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Advanced Strategy
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Advanced Hyperopt
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Advanced Hyperopt
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Creating and using a custom loss function
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Dynamic parameters
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Overriding Base estimator
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< h1 id = "advanced-hyperopt" > Advanced Hyperopt< a class = "headerlink" href = "#advanced-hyperopt" title = "Permanent link" > ¶ < / a > < / h1 >
< p > This page explains some advanced Hyperopt topics that may require higher
coding skills and Python knowledge than creation of an ordinal hyperoptimization
class.< / p >
< h2 id = "creating-and-using-a-custom-loss-function" > Creating and using a custom loss function< a class = "headerlink" href = "#creating-and-using-a-custom-loss-function" title = "Permanent link" > ¶ < / a > < / h2 >
< p > To use a custom loss function class, make sure that the function < code > hyperopt_loss_function< / code > is defined in your custom hyperopt loss class.
For the sample below, you then need to add the command line parameter < code > --hyperopt-loss SuperDuperHyperOptLoss< / code > to your hyperopt call so this function is being used.< / p >
< p > A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in < a href = "https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py" > userdata/hyperopts< / a > .< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "kn" > from< / span > < span class = "nn" > datetime< / span > < span class = "kn" > import< / span > < span class = "n" > datetime< / span >
< span class = "kn" > from< / span > < span class = "nn" > typing< / span > < span class = "kn" > import< / span > < span class = "n" > Any< / span > < span class = "p" > ,< / span > < span class = "n" > Dict< / span >
< span class = "kn" > from< / span > < span class = "nn" > pandas< / span > < span class = "kn" > import< / span > < span class = "n" > DataFrame< / span >
< span class = "kn" > from< / span > < span class = "nn" > freqtrade.constants< / span > < span class = "kn" > import< / span > < span class = "n" > Config< / span >
< span class = "kn" > from< / span > < span class = "nn" > freqtrade.optimize.hyperopt< / span > < span class = "kn" > import< / span > < span class = "n" > IHyperOptLoss< / span >
< span class = "n" > TARGET_TRADES< / span > < span class = "o" > =< / span > < span class = "mi" > 600< / span >
< span class = "n" > EXPECTED_MAX_PROFIT< / span > < span class = "o" > =< / span > < span class = "mf" > 3.0< / span >
< span class = "n" > MAX_ACCEPTED_TRADE_DURATION< / span > < span class = "o" > =< / span > < span class = "mi" > 300< / span >
< span class = "k" > class< / span > < span class = "nc" > SuperDuperHyperOptLoss< / span > < span class = "p" > (< / span > < span class = "n" > IHyperOptLoss< / span > < span class = "p" > ):< / span >
< span class = "w" > < / span > < span class = "sd" > " " " < / span >
< span class = "sd" > Defines the default loss function for hyperopt< / span >
< span class = "sd" > " " " < / span >
< span class = "nd" > @staticmethod< / span >
< span class = "k" > def< / span > < span class = "nf" > hyperopt_loss_function< / span > < span class = "p" > (< / span >
< span class = "o" > *< / span > < span class = "p" > ,< / span >
< span class = "n" > results< / span > < span class = "p" > :< / span > < span class = "n" > DataFrame< / span > < span class = "p" > ,< / span >
< span class = "n" > trade_count< / span > < span class = "p" > :< / span > < span class = "nb" > int< / span > < span class = "p" > ,< / span >
< span class = "n" > min_date< / span > < span class = "p" > :< / span > < span class = "n" > datetime< / span > < span class = "p" > ,< / span >
< span class = "n" > max_date< / span > < span class = "p" > :< / span > < span class = "n" > datetime< / span > < span class = "p" > ,< / span >
