Bo van Hasselt
GitHub
commit
110df20265
@ -1,13 +1,10 @@
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import ast
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import logging
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from pathlib import Path
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from typing import Any, Dict
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from typing import Any, Dict, List
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from freqtrade.constants import (USERPATH_HYPEROPTS,
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USERPATH_STRATEGIES,
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POSSIBLE_GUARDS,
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POSSIBLE_TRIGGERS,
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POSSIBLE_AIMS)
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USERPATH_STRATEGIES)
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from freqtrade.exceptions import OperationalException
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from freqtrade.state import RunMode
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from freqtrade.configuration import setup_utils_configuration
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@ -17,14 +14,58 @@ logger = logging.getLogger(__name__)
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# ---------------------------------------------------extract-strategy------------------------------------------------------
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'''
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TODO
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- get the stoploss value to optimize
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- get the values from the guards to specify the optimzation range (cooperation with custom-hyperopt)
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'''
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def get_indicator_info(file: List, indicators: Dict) -> None:
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"""
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Get all necessary information to build a custom hyperopt space using
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the file and a dictionary filled with the indicators and their corropsonding line numbers.
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"""
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info_list = []
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for indicator in indicators:
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indicator_info = []
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# find the corrosponding aim
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for position, line in enumerate(file):
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if position == indicators[indicator]:
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# use split twice to remove the context around the indicator
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back_of_line = line.split(f"(dataframe['{indicator}'] ", 1)[1]
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aim = back_of_line.split()[0]
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# add the indicator and aim to the info
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indicator_info.append(indicator)
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indicator_info.append(aim)
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# check if first character after aim is a d in which case the indicator is a trigger
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if back_of_line.split()[1][0] == "d":
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indicator_info.append("trigger")
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# add the second indicator of the guard to the info list
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back_of_line = back_of_line.split("dataframe['")[1]
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second_indicator = back_of_line.split("'])")[0]
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indicator_info.append(second_indicator)
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# elif indicator[0:3] == "CDL":
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# indicator_info.append("guard")
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# else it is a regular guard
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else:
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indicator_info.append("guard")
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value = back_of_line.split()[1]
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value = value[:-1]
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value = float(value)
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indicator_info.append(value)
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info_list.append(indicator_info)
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return info_list
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def extract_dicts(strategypath: Path):
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def extract_lists(strategypath: Path) -> None:
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"""
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Get the indicators, their aims and the stoploss and format them into lists
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"""
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# store the file in a list for reference
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stored_file = []
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with open(strategypath) as file:
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@ -46,13 +87,13 @@ def extract_dicts(strategypath: Path):
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# populate the indicators dictionaries with indicators attached to the line they are on
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buyindicators = {}
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sellindicators = {}
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for position, line in enumerate(stored_file):
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# check the lines in buy trend for indicator and add them
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if position in buy_lines and "(dataframe['" in line:
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# use split twice to remove the context around the indicator
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back_of_line = line.split("(dataframe['", 1)[1]
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buyindicator = back_of_line.split("'] ", 1)[0]
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buyindicators[buyindicator] = position
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# check the lines in sell trend for indicator and add them
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@ -60,38 +101,19 @@ def extract_dicts(strategypath: Path):
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# use split twice to remove the context around the indicator
