diff --git a/docs/hyperopt.md b/docs/hyperopt.md index 0b5d1a50e..0304c3188 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -18,19 +18,24 @@ Configuring hyperopt is similar to writing your own strategy, and many tasks wil ### Checklist on all tasks / possibilities in hyperopt -Depending on the space you want to optimize, only some of the below are required. +Depending on the space you want to optimize, only some of the below are required: * fill `populate_indicators` - probably a copy from your strategy * fill `buy_strategy_generator` - for buy signal optimization * fill `indicator_space` - for buy signal optimzation * fill `sell_strategy_generator` - for sell signal optimization * fill `sell_indicator_space` - for sell signal optimzation -* fill `roi_space` - for ROI optimization -* fill `generate_roi_table` - for ROI optimization (if you need more than 3 entries) -* fill `stoploss_space` - stoploss optimization -* Optional but recommended - * copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used - * copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used + +Optional, but recommended: + +* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used +* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used + +Rarely you may also need to override: + +* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default) +* `generate_roi_table` - for custom ROI optimization (if you need more than 4 entries in the ROI table) +* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default) ### 1. Install a Custom Hyperopt File @@ -345,7 +350,7 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: ### Understand Hyperopt ROI results -If you are optimizing ROI, you're result will look as follows and include a ROI table. +If you are optimizing ROI, your result will look as follows and include a ROI table: ``` Best result: @@ -376,6 +381,41 @@ minimal_roi = { } ``` +If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps) with the values that can vary in the following ranges: + +| # | minutes | ROI percentage | +|---|---|---| +| 1 | always 0 | 0.03...0.31 | +| 2 | 10...40 | 0.02...0.11 | +| 3 | 20...100 | 0.01...0.04 | +| 4 | 30...220 | always 0 | + +This structure of the ROI table is sufficient in most cases. Override the `roi_space()` method defining the ranges desired if you need components of the ROI tables to vary in other ranges. + +Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization in these methods if you need a different structure of the ROI table or other amount of rows (steps) in the ROI tables. + +### Understand Hyperopt Stoploss results + +If you are optimizing stoploss values, your result will look as follows and include stoploss: + +``` +Best result: + + 44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367 + +Buy hyperspace params: +{ 'adx-value': 44, + 'rsi-value': 29, + 'adx-enabled': False, + 'rsi-enabled': True, + 'trigger': 'bb_lower'} +Stoploss: -0.37996664668703606 +``` + +If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace can vary in the range -0.5...-0.02, which is sufficient in most cases. + +Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. + ### Validate backtesting results Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected. diff --git a/freqtrade/optimize/default_hyperopt.py b/freqtrade/optimize/default_hyperopt.py index ad76ff786..1d4c40916 100644 --- a/freqtrade/optimize/default_hyperopt.py +++ b/freqtrade/optimize/default_hyperopt.py @@ -5,7 +5,7 @@ from typing import Any, Callable, Dict, List import talib.abstract as ta from pandas import DataFrame -from skopt.space import Categorical, Dimension, Integer, Real +from skopt.space import Categorical, Dimension, Integer import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.optimize.hyperopt_interface import IHyperOpt @@ -156,42 +156,6 @@ class DefaultHyperOpts(IHyperOpt): '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 - """ - 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 stoploss_space() -> List[Dimension]: - """ - Stoploss Value to search - """ - return [ - Real(-0.5, -0.02, name='stoploss'), - ] - - @staticmethod - def roi_space() -> List[Dimension]: - """ - Values to search for each ROI steps - """ - 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'), - ] - def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators. Should be a copy of from strategy diff --git a/freqtrade/optimize/hyperopt_interface.py b/freqtrade/optimize/hyperopt_interface.py index 08823ece0..f1f123653 100644 --- a/freqtrade/optimize/hyperopt_interface.py +++ b/freqtrade/optimize/hyperopt_interface.py @@ -7,7 +7,7 @@ from abc import ABC, abstractmethod from typing import Dict, Any, Callable, List from pandas import DataFrame -from skopt.space import Dimension +from skopt.space import Dimension, Integer, Real class IHyperOpt(ABC): @@ -26,56 +26,80 @@ class IHyperOpt(ABC): @abstractmethod def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame: """ - Populate indicators that will be used in the Buy and Sell strategy - :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe() - :return: a Dataframe with all mandatory indicators for the strategies + Populate indicators that will be used in the Buy and Sell strategy. + :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe(). + :return: A Dataframe with all mandatory indicators for the strategies. """ @staticmethod @abstractmethod def buy_strategy_generator(params: Dict[str, Any]) -> Callable: """ - Create a buy strategy generator + Create a buy strategy generator. """ @staticmethod @abstractmethod def sell_strategy_generator(params: Dict[str, Any]) -> Callable: """ - Create a sell strategy generator + Create a sell strategy generator. """ @staticmethod @abstractmethod def indicator_space() -> List[Dimension]: """ - Create an indicator space + Create an indicator space. """ @staticmethod @abstractmethod def sell_indicator_space() -> List[Dimension]: """ - Create a sell indicator space + Create a sell indicator space. """ @staticmethod - @abstractmethod def generate_roi_table(params: Dict) -> Dict[int, float]: """ - Create an roi table + Create a ROI table. + + Generates the ROI table that will be used by Hyperopt. + You may override it in your custom Hyperopt class. """ + 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 - @abstractmethod def stoploss_space() -> List[Dimension]: """ - Create a stoploss space + Create a stoploss space. + + Defines range of stoploss values to search. + You may override it in your custom Hyperopt class. """ + return [ + Real(-0.5, -0.02, name='stoploss'), + ] @staticmethod - @abstractmethod def roi_space() -> List[Dimension]: """ - Create a roi space + Create a ROI space. + + Defines values to search for each ROI steps. + You may override it in your custom Hyperopt class. """ + 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'), + ] diff --git a/user_data/hyperopts/sample_hyperopt.py b/user_data/hyperopts/sample_hyperopt.py index a78906cf3..9a755e52e 100644 --- a/user_data/hyperopts/sample_hyperopt.py +++ b/user_data/hyperopts/sample_hyperopt.py @@ -18,14 +18,19 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt class SampleHyperOpts(IHyperOpt): """ This is a test hyperopt to inspire you. + More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md - You can: - - Rename the class name (Do not forget to update class_name) - - Add any methods you want to build your hyperopt - - Add any lib you need to build your hyperopt - You must keep: - - the prototype for the methods: populate_indicators, indicator_space, buy_strategy_generator, - roi_space, generate_roi_table, stoploss_space + + You can: + - Rename the class 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 were moved to the parent class, you may + override them here if you need it. """ @staticmethod @@ -167,42 +172,6 @@ class SampleHyperOpts(IHyperOpt): '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 - """ - 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 stoploss_space() -> List[Dimension]: - """ - Stoploss Value to search - """ - return [ - Real(-0.5, -0.02, name='stoploss'), - ] - - @staticmethod - def roi_space() -> List[Dimension]: - """ - Values to search for each ROI steps - """ - 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'), - ] - def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators. Should be a copy of from strategy