< span class = "n" > config< / span > < span class = "p" > :< / span > < span class = "n" > Config< / span > < span class = "p" > ,< / span >
< span class = "n" > processed< / span > < span class = "p" > :< / span > < span class = "n" > Dict< / span > < span class = "p" > [< / span > < span class = "nb" > str< / span > < span class = "p" > ,< / span > < span class = "n" > DataFrame< / span > < span class = "p" > ],< / span >
< span class = "n" > backtest_stats< / span > < span class = "p" > :< / span > < span class = "n" > Dict< / span > < span class = "p" > [< / span > < span class = "nb" > str< / span > < span class = "p" > ,< / span > < span class = "n" > Any< / span > < span class = "p" > ],< / span >
< span class = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ,< / span >
< span class = "p" > )< / span > < span class = "o" > -> < / span > < span class = "nb" > float< / span > < span class = "p" > :< / span >
< span class = "w" > < / span > < span class = "sd" > " " " < / span >
< span class = "sd" > Objective function, returns smaller number for better results< / span >
< span class = "sd" > This is the legacy algorithm (used until now in freqtrade).< / span >
< span class = "sd" > Weights are distributed as follows:< / span >
< span class = "sd" > * 0.4 to trade duration< / span >
< span class = "sd" > * 0.25: Avoiding trade loss< / span >
< span class = "sd" > * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above< / span >
< span class = "sd" > " " " < / span >
< span class = "n" > total_profit< / span > < span class = "o" > =< / span > < span class = "n" > results< / span > < span class = "p" > [< / span > < span class = "s1" > ' profit_ratio' < / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > sum< / span > < span class = "p" > ()< / span >
< span class = "n" > trade_duration< / span > < span class = "o" > =< / span > < span class = "n" > results< / span > < span class = "p" > [< / span > < span class = "s1" > ' trade_duration' < / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > mean< / span > < span class = "p" > ()< / span >
< span class = "n" > trade_loss< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "o" > -< / span > < span class = "mf" > 0.25< / span > < span class = "o" > *< / span > < span class = "n" > exp< / span > < span class = "p" > (< / span > < span class = "o" > -< / span > < span class = "p" > (< / span > < span class = "n" > trade_count< / span > < span class = "o" > -< / span > < span class = "n" > TARGET_TRADES< / span > < span class = "p" > )< / span > < span class = "o" > **< / span > < span class = "mi" > 2< / span > < span class = "o" > /< / span > < span class = "mi" > 10< / span > < span class = "o" > **< / span > < span class = "mf" > 5.8< / span > < span class = "p" > )< / span >
< span class = "n" > profit_loss< / span > < span class = "o" > =< / span > < span class = "nb" > max< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "o" > -< / span > < span class = "n" > total_profit< / span > < span class = "o" > /< / span > < span class = "n" > EXPECTED_MAX_PROFIT< / span > < span class = "p" > )< / span >
< span class = "n" > duration_loss< / span > < span class = "o" > =< / span > < span class = "mf" > 0.4< / span > < span class = "o" > *< / span > < span class = "nb" > min< / span > < span class = "p" > (< / span > < span class = "n" > trade_duration< / span > < span class = "o" > /< / span > < span class = "n" > MAX_ACCEPTED_TRADE_DURATION< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "n" > result< / span > < span class = "o" > =< / span > < span class = "n" > trade_loss< / span > < span class = "o" > +< / span > < span class = "n" > profit_loss< / span > < span class = "o" > +< / span > < span class = "n" > duration_loss< / span >
< span class = "k" > return< / span > < span class = "n" > result< / span >
< / code > < / pre > < / div >
< p > Currently, the arguments are:< / p >
< ul >
< li > < code > results< / code > : DataFrame containing the resulting trades.