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back_of_line = line.split("(dataframe['", 1)[1]
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sellindicator = back_of_line.split("'] ", 1)[0]
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sellindicators[sellindicator] = position
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# build the final buy dictionary
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buy_dict = {}
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for indicator in buyindicators:
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# find the corrosponding aim
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for position, line in enumerate(stored_file):
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if position == buyindicators[indicator]:
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# use split twice to remove the context around the indicator
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back_of_line = line.split(f"(dataframe['{indicator}'] ", 1)[1]
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aim = back_of_line.split()[0]
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buy_dict[indicator] = aim
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# build the final lists
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buy_info_list = get_indicator_info(stored_file, buyindicators)
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sell_info_list = get_indicator_info(stored_file, sellindicators)
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# build the final sell dictionary
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sell_dict = {}
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for indicator in sellindicators:
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# find the corrosponding aim
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for position, line in enumerate(stored_file):
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if position == sellindicators[indicator]:
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# use split twice to remove the context around the indicator
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back_of_line = line.split(f"(dataframe['{indicator}'] ", 1)[1]
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aim = back_of_line.split()[0]
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sell_dict[indicator] = aim
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# put the final lists into a tuple
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final_lists = (buy_info_list, sell_info_list)
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# put the final dicts into a tuple
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final_dicts = (buy_dict, sell_dict)
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return final_dicts
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return final_lists
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def start_extract_strategy(args: Dict[str, Any]) -> None:
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def start_extract_strategy(args: Dict) -> None:
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"""
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Check if the right subcommands where passed and start extracting the strategy data
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"""
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@ -113,26 +135,16 @@ def start_extract_strategy(args: Dict[str, Any]) -> None:
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strategy_path = config['user_data_dir'] / USERPATH_STRATEGIES / (args['strategy'] + '.py')
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# extract the buy and sell indicators as dicts
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extracted_dicts = str(extract_dicts(strategy_path))
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extracted_lists = str(extract_lists(strategy_path))
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# save the dicts in a file
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logger.info(f"Writing custom hyperopt to `{new_path}`.")
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new_path.write_text(extracted_dicts)
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new_path.write_text(extracted_lists)
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# --------------------------------------------------custom-hyperopt------------------------------------------------------
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'''
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TODO
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-make the code below more dynamic with a large list of indicators and aims
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-buy_space integer values variation based on aim and input value form extract-strategy(later deep learning)
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-add --mode , see notes (denk hierbij ook aan value range bij guards)
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-Custom stoploss and roi (based on input from extract-strategy)
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-cli option to read extracted strategies files (--extraction)
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-code cleanup (maybe the two elements functions can be combined)
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'''
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def custom_hyperopt_buyelements(buy_indicators: Dict[str, str]):
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def custom_hyperopt_buyelements(buy_indicators: List):
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"""
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Build the arguments with the placefillers for the buygenerator
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"""
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@ -140,39 +152,44 @@ def custom_hyperopt_buyelements(buy_indicators: Dict[str, str]):
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buy_triggers = ""
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buy_space = ""
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for indicator in buy_indicators:
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# Error handling
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if not indicator in POSSIBLE_GUARDS and not indicator in POSSIBLE_TRIGGERS:
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raise OperationalException(
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f"`{indicator}` is not part of the available indicators. The current options are {POSSIBLE_GUARDS + POSSIBLE_TRIGGERS}.")
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elif not buy_indicators[indicator] in POSSIBLE_AIMS:
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raise OperationalException(
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f"`{buy_indicators[indicator]}` is not part of the available indicator options. The current options are {POSSIBLE_AIMS}.")
