feat(core): implement core MEV bot functionality with market scanning and Uniswap V3 pricing
Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
This commit is contained in:
Krypto Kajun
425
vendor/github.com/ethereum/go-ethereum/metrics/sample.go
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vendored
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425
vendor/github.com/ethereum/go-ethereum/metrics/sample.go
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package metrics
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import (
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"math"
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"math/rand"
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"slices"
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"sync"
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"time"
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)
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const rescaleThreshold = time.Hour
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// Sample maintains a statistically-significant selection of values from
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// a stream.
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type Sample interface {
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Snapshot() *sampleSnapshot
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Clear()
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Update(int64)
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}
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var (
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_ Sample = (*ExpDecaySample)(nil)
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_ Sample = (*UniformSample)(nil)
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_ Sample = (*resettingSample)(nil)
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)
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// sampleSnapshot is a read-only copy of a Sample.
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type sampleSnapshot struct {
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count int64
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values []int64
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max int64
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min int64
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mean float64
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sum int64
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variance float64
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}
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// newSampleSnapshotPrecalculated creates a read-only sampleSnapShot, using
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// precalculated sums to avoid iterating the values
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func newSampleSnapshotPrecalculated(count int64, values []int64, min, max, sum int64) *sampleSnapshot {
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if len(values) == 0 {
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return &sampleSnapshot{
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count: count,
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values: values,
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}
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}
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return &sampleSnapshot{
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count: count,
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values: values,
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max: max,
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min: min,
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mean: float64(sum) / float64(len(values)),
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sum: sum,
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}
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}
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// newSampleSnapshot creates a read-only sampleSnapShot, and calculates some
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// numbers.
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func newSampleSnapshot(count int64, values []int64) *sampleSnapshot {
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var (
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max int64 = math.MinInt64
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min int64 = math.MaxInt64
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sum int64
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)
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for _, v := range values {
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sum += v
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if v > max {
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max = v
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}
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if v < min {
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min = v
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}
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}
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return newSampleSnapshotPrecalculated(count, values, min, max, sum)
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}
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// Count returns the count of inputs at the time the snapshot was taken.
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func (s *sampleSnapshot) Count() int64 { return s.count }
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// Max returns the maximal value at the time the snapshot was taken.
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func (s *sampleSnapshot) Max() int64 { return s.max }
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// Mean returns the mean value at the time the snapshot was taken.
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func (s *sampleSnapshot) Mean() float64 { return s.mean }
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// Min returns the minimal value at the time the snapshot was taken.
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func (s *sampleSnapshot) Min() int64 { return s.min }
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// Percentile returns an arbitrary percentile of values at the time the
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// snapshot was taken.
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func (s *sampleSnapshot) Percentile(p float64) float64 {
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return SamplePercentile(s.values, p)
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}
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// Percentiles returns a slice of arbitrary percentiles of values at the time
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// the snapshot was taken.
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func (s *sampleSnapshot) Percentiles(ps []float64) []float64 {
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return CalculatePercentiles(s.values, ps)
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}
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// Size returns the size of the sample at the time the snapshot was taken.
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func (s *sampleSnapshot) Size() int { return len(s.values) }
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// StdDev returns the standard deviation of values at the time the snapshot was
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// taken.
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func (s *sampleSnapshot) StdDev() float64 {
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if s.variance == 0.0 {
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s.variance = SampleVariance(s.mean, s.values)
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}
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return math.Sqrt(s.variance)
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}
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// Sum returns the sum of values at the time the snapshot was taken.
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func (s *sampleSnapshot) Sum() int64 { return s.sum }
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// Values returns a copy of the values in the sample.
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func (s *sampleSnapshot) Values() []int64 {
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return slices.Clone(s.values)
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}
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// Variance returns the variance of values at the time the snapshot was taken.
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func (s *sampleSnapshot) Variance() float64 {
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if s.variance == 0.0 {
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s.variance = SampleVariance(s.mean, s.values)
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}
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return s.variance
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}
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// ExpDecaySample is an exponentially-decaying sample using a forward-decaying
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// priority reservoir. See Cormode et al's "Forward Decay: A Practical Time
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// Decay Model for Streaming Systems".