The following columns are available in results (corresponds to the output-file of backtesting when used with < code > --export trades< / code > ):< br / >
< code > pair, profit_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, exit_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs< / code > < / li >
< li > < code > trade_count< / code > : Amount of trades (identical to < code > len(results)< / code > )< / li >
< li > < code > min_date< / code > : Start date of the timerange used< / li >
< li > < code > min_date< / code > : End date of the timerange used< / li >
< li > < code > config< / code > : Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).< / li >
< li > < code > processed< / code > : Dict of Dataframes with the pair as keys containing the data used for backtesting.< / li >
< li > < code > backtest_stats< / code > : Backtesting statistics using the same format as the backtesting file "strategy" substructure. Available fields can be seen in < code > generate_strategy_stats()< / code > in < code > optimize_reports.py< / code > .< / li >
< / ul >
< p > This function needs to return a floating point number (< code > float< / code > ). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.< / p >
< div class = "admonition note" >
< p class = "admonition-title" > Note< / p >
< p > This function is called once per epoch - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.< / p >
< / div >
< div class = "admonition note" >
< p class = "admonition-title" > < code > *args< / code > and < code > **kwargs< / code > < / p >
< p > Please keep the arguments < code > *args< / code > and < code > **kwargs< / code > in the interface to allow us to extend this interface in the future.< / p >
< / div >
< h2 id = "overriding-pre-defined-spaces" > Overriding pre-defined spaces< a class = "headerlink" href = "#overriding-pre-defined-spaces" title = "Permanent link" > ¶ < / a > < / h2 >
< p > To override a pre-defined space (< code > roi_space< / code > , < code > generate_roi_table< / code > , < code > stoploss_space< / code > , < code > trailing_space< / code > , < code > max_open_trades_space< / code > ), define a nested class called Hyperopt and define the required spaces as follows:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "kn" > from< / span > < span class = "nn" > freqtrade.optimize.space< / span > < span class = "kn" > import< / span > < span class = "n" > Categorical< / span > < span class = "p" > ,< / span > < span class = "n" > Dimension< / span > < span class = "p" > ,< / span > < span class = "n" > Integer< / span > < span class = "p" > ,< / span > < span class = "n" > SKDecimal< / span >
< span class = "k" > class< / span > < span class = "nc" > MyAwesomeStrategy< / span > < span class = "p" > (< / span > < span class = "n" > IStrategy< / span > < span class = "p" > ):< / span >
< span class = "k" > class< / span > < span class = "nc" > HyperOpt< / span > < span class = "p" > :< / span >
< span class = "c1" > # Define a custom stoploss space.< / span >
< span class = "k" > def< / span > < span class = "nf" > stoploss_space< / span > < span class = "p" > ():< / span >
< span class = "k" > return< / span > < span class = "p" > [< / span > < span class = "n" > SKDecimal< / span > < span class = "p" > (< / span > < span class = "o" > -< / span > < span class = "mf" > 0.05< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "n" > decimals< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' stoploss' < / span > < span class = "p" > )]< / span >
< span class = "c1" > # Define custom ROI space< / span >
< span class = "k" > def< / span > < span class = "nf" > roi_space< / span > < span class = "p" > ()< / span > < span class = "o" > -> < / span > < span class = "n" > List< / span > < span class = "p" > [< / span > < span class = "n" > Dimension< / span > < span class = "p" > ]:< / span >
< span class = "k" > return< / span > < span class = "p" > [< / span >
< span class = "n" > Integer< / span > < span class = "p" > (< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span > < span class = "mi" > 120< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' roi_t1' < / span > < span class = "p" > ),< / span >
< span class = "n" > Integer< / span > < span class = "p" > (< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span > < span class = "mi" > 60< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' roi_t2' < / span > < span class = "p" > ),< / span >
< span class = "n" > Integer< / span > < span class = "p" > (< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span > < span class = "mi" > 40< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' roi_t3' < / span > < span class = "p" > ),< / span >