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for indicator_info in buy_indicators:
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indicator = indicator_info[0]
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aim = indicator_info[1]
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usage = indicator_info[2]
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# If the indicator is a guard
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elif indicator in POSSIBLE_GUARDS:
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# get the symbol corrosponding to the value
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aim = buy_indicators[indicator]
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if usage == "guard":
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value = indicator_info[3]
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if value >= -1.0 and value <= 1.0:
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lower_bound = value - 0.3
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upper_bound = value + 0.3
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else:
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lower_bound = value - 30.0
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upper_bound = value + 30.0
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# add the guard to its argument
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buy_guards += f"if '{indicator}-enabled' in params and params['{indicator}-enabled']:\n conditions.append(dataframe['{indicator}'] {aim} params['{indicator}-value'])\n"
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buy_guards += f"if params.get('{indicator}-enabled'):\n conditions.append(dataframe['{indicator}'] {aim} params['{indicator}-value'])\n"
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# add the space to its argument
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buy_space += f"Integer(10, 90, name='{indicator}-value'),\nCategorical([True, False], name='{indicator}-enabled'),\n"
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# If the indicator is a trigger
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elif indicator in POSSIBLE_TRIGGERS:
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# get the symbol corrosponding to the value
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aim = buy_indicators[indicator]
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buy_space += f"Integer({lower_bound}, {upper_bound}, name='{indicator}-value'),\nCategorical([True, False], name='{indicator}-enabled'),\n"
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# If the indicator is a trigger
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elif usage == "trigger":
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secondindicator = indicator_info[3]
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# add the trigger to its argument
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buy_triggers += f"if params['trigger'] == '{indicator}':\n conditions.append(dataframe['{indicator}'] {aim} dataframe['close'])\n"
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buy_triggers += f"if params['trigger'] == '{indicator}':\n conditions.append(dataframe['{indicator}'] {aim} dataframe['{secondindicator}'])\n"
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# Final line of indicator space makes all triggers
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buy_space += "Categorical(["
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# adding all triggers to the list
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for indicator in buy_indicators:
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if indicator in POSSIBLE_TRIGGERS:
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for indicator_info in buy_indicators:
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indicator = indicator_info[0]
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usage = indicator_info[2]
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if usage == "trigger":
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buy_space += f"'{indicator}', "
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# Deleting the last ", "
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@ -190,44 +207,50 @@ def custom_hyperopt_sellelements(sell_indicators: Dict[str, str]):
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sell_triggers = ""
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sell_space = ""
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for indicator in sell_indicators:
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# Error handling
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if not indicator in POSSIBLE_GUARDS and not indicator in POSSIBLE_TRIGGERS:
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raise OperationalException(
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f"`{indicator}` is not part of the available indicators. The current options are {POSSIBLE_GUARDS + POSSIBLE_TRIGGERS}.")
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elif not sell_indicators[indicator] in POSSIBLE_AIMS:
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raise OperationalException(
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f"`{sell_indicators[indicator]}` is not part of the available indicator options. The current options are {POSSIBLE_AIMS}.")
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# If indicator is a guard
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elif indicator in POSSIBLE_GUARDS:
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# get the symbol corrosponding to the value
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aim = sell_indicators[indicator]
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for indicator_info in sell_indicators:
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indicator = indicator_info[0]
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aim = indicator_info[1]
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usage = indicator_info[2]
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# If the indicator is a guard
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if usage == "guard":
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value = indicator_info[3]
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if value >= -1 and value <= 1:
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lower_bound = value - 0.3
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upper_bound = value + 0.3
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else:
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lower_bound = value - 30
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upper_bound = value + 30
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# add the guard to its argument
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sell_guards += f"if '{indicator}-enabled' in params and params['sell-{indicator}-enabled']:\n conditions.append(dataframe['{indicator}'] {aim} params['sell-{indicator}-value'])\n"
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sell_guards += f"if params.get('sell-{indicator}-enabled'):\n conditions.append(dataframe['{indicator}'] {aim} params['sell-{indicator}-value'])\n"
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# add the space to its argument
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sell_space += f"Integer(10, 90, name='sell-{indicator}-value'),\nCategorical([True, False], name='sell-{indicator}-enabled'),\n"
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sell_space += f"Integer({lower_bound}, {upper_bound}, name='sell-{indicator}-value'),\nCategorical([True, False], name='sell-{indicator}-enabled'),\n"
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# If the indicator is a trigger
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elif indicator in POSSIBLE_TRIGGERS:
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# get the symbol corrosponding to the value
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aim = sell_indicators[indicator]
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elif usage == "trigger":
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secondindicator = indicator_info[3]
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# add the trigger to its argument
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sell_triggers += f"if params['sell-trigger'] == 'sell-{indicator}':\n conditions.append(dataframe['{indicator}'] {aim} dataframe['close'])\n"
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sell_triggers += f"if params['sell-trigger'] == 'sell-{indicator}':\n conditions.append(dataframe['{indicator}'] {aim} dataframe['{secondindicator}'])\n"
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# Final line of indicator space makes all triggers
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sell_space += "Categorical(["
