151 lines
5.0 KiB
Python
151 lines
5.0 KiB
Python
import math, random
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hashpower = [float(x) for x in open('hashpower.csv').readlines()]
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# Target block time
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TARGET = 12
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# Should be 86400, but can reduce for a quicker sim
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SECONDS_IN_DAY = 86400
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# Look at the 1/x day exponential moving average
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EMA_FACTOR = 0.01
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# Damping factor for simple difficulty adjustment
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SIMPLE_ADJUST_DAMPING_FACTOR = 20
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# Maximum per-block diff adjustment (as fraction of current diff)
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SIMPLE_ADJUST_MAX = 0.5
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# Damping factor for quadratic difficulty adjustment
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QUADRATIC_ADJUST_DAMPING_FACTOR = 3
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# Maximum per-block diff adjustment (as fraction of current diff)
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QUADRATIC_ADJUST_MAX = 0.5
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# Threshold for bounded adjustor
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BOUNDED_ADJUST_THRESHOLD = 1.3
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# Bounded adjustment factor
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BOUNDED_ADJUST_FACTOR = 0.01
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# How many blocks back to look
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BLKS_BACK = 10
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# Naive difficulty adjustment factor
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NAIVE_ADJUST_FACTOR = 1/1024.
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# Produces a value according to the exponential distribution; used
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# to determine the time until the next block given an average block
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# time of t
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def expdiff(t):
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return -math.log(random.random()) * t
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# abs_sqr(3) = 9, abs_sqr(-7) = -49, etc
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def abs_sqr(x):
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return -(x**2) if x < 0 else x**2
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# Given an array of the most recent timestamps, and the most recent
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# difficulties, compute the next difficulty
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def simple_adjust(timestamps, diffs):
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if len(timestamps) < BLKS_BACK + 2:
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return diffs[-1]
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# Total interval between previous block and block a bit further back
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delta = timestamps[-2] - timestamps[-2-BLKS_BACK] + 0.0
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# Expected interval
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expected = TARGET * BLKS_BACK
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# Compute adjustment factor
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fac = 1 - (delta / expected - 1) / SIMPLE_ADJUST_DAMPING_FACTOR
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fac = max(min(fac, 1 + SIMPLE_ADJUST_MAX), 1 - SIMPLE_ADJUST_MAX)
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return diffs[-1] * fac
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# Alternative adjustment algorithm
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def quadratic_adjust(timestamps, diffs):
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if len(timestamps) < BLKS_BACK + 2:
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return diffs[-1]
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# Total interval between previous block and block a bit further back
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delta = timestamps[-2] - timestamps[-2-BLKS_BACK] + 0.0
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# Expected interval
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expected = TARGET * BLKS_BACK
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# Compute adjustment factor
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fac = 1 - abs_sqr(delta / expected - 1) / QUADRATIC_ADJUST_DAMPING_FACTOR
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fac = max(min(fac, 1 + QUADRATIC_ADJUST_MAX), 1 - QUADRATIC_ADJUST_MAX)
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return diffs[-1] * fac
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# Alternative adjustment algorithm
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def bounded_adjust(timestamps, diffs):
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if len(timestamps) < BLKS_BACK + 2:
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return diffs[-1]
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# Total interval between previous block and block a bit further back
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delta = timestamps[-2] - timestamps[-2-BLKS_BACK] + 0.0
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# Expected interval
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expected = TARGET * BLKS_BACK
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if delta / expected > BOUNDED_ADJUST_THRESHOLD:
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fac = (1 - BOUNDED_ADJUST_FACTOR)
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elif delta / expected < 1 / BOUNDED_ADJUST_THRESHOLD:
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fac = (1 + BOUNDED_ADJUST_FACTOR) ** (delta / expected)
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else:
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fac = 1
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return diffs[-1] * fac
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# Old Ethereum algorithm
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def old_adjust(timestamps, diffs):
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if len(timestamps) < 2:
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return diffs[-1]
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delta = timestamps[-1] - timestamps[-2]
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expected = TARGET * 0.693
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if delta > expected:
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fac = 1 - NAIVE_ADJUST_FACTOR
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else:
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fac = 1 + NAIVE_ADJUST_FACTOR
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return diffs[-1] * fac
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def test(source, adjust):
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# Variables to keep track of for stats purposes
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ema = maxema = minema = TARGET
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lthalf, gtdouble, lttq, gtft = 0, 0, 0, 0
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count = 0
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# Block times
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times = [0]
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# Block difficulty values
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diffs = [source[0]]
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# Next time to print status update
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nextprint = 10**6
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# Main loop
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while times[-1] < len(source) * SECONDS_IN_DAY:
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# Print status update every 10**6 seconds
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if times[-1] > nextprint:
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print '%d out of %d processed, ema %f' % \
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(times[-1], len(source) * SECONDS_IN_DAY, ema)
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nextprint += 10**6
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# Grab hashpower from data source
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hashpower = source[int(times[-1] // SECONDS_IN_DAY)]
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# Calculate new difficulty
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diffs.append(adjust(times, diffs))
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# Calculate next block time
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times.append(times[-1] + expdiff(diffs[-1] / hashpower))
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# Calculate min and max ema
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ema = ema * (1 - EMA_FACTOR) + (times[-1] - times[-2]) * EMA_FACTOR
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minema = min(minema, ema)
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maxema = max(maxema, ema)
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count += 1
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# Keep track of number of blocks we are below 75/50% or above
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# 133/200% of target
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if ema < TARGET * 0.75:
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lttq += 1
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if ema < TARGET * 0.5:
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lthalf += 1
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elif ema > TARGET * 1.33333:
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gtft += 1
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if ema > TARGET * 2:
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gtdouble += 1
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# Pop items to save memory
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if len(times) > 2000:
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times.pop(0)
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diffs.pop(0)
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print 'min', minema, 'max', maxema, 'avg', times[-1] / count, \
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'ema < half', lthalf * 1.0 / count, \
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'ema > double', gtdouble * 1.0 / count, \
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'ema < 3/4', lttq * 1.0 / count, \
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'ema > 4/3', gtft * 1.0 / count
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# Example usage
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# blkdiff.test(blkdiff.hashpower, blkdiff.simple_adjust)
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