[reesarch/lotterysim] pool zero advantage demonestration

script/research/lotterysim/pool_advantage.py

@@ -0,0 +1,60 @@
+import os
+import numpy
+from core.strategy import *
+from core.lottery import *
+import matplotlib.pyplot as plt
+import scipy.stats as stats
+import math
+from draw import draw
+
+os.system("rm log/*_feedback.hist; rm log/*_output.hist")
+
+RUNNING_TIME = int(input("running time:"))
+NODES=100
+
+if __name__ == "__main__":
+    egalitarian = ERC20DRK/NODES
+    darkies = []
+
+    darkie = Darkie(egalitarian, strategy=random_strategy())
+    darkies += [darkie]
+    darkie = Darkie(int(ERC20DRK-egalitarian), strategy=random_strategy())
+    darkies += [darkie]
+
+    airdrop = ERC20DRK
+    effective_airdrop  = 0
+    for darkie in darkies:
+        effective_airdrop+=darkie.stake
+    print("network airdrop: {}, staked token: {}/{}% on {} nodes".format(airdrop, effective_airdrop, effective_airdrop/airdrop*100, len(darkies)))
+    dt = DarkfiTable(airdrop, 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)
+    for darkie in darkies:
+        dt.add_darkie(darkie)
+    acc, avg_apy, avg_reward, stake_ratio, avg_apr = dt.background(rand_running_time=False)
+    sum_zero_stake = sum([darkie.stake for darkie in darkies[NODES:]])
+    print('acc: {}, avg(apr): {}, avg(reward): {}, stake_ratio: {}'.format(acc, avg_apr, avg_reward, stake_ratio))
+    print('total stake of 0mint: {}, ratio: {}'.format(sum_zero_stake, sum_zero_stake/ERC20DRK))
+    dt.write()
+    aprs = []
+    fortuners = 0.0
+    for darkie in darkies:
+        aprs += [float(darkie.apr_scaled_to_runningtime())]
+        if darkie.initial_stake[-1] - darkie.initial_stake[0] > 0:
+            fortuners+=1
+
+    print('fortuners: {}'.format(str(fortuners/len(darkies))))
+    total  = sum([darkie.stake for darkie in darkies])
+    for idx, darkie in enumerate(darkies):
+        print('{}% idx: {}, stake:{}'.format(float(darkie.stake/total), idx, darkie.stake))
+    # distribution of aprs
+    aprs = sorted(aprs)
+    mu = float(sum(aprs)/len(aprs))
+    shifted_aprs = [apr - mu for apr in aprs]
+    plt.plot([apr*100 for apr in aprs])
+    plt.title('annual percentage return, avg: {:}'.format(mu*100))
+    plt.savefig('img/apr_distribution.png')
+    plt.show()
+
+
+    variance = sum(shifted_aprs)/(len(aprs)-1)
+    print('mu: {}, variance: {}'.format(str(mu), str(variance)))
+    draw()