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basefee_discrete_autocrawler_pi.py
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from argparse import ArgumentParser
from core.lottery import DarkfiTable
from core.utils import *
from core.darkie import Darkie
from tqdm import tqdm
import os
from core.strategy import random_strategy
AVG_LEN = 10
KP_STEP=0.0001
KP_SEARCH=-0.047919999999999366
KI_STEP=0.0001
KI_SEARCH=-0.00055
RUNNING_TIME=1000
NODES = 1000
SHIFTING = 0.05
highest_apr = 0.05
highest_acc = 0.2
highest_cc_acc = 0.01
highest_staked = 0.3
lowest_apr2target_diff = 1
KP='kp'
KI='ki'
KP_RANGE_MULTIPLIER = 1.1
KI_RANGE_MULTIPLIER = 1.1
highest_gain = (KP_SEARCH, KI_SEARCH)
parser = ArgumentParser()
parser.add_argument('-p', '--high-precision', action='store_false', default=False)
parser.add_argument('-r', '--randomizenodes', action='store_true', default=True)
parser.add_argument('-t', '--rand-running-time', action='store_true', default=True)
parser.add_argument('-d', '--debug', action='store_false')
args = parser.parse_args()
high_precision = args.high_precision
randomize_nodes = args.randomizenodes
rand_running_time = args.rand_running_time
debug = args.debug
def experiment(controller_type=CONTROLLER_TYPE_DISCRETE, fkp=0, fki=0, distribution=[], hp=True):
dt = DarkfiTable(ERC20DRK, RUNNING_TIME, controller_type, kp=-0.010399999999938556, ki=-0.0365999996461878, kd=0.03840000000000491, r_kp=-0.719, r_ki=1.6, r_kd=0.1, fee_kp=fkp, fee_ki=fki)
RND_NODES = random.randint(5, NODES) if randomize_nodes else NODES
for idx in range(0,RND_NODES):
darkie = Darkie(distribution[idx], strategy=random_strategy(EPOCH_LENGTH))
dt.add_darkie(darkie)
acc, cc_acc, apy, reward, stake_ratio, apr = dt.background(rand_running_time, hp)
return acc, cc_acc, apy, reward, stake_ratio, apr
def multi_trial_exp(kp, ki, distribution = [], hp=True):
global highest_apr
global highest_cc_acc
global highest_acc
global highest_staked
global highest_gain
global lowest_apr2target_diff
new_record=False
accs = []
aprs = []
rewards = []
stakes_ratios = []
aprs = []
cc_accs = []
for i in range(0, AVG_LEN):
acc, cc_acc, apy, reward, stake_ratio, apr = experiment(CONTROLLER_TYPE_DISCRETE, fkp=kp, fki=ki, distribution=distribution, hp=hp)
accs += [acc]
cc_accs += [cc_acc]
rewards += [reward]
aprs += [apr]
stakes_ratios += [stake_ratio]
avg_acc = float(sum(accs))/AVG_LEN
avg_cc_acc = float(sum(cc_accs))/AVG_LEN if len(cc_accs) else 0
avg_reward = float(sum(rewards))/AVG_LEN
avg_staked = float(sum(stakes_ratios))/AVG_LEN
avg_apr = float(sum(aprs))/AVG_LEN
buff = 'avg(acc): {}, avg(cc_acc): {}, avg(apr): {},avg(reward): {}, avg(stake ratio): {}, kp: {}, ki:{}, '.format(avg_acc, avg_cc_acc, avg_apr, avg_reward, avg_staked, kp, ki)
print('avg_cc_acc: {}'.format(avg_cc_acc))
if avg_cc_acc > highest_cc_acc:
#if avg_apr > 0:
gain = (kp, ki)
acc_gain = (avg_apr, gain)
apr2target_diff = math.fabs(avg_apr - float(TARGET_APR))
#if avg_acc > highest_acc and apr2target_diff < 0.08:
#if avg_cc_acc > highest_cc_acc:
new_record = True
highest_apr = avg_apr
highest_acc = avg_acc
highest_cc_acc = avg_cc_acc
highest_staked = avg_staked
highest_gain = (kp, ki)
lowest_apr2target_diff = apr2target_diff
with open('log'+os.sep+"highest_gain.txt", 'w') as f:
f.write(buff)
return buff, new_record
def crawler(crawl, range_multiplier, step=0.1):
start = None
if crawl==KP:
start = highest_gain[0]
elif crawl==KI:
start = highest_gain[1]
range_start = (start*range_multiplier if start <=0 else -1*start)
range_end = (-1*start if start<=0 else range_multiplier*start)
# if number of steps under 10 step resize the step to 50
while (range_end-range_start)/step < 10:
range_start -= SHIFTING
range_end += SHIFTING
step /= 10
while True:
try:
crawl_range = np.arange(range_start, range_end, step)
break
except Exception as e:
print('start: {}, end: {}, step: {}, exp: {}'.format(range_start, rang_end, step, e))
step*=10
np.random.shuffle(crawl_range)
crawl_range = tqdm(crawl_range)
mu = ERC20DRK/NODES
distribution = [random.gauss(mu, mu/10) for i in range(NODES)]
for i in crawl_range:
kp = i if crawl==KP else highest_gain[0]
ki = i if crawl==KI else highest_gain[1]
buff, new_record = multi_trial_exp(kp, ki, distribution, hp=high_precision)
crawl_range.set_description('highest:{} / {}'.format(highest_cc_acc, buff))
if new_record:
break
while True:
prev_highest_gain = highest_gain
# kp crawl
crawler(KP, KP_RANGE_MULTIPLIER, KP_STEP)
if highest_gain[0] == prev_highest_gain[0]:
KP_RANGE_MULTIPLIER+=1
KP_STEP/=10
else:
start = highest_gain[0]
range_start = (start*KP_RANGE_MULTIPLIER if start <=0 else -1*start) - SHIFTING
range_end = (-1*start if start<=0 else KP_RANGE_MULTIPLIER*start) + SHIFTING
while (range_end - range_start)/KP_STEP >500:
#if KP_STEP < 0.1:
KP_STEP*=2
KP_RANGE_MULTIPLIER-=1
#TODO (res) shouldn't the range also shrink?
# not always true.
# how to distinguish between thrinking range, and large step?
# good strategy is step shoudn't > 0.1
# range also should be > 0.8
# what about range multiplier?
# ki crawl
crawler(KI, KI_RANGE_MULTIPLIER, KI_STEP)
if highest_gain[1] == prev_highest_gain[1]:
KI_RANGE_MULTIPLIER+=1
KI_STEP/=10
else:
start = highest_gain[1]
range_start = (start*KI_RANGE_MULTIPLIER if start <=0 else -1*start) - SHIFTING
range_end = (-1*start if start<=0 else KI_RANGE_MULTIPLIER*start) + SHIFTING
while (range_end - range_start)/KI_STEP >500:
#print('range_end: {}, range_start: {}, ki_step: {}'.format(range_end, range_start, KI_STEP))
#if KP_STEP < 1:
KI_STEP*=2
KI_RANGE_MULTIPLIER-=1