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67f8b1c32c
Currently support the following search algorithms: - Tree-structured Parzen Estimators (tpe, default) - Covariance Matrix Adaptation Evolution Strategy (cmaes) - Quasi-monte carlo sampling based on Sobol sequence (sobol) - random search (random) And the following objective function: - profit - volume - equity
45 lines
1.2 KiB
YAML
45 lines
1.2 KiB
YAML
# usage:
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#
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# go run ./cmd/bbgo optimizeex --config bollmaker_ethusdt.yaml --optimizer-config optimizer-hyperparam-search.yaml
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#
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---
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# The search algorithm. Supports the following algorithms:
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# - tpe: (default) Tree-structured Parzen Estimators
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# - cmaes: Covariance Matrix Adaptation Evolution Strategy
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# - sobol: Quasi-monte carlo sampling based on Sobol sequence
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# - random: random search
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# Reference: https://c-bata.medium.com/practical-bayesian-optimization-in-go-using-goptuna-edf97195fcb5
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algorithm: tpe
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# The objective function to be maximized. Possible options are:
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# - profit: by trading profit
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# - volume: by trading volume
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# - equity: by equity difference
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objectiveBy: equity
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# Maximum number of search evaluations.
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maxEvaluation: 1000
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executor:
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type: local
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local:
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maxNumberOfProcesses: 10
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matrix:
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- type: string # alias: iterate
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path: '/exchangeStrategies/0/bollmaker/interval'
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values: ["1m", "5m"]
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- type: rangeInt
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label: window
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path: '/exchangeStrategies/0/bollmaker/defaultBollinger/window'
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min: 12
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max: 240
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- type: rangeFloat # alias: range
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path: '/exchangeStrategies/0/bollmaker/spread'
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min: 0.001
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max: 0.002
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- type: bool
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path: '/exchangeStrategies/0/bollmaker/buyBelowNeutralSMA' |