diff --git a/script/research/lotterysim/core/constants.py b/script/research/lotterysim/core/constants.py index b59adff11..30dce2a45 100644 --- a/script/research/lotterysim/core/constants.py +++ b/script/research/lotterysim/core/constants.py @@ -41,4 +41,4 @@ BASE_L_HP = Num(BASE_L) # HEADSTART AIRDROP period ~ 1 month HEADSTART_AIRDROP=2880 -SLASHING_RATIO = 0.0001 +SLASHING_RATIO = 0.01 diff --git a/script/research/lotterysim/core/darkie.py b/script/research/lotterysim/core/darkie.py index faf6e41f9..1defd289f 100644 --- a/script/research/lotterysim/core/darkie.py +++ b/script/research/lotterysim/core/darkie.py @@ -27,13 +27,18 @@ class Darkie(): def vesting_wrapped_initial_stake(self): #print('initial stake: {}, corresponding vesting: {}'.format(self.initial_stake[0], self.vesting[int((self.slot)/VESTING_PERIOD)])) # note index is previous slot since update_vesting is called after background execution. - return self.current_vesting() if self.slot>0 else self.initial_stake[-1] + #return self.current_vesting() if self.slot>0 else self.initial_stake[-1] + return (self.current_vesting() if self.slot>0 else self.initial_stake[-1]) + self.initial_stake[-1] def apr_scaled_to_runningtime(self): initial_stake = self.vesting_wrapped_initial_stake() #print('stake: {}, initial_stake: {}'.format(self.stake, initial_stake)) assert self.stake >= initial_stake, 'stake: {}, initial_stake: {}, slot: {}, current: {}, previous: {} vesting'.format(self.stake, initial_stake, self.slot, self.current_vesting(), self.prev_vesting()) - return Num(self.stake - initial_stake) / Num(initial_stake) * Num(ONE_YEAR/(self.slot/EPOCH_LENGTH)) if self.slot> 0 and initial_stake>0 else 0 + #if self.slot%100==0: + #print('stake: {}, initial stake: {}'.format(self.stake, initial_stake)) + #print(self.initial_stake) + apr = Num(self.stake - initial_stake) / Num(initial_stake) * Num(ONE_YEAR/(self.slot/EPOCH_LENGTH)) if self.slot> 0 and initial_stake>0 else 0 + return apr def staked_tokens(self): ''' @@ -63,7 +68,7 @@ class Darkie(): x = (Num(1) if hp else 1) - (Num(tune_parameter) if hp else tune_parameter) c = (x.ln() if type(x)==Num else math.log(x)) sigmas = [ c/((self.Sigma+EPSILON)**i) * ( ((L_HP if hp else L)/fact(i)) ) for i in range(1, k+1) ] - scaled_target = approx_target_in_zk(sigmas, Num(stake)) + (BASE_L_HP if hp else BASE_L) + scaled_target = approx_target_in_zk(sigmas, Num(stake)) + ((BASE_L_HP if hp else BASE_L) if self.slot < HEADSTART_AIRDROP else 0) return scaled_target if self.slot % EPOCH_LENGTH ==0 and self.slot > 0: @@ -71,7 +76,8 @@ class Darkie(): # staked ratio is added in strategy self.strategy.set_ratio(self.slot, apr) # epoch stake is added - self.initial_stake +=[self.stake] + if self.slot < HEADSTART_AIRDROP: + self.initial_stake +=[self.stake] #if self.slot == HEADSTART_AIRDROP: # self.initial_stake += [self.stake] T = target(self.f, self.strategy.staked_value(self.stake)) diff --git a/script/research/lotterysim/core/lottery.py b/script/research/lotterysim/core/lottery.py index c08617f09..a95c68976 100644 --- a/script/research/lotterysim/core/lottery.py +++ b/script/research/lotterysim/core/lottery.py @@ -67,8 +67,8 @@ class DarkfiTable: if random.random() < SLASHING_RATIO: self.darkies.remove(self.darkies[i]) print('stakeholder {} slashed'.format(i)) - break - self.darkies[i].update_stake(self.rewards[-1]) + else: + self.darkies[i].update_stake(self.rewards[-1]) break # resolve finalization self.Sigma += self.rewards[-1] @@ -93,7 +93,7 @@ class DarkfiTable: break self.darkies[darkie_winning_idx].resync_stake(resync_reward) self.Sigma += resync_reward - rt_range.set_description('issuance {} DRK, acc: {}, stake = {}%, sr: {}%, reward:{}'.format(round(sum(self.rewards),2), round(acc,2), round(total_stake/self.Sigma*100 if self.Sigma>0 else 0,2), self.avg_stake_ratio()*100, self.rewards[-1])) + rt_range.set_description('epoch: {}, issuance {} DRK, acc: {}, stake = {}%, sr: {}%, reward:{}, apr: {}'.format(int(count/EPOCH_LENGTH), round(self.Sigma,2), round(acc,2), round(total_stake/self.Sigma*100 if self.Sigma>0 else 0,2), round(self.avg_stake_ratio()*100,2) , round(self.rewards[-1],2), round(self.avg_apr(),2))) #print('[2]stake: {}, sigma: {}, reward: {}'.format(total_stake, self.Sigma, self.rewards[-1])) assert(round(total_stake,1) <= round(self.Sigma,1)) count+=1 diff --git a/script/research/lotterysim/discrete_instance_pi_headstart.py b/script/research/lotterysim/discrete_instance_pi_headstart.py index 21a0ad8e5..207e8fee2 100644 --- a/script/research/lotterysim/discrete_instance_pi_headstart.py +++ b/script/research/lotterysim/discrete_instance_pi_headstart.py @@ -14,7 +14,8 @@ NODES=1000 if __name__ == "__main__": darkies = [Darkie(0, strategy=LinearStrategy(EPOCH_LENGTH)) for _ in range(NODES)] - dt = DarkfiTable(0, RUNNING_TIME, CONTROLLER_TYPE_DISCRETE, kp=-0.010399999999938556, ki=-0.0365999996461878, kd=0.03840000000000491, r_kp=-2.53, r_ki=29.5, r_kd=53.77) + #dt = DarkfiTable(0, RUNNING_TIME, CONTROLLER_TYPE_DISCRETE, kp=-0.010399999999938556, ki=-0.0365999996461878, kd=0.03840000000000491, r_kp=-2.53, r_ki=29.5, r_kd=53.77) + dt = DarkfiTable(0, RUNNING_TIME, CONTROLLER_TYPE_DISCRETE, kp=-0.010399999999938556, ki=-0.0365999996461878, kd=0.03840000000000491, r_kp=-0.719, r_ki=1.6, r_kd=0.1) for darkie in darkies: dt.add_darkie(darkie) acc, avg_apy, avg_reward, stake_ratio, avg_apr = dt.background(rand_running_time=False) diff --git a/script/research/lotterysim/primary_discrete_auto_crawler.py b/script/research/lotterysim/primary_discrete_auto_crawler.py index b3a62f406..46fc9e52b 100644 --- a/script/research/lotterysim/primary_discrete_auto_crawler.py +++ b/script/research/lotterysim/primary_discrete_auto_crawler.py @@ -39,7 +39,7 @@ highest_gain = (KP_SEARCH, KI_SEARCH, KD_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('-r', '--randomizenodes', action='store_false', 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() @@ -49,7 +49,7 @@ rand_running_time = args.rand_running_time debug = args.debug def experiment(controller_type=CONTROLLER_TYPE_DISCRETE, rkp=0, rki=0, rkd=0, distribution=[], hp=True): - dt = DarkfiTable(ERC20DRK, RUNNING_TIME, controller_type, kp=-0.010399999999938556, ki=-0.0365999996461878, kd=0.03840000000000491, r_kp=rkp, r_ki=rki, r_kd=rkd) + dt = DarkfiTable(ERC20DRK, RUNNING_TIME, controller_type, kp=-0.010399999999938556, ki=-0.0365999996461878, kd=0.038400000000004f91, r_kp=rkp, r_ki=rki, r_kd=rkd) 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))