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//
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// <http://dimacs.rutgers.edu/~graham/pubs/papers/fwddecay.pdf>
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type ExpDecaySample struct {
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alpha float64
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count int64
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mutex sync.Mutex
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reservoirSize int
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t0, t1 time.Time
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values *expDecaySampleHeap
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rand *rand.Rand
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}
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// NewExpDecaySample constructs a new exponentially-decaying sample with the
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// given reservoir size and alpha.
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func NewExpDecaySample(reservoirSize int, alpha float64) Sample {
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s := &ExpDecaySample{
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alpha: alpha,
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reservoirSize: reservoirSize,
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t0: time.Now(),
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values: newExpDecaySampleHeap(reservoirSize),
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}
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s.t1 = s.t0.Add(rescaleThreshold)
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return s
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}
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// SetRand sets the random source (useful in tests)
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func (s *ExpDecaySample) SetRand(prng *rand.Rand) Sample {
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s.rand = prng
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return s
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}
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// Clear clears all samples.
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func (s *ExpDecaySample) Clear() {
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s.mutex.Lock()
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defer s.mutex.Unlock()
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s.count = 0
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s.t0 = time.Now()
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s.t1 = s.t0.Add(rescaleThreshold)
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s.values.Clear()
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}
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// Snapshot returns a read-only copy of the sample.
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func (s *ExpDecaySample) Snapshot() *sampleSnapshot {
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s.mutex.Lock()
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defer s.mutex.Unlock()
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var (
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samples = s.values.Values()
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values = make([]int64, len(samples))
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max int64 = math.MinInt64
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min int64 = math.MaxInt64
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sum int64
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)
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for i, item := range samples {
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v := item.v
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values[i] = v
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sum += v
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if v > max {
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max = v
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}
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if v < min {
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min = v
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}
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}
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return newSampleSnapshotPrecalculated(s.count, values, min, max, sum)
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}
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// Update samples a new value.
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func (s *ExpDecaySample) Update(v int64) {
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if !metricsEnabled {
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return
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}
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s.update(time.Now(), v)
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}
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// update samples a new value at a particular timestamp. This is a method all
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// its own to facilitate testing.
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func (s *ExpDecaySample) update(t time.Time, v int64) {
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s.mutex.Lock()
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defer s.mutex.Unlock()
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s.count++
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if s.values.Size() == s.reservoirSize {
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s.values.Pop()
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}
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var f64 float64
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if s.rand != nil {
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f64 = s.rand.Float64()
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} else {
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f64 = rand.Float64()
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}
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s.values.Push(expDecaySample{
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k: math.Exp(t.Sub(s.t0).Seconds()*s.alpha) / f64,
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v: v,
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})
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if t.After(s.t1) {
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values := s.values.Values()
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t0 := s.t0
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s.values.Clear()
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s.t0 = t
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s.t1 = s.t0.Add(rescaleThreshold)
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for _, v := range values {
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v.k = v.k * math.Exp(-s.alpha*s.t0.Sub(t0).Seconds())
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s.values.Push(v)
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}
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}
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}
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// SamplePercentile returns an arbitrary percentile of the slice of int64.
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func SamplePercentile(values []int64, p float64) float64 {
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return CalculatePercentiles(values, []float64{p})[0]
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}
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// CalculatePercentiles returns a slice of arbitrary percentiles of the slice of
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// int64. This method returns interpolated results, so e.g. if there are only two
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// values, [0, 10], a 50% percentile will land between them.
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//
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// Note: As a side-effect, this method will also sort the slice of values.
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// Note2: The input format for percentiles is NOT percent! To express 50%, use 0.5, not 50.
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func CalculatePercentiles(values []int64, ps []float64) []float64 {
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scores := make([]float64, len(ps))
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size := len(values)
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if size == 0 {
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return scores
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}
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slices.Sort(values)
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for i, p := range ps {
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pos := p * float64(size+1)
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if pos < 1.0 {
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scores[i] = float64(values[0])
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} else if pos >= float64(size) {
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scores[i] = float64(values[size-1])
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} else {
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lower := float64(values[int(pos)-1])
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upper := float64(values[int(pos)])
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scores[i] = lower + (pos-math.Floor(pos))*(upper-lower)
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}
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}
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return scores
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}
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// SampleVariance returns the variance of the slice of int64.