< span class = "n" > SKDecimal< / span > < span class = "p" > (< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.04< / span > < span class = "p" > ,< / span > < span class = "n" > decimals< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' roi_p1' < / span > < span class = "p" > ),< / span >
< span class = "n" > SKDecimal< / span > < span class = "p" > (< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.07< / span > < span class = "p" > ,< / span > < span class = "n" > decimals< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' roi_p2' < / span > < span class = "p" > ),< / span >
< span class = "n" > SKDecimal< / span > < span class = "p" > (< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.20< / span > < span class = "p" > ,< / span > < span class = "n" > decimals< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' roi_p3' < / span > < span class = "p" > ),< / span >
< span class = "p" > ]< / span >
< span class = "k" > def< / span > < span class = "nf" > generate_roi_table< / span > < span class = "p" > (< / span > < span class = "n" > params< / span > < span class = "p" > :< / span > < span class = "n" > Dict< / span > < span class = "p" > )< / span > < span class = "o" > -> < / span > < span class = "n" > Dict< / span > < span class = "p" > [< / span > < span class = "nb" > int< / span > < span class = "p" > ,< / span > < span class = "nb" > float< / span > < span class = "p" > ]:< / span >
< span class = "n" > roi_table< / span > < span class = "o" > =< / span > < span class = "p" > {}< / span >
< span class = "n" > roi_table< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_p1' < / span > < span class = "p" > ]< / span > < span class = "o" > +< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_p2' < / span > < span class = "p" > ]< / span > < span class = "o" > +< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_p3' < / span > < span class = "p" > ]< / span >
< span class = "n" > roi_table< / span > < span class = "p" > [< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_t3' < / span > < span class = "p" > ]]< / span > < span class = "o" > =< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_p1' < / span > < span class = "p" > ]< / span > < span class = "o" > +< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_p2' < / span > < span class = "p" > ]< / span >
< span class = "n" > roi_table< / span > < span class = "p" > [< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_t3' < / span > < span class = "p" > ]< / span > < span class = "o" > +< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_t2' < / span > < span class = "p" > ]]< / span > < span class = "o" > =< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_p1' < / span > < span class = "p" > ]< / span >
< span class = "n" > roi_table< / span > < span class = "p" > [< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_t3' < / span > < span class = "p" > ]< / span > < span class = "o" > +< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_t2' < / span > < span class = "p" > ]< / span > < span class = "o" > +< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s1" > ' roi_t1' < / span > < span class = "p" > ]]< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span >
< span class = "k" > return< / span > < span class = "n" > roi_table< / span >
< span class = "k" > def< / span > < span class = "nf" > trailing_space< / span > < span class = "p" > ()< / span > < span class = "o" > -> < / span > < span class = "n" > List< / span > < span class = "p" > [< / span > < span class = "n" > Dimension< / span > < span class = "p" > ]:< / span >
< span class = "c1" > # All parameters here are mandatory, you can only modify their type or the range.< / span >
< span class = "k" > return< / span > < span class = "p" > [< / span >
< span class = "c1" > # Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times.< / span >
< span class = "n" > Categorical< / span > < span class = "p" > ([< / span > < span class = "kc" > True< / span > < span class = "p" > ],< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' trailing_stop' < / span > < span class = "p" > ),< / span >
< span class = "n" > SKDecimal< / span > < span class = "p" > (< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.35< / span > < span class = "p" > ,< / span > < span class = "n" > decimals< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' trailing_stop_positive' < / span > < span class = "p" > ),< / span >
< span class = "c1" > # ' trailing_stop_positive_offset' should be greater than ' trailing_stop_positive' ,< / span >
< span class = "c1" > # so this intermediate parameter is used as the value of the difference between< / span >