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# Adding all triggers to the list
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for indicator in sell_indicators:
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if indicator in POSSIBLE_TRIGGERS:
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# adding all triggers to the list
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for indicator_info in sell_indicators:
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indicator = indicator_info[0]
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usage = indicator_info[2]
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if usage == "trigger":
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sell_space += f"'sell-{indicator}', "
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# Deleting the last ", "
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sell_space = sell_space[:-2]
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sell_space += "], name='trigger')"
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sell_space += "], name='sell-trigger')"
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return {"sell_guards": sell_guards, "sell_triggers": sell_triggers, "sell_space": sell_space}
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@ -242,7 +265,7 @@ def deploy_custom_hyperopt(hyperopt_name: str, hyperopt_path: Path, buy_indicato
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sell_args = custom_hyperopt_sellelements(sell_indicators)
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# Build the final template
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strategy_text = render_template(templatefile='base_hyperopt.py.j2',
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strategy_text = render_template(templatefile='base_custom_hyperopt.py.j2',
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arguments={"hyperopt": hyperopt_name,
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"buy_guards": buy_args["buy_guards"],
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"buy_triggers": buy_args["buy_triggers"],
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@ -285,11 +308,6 @@ def start_custom_hyperopt(args: Dict[str, Any]) -> None:
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# --------------------------------------------------build-hyperopt------------------------------------------------------
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'''
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TODO
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-hyperopt optional (door bv standaard naming toe te passen)
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'''
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def start_build_hyperopt(args: Dict[str, Any]) -> None:
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"""
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@ -301,7 +319,7 @@ def start_build_hyperopt(args: Dict[str, Any]) -> None:
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if not 'strategy' in args or not args['strategy']:
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raise OperationalException("`build-hyperopt` requires --strategy to be set.")
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if not 'hyperopt' in args or not args['hyperopt']:
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raise OperationalException("`build-hyperopt` requires --hyperopt to be set.")
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args['hyperopt'] = args['strategy'] + "opt"
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else:
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if args['hyperopt'] == 'DefaultHyperopt':
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raise OperationalException("DefaultHyperopt is not allowed as name.")
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@ -316,10 +334,10 @@ def start_build_hyperopt(args: Dict[str, Any]) -> None:
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"Please choose another Hyperopt Name.")
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# extract the buy and sell indicators as dicts
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extracted_dicts = extract_dicts(strategy_path)
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extracted_lists = extract_lists(strategy_path)
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buy_indicators = extracted_dicts[0]
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sell_indicators = extracted_dicts[1]
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buy_indicators = extracted_lists[0]
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sell_indicators = extracted_lists[1]
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# use the dicts to write the hyperopt
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deploy_custom_hyperopt(args['hyperopt'], new_path,
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@ -279,14 +279,16 @@ AVAILABLE_CLI_OPTIONS = {
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"buy_indicators": Arg(
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'-b', '--buy-indicators',
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help='Specify the buy indicators the hyperopt should build. '
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'Example: --buy-indicators `{"rsi":"above","bb_lowerband":"below"}`',
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metavar='DICT',
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'Example: --buy-indicators `[["rsi","<","trigger",30.0],["bb_lowerband",">","guard","close"]]`'
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'Check the documentation for specific requirements for the lists.',
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metavar='LIST',
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),
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"sell_indicators": Arg(
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'-s', '--sell-indicators',
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help='Specify the sell indicators the hyperopt should build. '
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'Example: --sell-indicators `{"rsi":"above","bb_lowerband":"below"}`',
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metavar='DICT',
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'Example: --sell-indicators [["rsi",">","trigger",70.0],["bb_lowerband","<","guard","close"]]'
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'Check the documentation for specific requirements for the lists.',
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metavar='LIST',
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),
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"extract_name": Arg(
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'--extract-name',
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@ -383,23 +383,3 @@ ListPairsWithTimeframes = List[PairWithTimeframe]
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# Type for trades list
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TradeList = List[List]
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#Build-hyperopt options
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POSSIBLE_GUARDS = [
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"rsi",
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"mfi",
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"fastd",
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"slowk",
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"CDLHAMMER",
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"fisher_rsi"
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]
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POSSIBLE_TRIGGERS = [
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"sar",
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"bb_lowerband",
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"bb_upperband"
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]
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POSSIBLE_AIMS = [
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">",
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"<",
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"=="
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]
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164
freqtrade/templates/base_custom_hyperopt.py.j2
Normal file
164
freqtrade/templates/base_custom_hyperopt.py.j2
Normal file
@ -0,0 +1,164 @@
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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 the documentation: https://www.freqtrade.io/en/latest/hyperopt/
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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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|
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The methods roi_space, generate_roi_table and stoploss_space are not required
|
||||
and are provided by default.