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func SampleVariance(mean float64, values []int64) float64 {
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if len(values) == 0 {
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return 0.0
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}
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var sum float64
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for _, v := range values {
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d := float64(v) - mean
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sum += d * d
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}
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return sum / float64(len(values))
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}
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// UniformSample implements a uniform sample using Vitter's Algorithm R.
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//
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// <http://www.cs.umd.edu/~samir/498/vitter.pdf>
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type UniformSample struct {
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count int64
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mutex sync.Mutex
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reservoirSize int
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values []int64
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rand *rand.Rand
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}
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// NewUniformSample constructs a new uniform sample with the given reservoir
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// size.
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func NewUniformSample(reservoirSize int) Sample {
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return &UniformSample{
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reservoirSize: reservoirSize,
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values: make([]int64, 0, reservoirSize),
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}
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}
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// SetRand sets the random source (useful in tests)
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func (s *UniformSample) SetRand(prng *rand.Rand) Sample {
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s.rand = prng
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return s
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}
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// Clear clears all samples.
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func (s *UniformSample) Clear() {
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s.mutex.Lock()
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defer s.mutex.Unlock()
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s.count = 0
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clear(s.values)
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}
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// Snapshot returns a read-only copy of the sample.
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func (s *UniformSample) Snapshot() *sampleSnapshot {
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s.mutex.Lock()
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values := slices.Clone(s.values)
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count := s.count
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s.mutex.Unlock()
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return newSampleSnapshot(count, values)
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}
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// Update samples a new value.
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func (s *UniformSample) Update(v int64) {
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if !metricsEnabled {
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return
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}
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s.mutex.Lock()
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defer s.mutex.Unlock()
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s.count++
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if len(s.values) < s.reservoirSize {
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s.values = append(s.values, v)
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return
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}
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var r int64
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if s.rand != nil {
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r = s.rand.Int63n(s.count)
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} else {
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r = rand.Int63n(s.count)
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}
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if r < int64(len(s.values)) {
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s.values[int(r)] = v
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}
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}
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// expDecaySample represents an individual sample in a heap.
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type expDecaySample struct {
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k float64
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v int64
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}
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func newExpDecaySampleHeap(reservoirSize int) *expDecaySampleHeap {
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return &expDecaySampleHeap{make([]expDecaySample, 0, reservoirSize)}
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}
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// expDecaySampleHeap is a min-heap of expDecaySamples.
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// The internal implementation is copied from the standard library's container/heap
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type expDecaySampleHeap struct {
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s []expDecaySample
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}
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func (h *expDecaySampleHeap) Clear() {
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h.s = h.s[:0]
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}
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func (h *expDecaySampleHeap) Push(s expDecaySample) {
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n := len(h.s)
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h.s = h.s[0 : n+1]
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h.s[n] = s
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h.up(n)
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}
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func (h *expDecaySampleHeap) Pop() expDecaySample {
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n := len(h.s) - 1
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h.s[0], h.s[n] = h.s[n], h.s[0]
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h.down(0, n)
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n = len(h.s)
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s := h.s[n-1]
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h.s = h.s[0 : n-1]
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return s
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}
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func (h *expDecaySampleHeap) Size() int {
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return len(h.s)
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}
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func (h *expDecaySampleHeap) Values() []expDecaySample {
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return h.s
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}
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func (h *expDecaySampleHeap) up(j int) {
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for {
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i := (j - 1) / 2 // parent
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if i == j || !(h.s[j].k < h.s[i].k) {
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break
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}
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h.s[i], h.s[j] = h.s[j], h.s[i]
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j = i
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}
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}
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func (h *expDecaySampleHeap) down(i, n int) {
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for {
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j1 := 2*i + 1
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if j1 >= n || j1 < 0 { // j1 < 0 after int overflow
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break
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}
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j := j1 // left child
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if j2 := j1 + 1; j2 < n && !(h.s[j1].k < h.s[j2].k) {
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j = j2 // = 2*i + 2 // right child
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}
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if !(h.s[j].k < h.s[i].k) {
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break
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}
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h.s[i], h.s[j] = h.s[j], h.s[i]
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i = j
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}
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}
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