< span class = "c1" > # them. The value of the ' trailing_stop_positive_offset' is constructed in the< / span >
< span class = "c1" > # generate_trailing_params() method.< / span >
< span class = "c1" > # This is similar to the hyperspace dimensions used for constructing the ROI tables.< / span >
< span class = "n" > SKDecimal< / span > < span class = "p" > (< / span > < span class = "mf" > 0.001< / span > < span class = "p" > ,< / span > < span class = "mf" > 0.1< / span > < span class = "p" > ,< / span > < span class = "n" > decimals< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' trailing_stop_positive_offset_p1' < / span > < span class = "p" > ),< / span >
< span class = "n" > Categorical< / span > < span class = "p" > ([< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "kc" > False< / span > < span class = "p" > ],< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' trailing_only_offset_is_reached' < / span > < span class = "p" > ),< / span >
< span class = "p" > ]< / span >
< span class = "c1" > # Define a custom max_open_trades space< / span >
< span class = "k" > def< / span > < span class = "nf" > max_open_trades_space< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > )< / span > < span class = "o" > -> < / span > < span class = "n" > List< / span > < span class = "p" > [< / span > < span class = "n" > Dimension< / span > < span class = "p" > ]:< / span >
< span class = "k" > return< / span > < span class = "p" > [< / span >
< span class = "n" > Integer< / span > < span class = "p" > (< / span > < span class = "o" > -< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "s1" > ' max_open_trades' < / span > < span class = "p" > ),< / span >
< span class = "p" > ]< / span >
< / code > < / pre > < / div >
< div class = "admonition note" >
< p class = "admonition-title" > Note< / p >
< p > All overrides are optional and can be mixed/matched as necessary.< / p >
< / div >
< h3 id = "dynamic-parameters" > Dynamic parameters< a class = "headerlink" href = "#dynamic-parameters" title = "Permanent link" > ¶ < / a > < / h3 >
< p > Parameters can also be defined dynamically, but must be available to the instance once the < a href = "../strategy-callbacks/#bot-start" > < code > bot_start()< / code > callback< / a > has been called.< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "k" > class< / span > < span class = "nc" > MyAwesomeStrategy< / span > < span class = "p" > (< / span > < span class = "n" > IStrategy< / span > < span class = "p" > ):< / span >
< span class = "k" > def< / span > < span class = "nf" > bot_start< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > )< / span > < span class = "o" > -> < / span > < span class = "kc" > None< / span > < span class = "p" > :< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > buy_adx< / span > < span class = "o" > =< / span > < span class = "n" > IntParameter< / span > < span class = "p" > (< / span > < span class = "mi" > 20< / span > < span class = "p" > ,< / span > < span class = "mi" > 30< / span > < span class = "p" > ,< / span > < span class = "n" > default< / span > < span class = "o" > =< / span > < span class = "mi" > 30< / span > < span class = "p" > ,< / span > < span class = "n" > optimize< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< span class = "c1" > # ...< / span >
< / code > < / pre > < / div >
< div class = "admonition warning" >
< p class = "admonition-title" > Warning< / p >
< p > Parameters created this way will not show up in the < code > list-strategies< / code > parameter count.< / p >
< / div >
< h3 id = "overriding-base-estimator" > Overriding Base estimator< a class = "headerlink" href = "#overriding-base-estimator" title = "Permanent link" > ¶ < / a > < / h3 >
< p > You can define your own estimator for Hyperopt by implementing < code > generate_estimator()< / code > in the Hyperopt subclass.< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "k" > class< / span > < span class = "nc" > MyAwesomeStrategy< / span > < span class = "p" > (< / span > < span class = "n" > IStrategy< / span > < span class = "p" > ):< / span >
< span class = "k" > class< / span > < span class = "nc" > HyperOpt< / span > < span class = "p" > :< / span >
< span class = "k" > def< / span > < span class = "nf" > generate_estimator< / span > < span class = "p" > (< / span > < span class = "n" > dimensions< / span > < span class = "p" > :< / span > < span class = "n" > List< / span > < span class = "p" > [< / span > < span class = "s1" > ' Dimension' < / span > < span class = "p" > ],< / span > < span class = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "s2" > " RF" < / span >
< / code > < / pre > < / div >
