-
-
Notifications
You must be signed in to change notification settings - Fork 270
/
hpoptimizer.go
303 lines (276 loc) · 9.55 KB
/
hpoptimizer.go
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
package optimizer
import (
"context"
"fmt"
"math"
"sync"
"github.com/c-bata/goptuna"
goptunaCMAES "github.com/c-bata/goptuna/cmaes"
goptunaSOBOL "github.com/c-bata/goptuna/sobol"
goptunaTPE "github.com/c-bata/goptuna/tpe"
"github.com/c9s/bbgo/pkg/fixedpoint"
"github.com/cheggaaa/pb/v3"
"github.com/sirupsen/logrus"
"golang.org/x/sync/errgroup"
)
// WARNING: the text here could only be lower cases
const (
// HpOptimizerObjectiveEquity optimize the parameters to maximize equity gain
HpOptimizerObjectiveEquity = "equity"
// HpOptimizerObjectiveProfit optimize the parameters to maximize trading profit
HpOptimizerObjectiveProfit = "profit"
// HpOptimizerObjectiveVolume optimize the parameters to maximize trading volume
HpOptimizerObjectiveVolume = "volume"
// HpOptimizerObjectiveProfitFactor optimize the parameters to maximize profit factor
HpOptimizerObjectiveProfitFactor = "profitfactor"
)
const (
// HpOptimizerAlgorithmTPE is the implementation of Tree-structured Parzen Estimators
HpOptimizerAlgorithmTPE = "tpe"
// HpOptimizerAlgorithmCMAES is the implementation Covariance Matrix Adaptation Evolution Strategy
HpOptimizerAlgorithmCMAES = "cmaes"
// HpOptimizerAlgorithmSOBOL is the implementation Quasi-monte carlo sampling based on Sobol sequence
HpOptimizerAlgorithmSOBOL = "sobol"
// HpOptimizerAlgorithmRandom is the implementation random search
HpOptimizerAlgorithmRandom = "random"
)
type HyperparameterOptimizeTrialResult struct {
Value fixedpoint.Value `json:"value"`
Parameters map[string]interface{} `json:"parameters"`
ID *int `json:"id,omitempty"`
State string `json:"state,omitempty"`
}
type HyperparameterOptimizeReport struct {
Name string `json:"studyName"`
Objective string `json:"objective"`
Parameters map[string]string `json:"domains"`
Best *HyperparameterOptimizeTrialResult `json:"best"`
Trials []*HyperparameterOptimizeTrialResult `json:"trials,omitempty"`
}
func buildBestHyperparameterOptimizeResult(study *goptuna.Study) *HyperparameterOptimizeTrialResult {
val, _ := study.GetBestValue()
params, _ := study.GetBestParams()
return &HyperparameterOptimizeTrialResult{
Value: fixedpoint.NewFromFloat(val),
Parameters: params,
}
}
func buildHyperparameterOptimizeTrialResults(study *goptuna.Study) []*HyperparameterOptimizeTrialResult {
trials, _ := study.GetTrials()
results := make([]*HyperparameterOptimizeTrialResult, len(trials))
for i, trial := range trials {
trialId := trial.ID
trialResult := &HyperparameterOptimizeTrialResult{
ID: &trialId,
Value: fixedpoint.NewFromFloat(trial.Value),
Parameters: trial.Params,
}
results[i] = trialResult
}
return results
}
type HyperparameterOptimizer struct {
SessionName string
Config *Config
// Workaround for goptuna/tpe parameter suggestion. Remove this after fixed.
// ref: https://github.com/c-bata/goptuna/issues/236
paramSuggestionLock sync.Mutex
}
func (o *HyperparameterOptimizer) buildStudy(trialFinishChan chan goptuna.FrozenTrial) (*goptuna.Study, error) {
var studyOpts = make([]goptuna.StudyOption, 0, 2)
// maximum the profit, volume, equity gain, ...etc
studyOpts = append(studyOpts, goptuna.StudyOptionDirection(goptuna.StudyDirectionMaximize))
// disable search log and collect trial progress
studyOpts = append(studyOpts, goptuna.StudyOptionLogger(nil))
studyOpts = append(studyOpts, goptuna.StudyOptionTrialNotifyChannel(trialFinishChan))
// the search algorithm
var sampler goptuna.Sampler = nil
var relativeSampler goptuna.RelativeSampler = nil
switch o.Config.Algorithm {
case HpOptimizerAlgorithmRandom:
sampler = goptuna.NewRandomSampler()
case HpOptimizerAlgorithmTPE:
sampler = goptunaTPE.NewSampler()
case HpOptimizerAlgorithmCMAES:
relativeSampler = goptunaCMAES.NewSampler(goptunaCMAES.SamplerOptionNStartupTrials(5))
case HpOptimizerAlgorithmSOBOL:
relativeSampler = goptunaSOBOL.NewSampler()
}
if sampler != nil {
studyOpts = append(studyOpts, goptuna.StudyOptionSampler(sampler))
} else {
studyOpts = append(studyOpts, goptuna.StudyOptionRelativeSampler(relativeSampler))
}
return goptuna.CreateStudy(o.SessionName, studyOpts...)
