqtrade/templates/strategy_analysis_example.ipynb
Normal file
@@ -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
|
||||
}
|
||||
3
freqtrade/templates/subtemplates/buy_trend_full.j2
Normal file
3
freqtrade/templates/subtemplates/buy_trend_full.j2
Normal file
@@ -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
|
||||
1
freqtrade/templates/subtemplates/buy_trend_minimal.j2
Normal file
1
freqtrade/templates/subtemplates/buy_trend_minimal.j2
Normal file
@@ -0,0 +1 @@
|
||||
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
|
||||
@@ -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'])
|
||||
@@ -0,0 +1,2 @@
|
||||
if params.get('rsi-enabled'):
|
||||
conditions.append(dataframe['rsi'] < params['rsi-value'])
|
||||
@@ -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')
|
||||
@@ -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')
|
||||
@@ -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'])
|
||||
@@ -0,0 +1,2 @@
|
||||
if params.get('sell-rsi-enabled'):
|
||||
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
|
||||
11
freqtrade/templates/subtemplates/hyperopt_sell_space_full.j2
Normal file
11
freqtrade/templates/subtemplates/hyperopt_sell_space_full.j2
Normal file
@@ -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')
|
||||
@@ -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')
|
||||
161
freqtrade/templates/subtemplates/indicators_full.j2
Normal file
161
freqtrade/templates/subtemplates/indicators_full.j2
Normal file
@@ -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]
|
||||
"""
|
||||
17
freqtrade/templates/subtemplates/indicators_minimal.j2
Normal file
17
freqtrade/templates/subtemplates/indicators_minimal.j2
Normal file
@@ -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]
|
||||
"""
|
||||
3
freqtrade/templates/subtemplates/sell_trend_full.j2
Normal file
3
freqtrade/templates/subtemplates/sell_trend_full.j2
Normal file
@@ -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
|
||||
1
freqtrade/templates/subtemplates/sell_trend_minimal.j2
Normal file
1
freqtrade/templates/subtemplates/sell_trend_minimal.j2
Normal file
@@ -0,0 +1 @@
|
||||
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
|
||||
@@ -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)
|
||||
|
||||
Reference in New Issue
Block a user