< p > Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the < a href = "https://scikit-optimize.github.io/" > scikit-optimize documentation< / a > ), or "an instance of a class that inherits from < code > RegressorMixin< / code > (from sklearn) and where the < code > predict< / code > method has an optional < code > return_std< / code > argument, which returns < code > std(Y | x)< / code > along with < code > E[Y | x]< / code > ".< / p >
< p > Some research will be necessary to find additional Regressors.< / p >
< p > Example for < code > ExtraTreesRegressor< / code > ("ET") with additional parameters:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "k" > class< / span > < span class = "nc" > MyAwesomeStrategy< / span > < span class = "p" > (< / span > < span class = "n" > IStrategy< / span > < span class = "p" > ):< / span >
< span class = "k" > class< / span > < span class = "nc" > HyperOpt< / span > < span class = "p" > :< / span >
< span class = "k" > def< / span > < span class = "nf" > generate_estimator< / span > < span class = "p" > (< / span > < span class = "n" > dimensions< / span > < span class = "p" > :< / span > < span class = "n" > List< / span > < span class = "p" > [< / span > < span class = "s1" > ' Dimension' < / span > < span class = "p" > ],< / span > < span class = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "kn" > from< / span > < span class = "nn" > skopt.learning< / span > < span class = "kn" > import< / span > < span class = "n" > ExtraTreesRegressor< / span >
< span class = "c1" > # Corresponds to " ET" - but allows additional parameters.< / span >
< span class = "k" > return< / span > < span class = "n" > ExtraTreesRegressor< / span > < span class = "p" > (< / span > < span class = "n" > n_estimators< / span > < span class = "o" > =< / span > < span class = "mi" > 100< / span > < span class = "p" > )< / span >
< / code > < / pre > < / div >
< p > The < code > dimensions< / code > parameter is the list of < code > skopt.space.Dimension< / code > objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the < code > skopt.learning.GaussianProcessRegressor< / code > estimator. Here's an example:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "k" > class< / span > < span class = "nc" > MyAwesomeStrategy< / span > < span class = "p" > (< / span > < span class = "n" > IStrategy< / span > < span class = "p" > ):< / span >
< span class = "k" > class< / span > < span class = "nc" > HyperOpt< / span > < span class = "p" > :< / span >
< span class = "k" > def< / span > < span class = "nf" > generate_estimator< / span > < span class = "p" > (< / span > < span class = "n" > dimensions< / span > < span class = "p" > :< / span > < span class = "n" > List< / span > < span class = "p" > [< / span > < span class = "s1" > ' Dimension' < / span > < span class = "p" > ],< / span > < span class = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "kn" > from< / span > < span class = "nn" > skopt.utils< / span > < span class = "kn" > import< / span > < span class = "n" > cook_estimator< / span >
< span class = "kn" > from< / span > < span class = "nn" > skopt.learning.gaussian_process.kernels< / span > < span class = "kn" > import< / span > < span class = "p" > (< / span > < span class = "n" > Matern< / span > < span class = "p" > ,< / span > < span class = "n" > ConstantKernel< / span > < span class = "p" > )< / span >
< span class = "n" > kernel_bounds< / span > < span class = "o" > =< / span > < span class = "p" > (< / span > < span class = "mf" > 0.0001< / span > < span class = "p" > ,< / span > < span class = "mi" > 10000< / span > < span class = "p" > )< / span >
< span class = "n" > kernel< / span > < span class = "o" > =< / span > < span class = "p" > (< / span >
< span class = "n" > ConstantKernel< / span > < span class = "p" > (< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_bounds< / span > < span class = "p" > )< / span > < span class = "o" > *< / span >
< span class = "n" > Matern< / span > < span class = "p" > (< / span > < span class = "n" > length_scale< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > ones< / span > < span class = "p" > (< / span > < span class = "nb" > len< / span > < span class = "p" > (< / span > < span class = "n" > dimensions< / span > < span class = "p" > )),< / span > < span class = "n" > length_scale_bounds< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "n" > kernel_bounds< / span > < span class = "k" > for< / span > < span class = "n" > d< / span > < span class = "ow" > in< / span > < span class = "n" > dimensions< / span > < span class = "p" > ],< / span > < span class = "n" > nu< / span > < span class = "o" > =< / span > < span class = "mf" > 2.5< / span > < span class = "p" > )< / span >
< span class = "p" > )< / span >