|
||||
However, you may override them if you need 'roi' and 'stoploss' spaces that
|
||||
differ from the defaults offered by Freqtrade.
|
||||
Sample implementation of these methods will be copied to `user_data/hyperopts` when
|
||||
creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
|
||||
or is available online under the following URL:
|
||||
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by Hyperopt.
|
||||
"""
|
||||
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Buy strategy Hyperopt will build and use.
|
||||
"""
|
||||
conditions = []
|
||||
|
||||
# GUARDS AND TRENDS
|
||||
{{ buy_guards | indent(12) }}
|
||||
|
||||
# TRIGGERS
|
||||
if 'trigger' in params:
|
||||
{{ buy_triggers | indent(16) }}
|
||||
|
||||
# Check that the candle had volume
|
||||
conditions.append(dataframe['volume'] > 0)
|
||||
|
||||
if conditions:
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
return populate_buy_trend
|
||||
|
||||
@staticmethod
|
||||
def indicator_space() -> List[Dimension]:
|
||||
"""
|
||||
Define your Hyperopt space for searching buy strategy parameters.
|
||||
"""
|
||||
return [
|
||||
{{ buy_space | indent(12) }}
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Define the sell strategy parameters to be used by Hyperopt.
|
||||
"""
|
||||
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Sell strategy Hyperopt will build and use.
|
||||
"""
|
||||
conditions = []
|
||||
|
||||
# GUARDS AND TRENDS
|
||||
{{ sell_guards | indent(12) }}
|
||||
|
||||
# TRIGGERS
|
||||
if 'sell-trigger' in params:
|
||||
{{ sell_triggers | indent(16) }}
|
||||
|
||||
# Check that the candle had volume
|
||||
conditions.append(dataframe['volume'] > 0)
|
||||
|
||||
if conditions:
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'sell'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
return populate_sell_trend
|
||||
|
||||
@staticmethod
|
||||
def sell_indicator_space() -> List[Dimension]:
|
||||
"""
|
||||
Define your Hyperopt space for searching sell strategy parameters.
|
||||
"""
|
||||
return [
|
||||
{{ sell_space | indent(12) }}
|
||||
]
|
||||
@ 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(-0.35, -0.02, name='stoploss'),
|
||||
]
|
||||
@ -55,7 +55,16 @@ class {{ hyperopt }}(IHyperOpt):
|
||||
|
||||
# TRIGGERS
|
||||
if 'trigger' in params:
|
||||
{{ buy_triggers | indent(16) }}
|
||||
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']
|
||||
))
|
||||
|
||||
# Check that the candle had volume
|
||||
conditions.append(dataframe['volume'] > 0)
|
||||
@ -94,7 +103,16 @@ class {{ hyperopt }}(IHyperOpt):
|
||||
|
||||
# TRIGGERS
|
||||
if 'sell-trigger' in params:
|
||||
{{ sell_triggers | indent(16) }}
|
||||
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']
|
||||
))
|
||||
|
||||
# Check that the candle had volume
|
||||
conditions.append(dataframe['volume'] > 0)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user