}
func (o *HyperparameterOptimizer) buildParamDomains() (map[string]string, []paramDomain) {
labelPaths := make(map[string]string)
domains := make([]paramDomain, 0, len(o.Config.Matrix))
for _, selector := range o.Config.Matrix {
var domain paramDomain
switch selector.Type {
case selectorTypeRange, selectorTypeRangeFloat:
if selector.Step.IsZero() {
domain = &floatRangeDomain{
paramDomainBase: paramDomainBase{
label: selector.Label,
path: selector.Path,
},
min: selector.Min.Float64(),
max: selector.Max.Float64(),
}
} else {
domain = &floatDiscreteRangeDomain{
paramDomainBase: paramDomainBase{
label: selector.Label,
path: selector.Path,
},
min: selector.Min.Float64(),
max: selector.Max.Float64(),
step: selector.Step.Float64(),
}
}
case selectorTypeRangeInt:
if selector.Step.IsZero() {
domain = &intRangeDomain{
paramDomainBase: paramDomainBase{
label: selector.Label,
path: selector.Path,
},
min: selector.Min.Int(),
max: selector.Max.Int(),
}
} else {
domain = &intStepRangeDomain{
paramDomainBase: paramDomainBase{
label: selector.Label,
path: selector.Path,
},
min: selector.Min.Int(),
max: selector.Max.Int(),
step: selector.Step.Int(),
}
}
case selectorTypeIterate, selectorTypeString:
domain = &stringDomain{
paramDomainBase: paramDomainBase{
label: selector.Label,
path: selector.Path,
},
options: selector.Values,
}
case selectorTypeBool:
domain = &boolDomain{
paramDomainBase: paramDomainBase{
label: selector.Label,
path: selector.Path,
},
}
default:
// unknown parameter type, skip
continue
}
labelPaths[selector.Label] = selector.Path
domains = append(domains, domain)
}
return labelPaths, domains
}
func (o *HyperparameterOptimizer) buildObjective(executor Executor, configJson []byte, paramDomains []paramDomain) goptuna.FuncObjective {
var metricValueFunc MetricValueFunc
switch o.Config.Objective {
case HpOptimizerObjectiveProfit:
metricValueFunc = TotalProfitMetricValueFunc
case HpOptimizerObjectiveVolume:
metricValueFunc = TotalVolume
case HpOptimizerObjectiveEquity:
metricValueFunc = TotalEquityDiff
case HpOptimizerObjectiveProfitFactor:
metricValueFunc = ProfitFactorMetricValueFunc
}
return func(trial goptuna.Trial) (float64, error) {
trialConfig, err := func(trialConfig []byte) ([]byte, error) {
o.paramSuggestionLock.Lock()
defer o.paramSuggestionLock.Unlock()
for _, domain := range paramDomains {
if patch, err := domain.buildPatch(&trial); err != nil {
return nil, err
} else if patchedConfig, err := patch.ApplyIndent(trialConfig, " "); err != nil {
return nil, err
} else {
trialConfig = patchedConfig
}
}
return trialConfig, nil
}(configJson)
if err != nil {
return 0.0, err
}
summary, err := executor.Execute(trialConfig)
if err != nil {
return 0.0, err
}
// By config, the Goptuna optimize the parameters by maximize the objective output.
return metricValueFunc(summary), nil
}
}
func (o *HyperparameterOptimizer) Run(ctx context.Context, executor Executor, configJson []byte) (*HyperparameterOptimizeReport, error) {
labelPaths, paramDomains := o.buildParamDomains()
objective := o.buildObjective(executor, configJson, paramDomains)
maxEvaluation := o.Config.MaxEvaluation
numOfProcesses := o.Config.Executor.LocalExecutorConfig.MaxNumberOfProcesses
if numOfProcesses > maxEvaluation {
numOfProcesses = maxEvaluation
}
maxEvaluationPerProcess := maxEvaluation / numOfProcesses
if maxEvaluation%numOfProcesses > 0 {
maxEvaluationPerProcess++
}
trialFinishChan := make(chan goptuna.FrozenTrial, 128)
allTrailFinishChan := make(chan struct{})
bar := pb.Full.Start(maxEvaluation)
bar.SetTemplateString(`{{ string . "log" | green}} | {{counters . }} {{bar . }} {{percent . }} {{etime . }} {{rtime . "ETA %s"}}`)
go func() {
defer close(allTrailFinishChan)
var bestVal = math.Inf(-1)
for result := range trialFinishChan {
log.WithFields(logrus.Fields{"ID": result.ID, "evaluation": result.Value, "state": result.State}).Debug("trial finished")
if result.State == goptuna.TrialStateFail {
log.WithFields(result.Params).Errorf("failed at trial #%d", result.ID)
}
if result.Value > bestVal {
bestVal = result.Value
}
bar.Set("log", fmt.Sprintf("best value: %v", bestVal))
bar.Increment()
}
}()
study, err := o.buildStudy(trialFinishChan)
if err != nil {
return nil, err
}
eg, studyCtx := errgroup.WithContext(ctx)
study.WithContext(studyCtx)
for i := 0; i < numOfProcesses; i++ {
processEvaluations := maxEvaluationPerProcess
if processEvaluations > maxEvaluation {
processEvaluations = maxEvaluation
}
eg.Go(func() error {
return study.Optimize(objective, processEvaluations)
})
maxEvaluation -= processEvaluations
}
if err := eg.Wait(); err != nil && ctx.Err() != context.Canceled {
return nil, err
}
close(trialFinishChan)
<-allTrailFinishChan
bar.Finish()
return &HyperparameterOptimizeReport{
Name: o.SessionName,
Objective: o.Config.Objective,
Parameters: labelPaths,
Best: buildBestHyperparameterOptimizeResult(study),
Trials: buildHyperparameterOptimizeTrialResults(study),
}, nil
}