< span class = "n" > kernel< / span > < span class = "o" > +=< / span > < span class = "p" > (< / span >
< span class = "n" > ConstantKernel< / span > < span class = "p" > (< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_bounds< / span > < span class = "p" > )< / span > < span class = "o" > *< / span >
< span class = "n" > Matern< / span > < span class = "p" > (< / span > < span class = "n" > length_scale< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > ones< / span > < span class = "p" > (< / span > < span class = "nb" > len< / span > < span class = "p" > (< / span > < span class = "n" > dimensions< / span > < span class = "p" > )),< / span > < span class = "n" > length_scale_bounds< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "n" > kernel_bounds< / span > < span class = "k" > for< / span > < span class = "n" > d< / span > < span class = "ow" > in< / span > < span class = "n" > dimensions< / span > < span class = "p" > ],< / span > < span class = "n" > nu< / span > < span class = "o" > =< / span > < span class = "mf" > 1.5< / span > < span class = "p" > )< / span >
< span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > cook_estimator< / span > < span class = "p" > (< / span > < span class = "s2" > " GP" < / span > < span class = "p" > ,< / span > < span class = "n" > space< / span > < span class = "o" > =< / span > < span class = "n" > dimensions< / span > < span class = "p" > ,< / span > < span class = "n" > kernel< / span > < span class = "o" > =< / span > < span class = "n" > kernel< / span > < span class = "p" > ,< / span > < span class = "n" > n_restarts_optimizer< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > )< / span >
< / code > < / pre > < / div >
< div class = "admonition note" >
< p class = "admonition-title" > Note< / p >
< p > While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
If you're unsure about this, best use one of the Defaults (< code > "ET"< / code > has proven to be the most versatile) without further parameters.< / p >
< / div >
< h2 id = "space-options" > Space options< a class = "headerlink" href = "#space-options" title = "Permanent link" > ¶ < / a > < / h2 >
< p > For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:< / p >
< ul >
< li > < code > Categorical< / code > - Pick from a list of categories (e.g. < code > Categorical(['a', 'b', 'c'], name="cat")< / code > )< / li >
< li > < code > Integer< / code > - Pick from a range of whole numbers (e.g. < code > Integer(1, 10, name='rsi')< / code > )< / li >
< li > < code > SKDecimal< / code > - Pick from a range of decimal numbers with limited precision (e.g. < code > SKDecimal(0.1, 0.5, decimals=3, name='adx')< / code > ). < em > Available only with freqtrade< / em > .< / li >
< li > < code > Real< / code > - Pick from a range of decimal numbers with full precision (e.g. < code > Real(0.1, 0.5, name='adx')< / code > < / li >
< / ul >
< p > You can import all of these from < code > freqtrade.optimize.space< / code > , although < code > Categorical< / code > , < code > Integer< / code > and < code > Real< / code > are only aliases for their corresponding scikit-optimize Spaces. < code > SKDecimal< / code > is provided by freqtrade for faster optimizations.< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "kn" > from< / span > < span class = "nn" > freqtrade.optimize.space< / span > < span class = "kn" > import< / span > < span class = "n" > Categorical< / span > < span class = "p" > ,< / span > < span class = "n" > Dimension< / span > < span class = "p" > ,< / span > < span class = "n" > Integer< / span > < span class = "p" > ,< / span > < span class = "n" > SKDecimal< / span > < span class = "p" > ,< / span > < span class = "n" > Real< / span > < span class = "c1" > # noqa< / span >
< / code > < / pre > < / div >
< div class = "admonition hint" >
< p class = "admonition-title" > SKDecimal vs. Real< / p >
< p > We recommend to use < code > SKDecimal< / code > instead of the < code > Real< / code > space in almost all cases. While the Real space provides full accuracy (up to ~16 decimal places) - this precision is rarely needed, and leads to unnecessary long hyperopt times.< / p >
< p > Assuming the definition of a rather small space (< code > SKDecimal(0.10, 0.15, decimals=2, name='xxx')< / code > ) - SKDecimal will have 5 possibilities (< code > [0.10, 0.11, 0.12, 0.13, 0.14, 0.15]< / code > ).< / p >
< p > A corresponding real space < code > Real(0.10, 0.15 name='xxx')< / code > on the other hand has an almost unlimited number of possibilities (< code > [0.10, 0.010000000001, 0.010000000002, ... 0.014999999999, 0.01500000000]< / code > ).< / p >
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