1327 lines
51 KiB
Plaintext
1327 lines
51 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"#import networkx as nx\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import scipy.stats as sts\n",
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"import seaborn as sns\n",
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"\n",
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"%matplotlib inline\n",
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"\n",
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"#import conviction files\n",
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"#from conviction_helpers import *\n",
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"#from conviction_system_logic3 import *\n",
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"from bonding_curve_eq import *"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"System initialization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"hatch_raise = 100000 # fiat units\n",
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"hatch_price = .1 #fiat per tokens\n",
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"theta = .5 #share of funds going to funding pool at launch\n",
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"\n",
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"R0 = hatch_raise*(1-theta)\n",
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"F0 = hatch_raise*theta\n",
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"S0 = hatch_raise/hatch_price\n",
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"\n",
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"kappa = 2\n",
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"V0 = invariant(R0,S0,kappa)\n",
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"P0 = spot_price(R0, V0, kappa)\n",
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"\n",
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"dust = 10**-8"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"agent initialization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"#number of agents\n",
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"n= 3\n",
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"\n",
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"#gain factors\n",
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"g = np.random.normal(2, .5, size=n)\n",
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"phat0 = g*F0/S0 #derivative, integral and proportion\n",
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"#agents as controllers, co-steering\n",
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"\n",
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"#wakeup rates\n",
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"gamma = sts.expon.rvs(loc=1,scale=5, size=n)\n",
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"\n",
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"#holdings fiat\n",
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"h = sts.expon.rvs( loc=100,scale=1000, size=n)\n",
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"\n",
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"#holdings tokens\n",
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"s_dist = sts.expon.rvs(loc=10, scale=10, size=n)\n",
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"s0 = s_dist/sum(s_dist)*S0\n",
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"\n",
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"#lambda for revenue process\n",
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"lam = 200\n",
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"\n",
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"#phi for exiting funds\n",
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"phi = .05\n",
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"\n",
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"#beta is param for armijo rule\n",
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"beta = .9"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(array([1., 0., 0., 0., 0., 0., 0., 0., 1., 1.]),\n",
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" array([2.8983858 , 2.95780209, 3.01721838, 3.07663467, 3.13605095,\n",
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" 3.19546724, 3.25488353, 3.31429981, 3.3737161 , 3.43313239,\n",
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" 3.49254868]),\n",
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" <a list of 10 Patch objects>)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
|
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.hist(gamma)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"params= {\n",
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" 'kappa': [kappa],\n",
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" 'lambda': [lam],\n",
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" 'gains': [g],\n",
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" 'rates':[1/gamma],\n",
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" 'population':[n],\n",
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" 'beta':[beta],\n",
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" 'phi': [phi],\n",
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" 'invariant': [V0],\n",
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" 'dust' : [dust]}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"initial_conditions = {'holdings': h,\n",
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" 'tokens': s0,\n",
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" 'supply': S0,\n",
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" 'prices': phat0,\n",
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" 'funds':F0,\n",
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" 'reserve': R0,\n",
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" 'spot_price': P0,\n",
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" 'actions': {}}"
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]
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},
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{
|
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"cell_type": "code",
|
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
|
|
"text/plain": [
|
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"{'holdings': array([1628.962088 , 609.07074557, 970.43373875]),\n",
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" 'tokens': array([107809.69284187, 241390.85830628, 650799.44885184]),\n",
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" 'supply': 1000000.0,\n",
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" 'prices': array([0.08959197, 0.11065636, 0.06829373]),\n",
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" 'funds': 50000.0,\n",
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" 'reserve': 50000.0,\n",
|
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" 'spot_price': 0.09999999999999999,\n",
|
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" 'actions': {}}"
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]
|
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},
|
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"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
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"initial_conditions"
|
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]
|
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},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#change in F (revenue and spending accounted for)\n",
|
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"def revenue_process(params, step, sL, s):\n",
|
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" lam = params['lambda']\n",
|
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" rv = sts.expon.rvs(loc = 0, scale=1/lam)\n",
|
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" delF= 1-1/lam+rv\n",
|
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" \n",
|
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" #avoid the crash (temporary hacks, tune martingale process better)\n",
|
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" #if delF <1:\n",
|
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" # if s['funds'] <1000:\n",
|
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" # delF =100\n",
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" \n",
|
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" print(delF)\n",
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" \n",
|
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" return({'delF':delF})"
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]
|
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},
|
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{
|
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"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def update_funds(params, step, sL, s, _input):\n",
|
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" \n",
|
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" funds = s['funds']*_input['delF']\n",
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" \n",
|
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" key = 'funds'\n",
|
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" value = funds\n",
|
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" \n",
|
|
" return (key, value)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def update_prices(params, step, sL, s, _input):\n",
|
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" \n",
|
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" g = params['gains']\n",
|
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" phat = g*s['funds']/s['supply']\n",
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" \n",
|
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" key = 'prices'\n",
|
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" value = phat\n",
|
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" \n",
|
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" return (key, value)"
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#change in F (revenue and spending accounted for)\n",
|
|
"def choose_agents(params, step, sL, s):\n",
|
|
" n = params['population']\n",
|
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" rates = params['rates']\n",
|
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" \n",
|
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" agents = []\n",
|
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" for a in range(n):\n",
|
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" sq_gap = (s['spot_price']-s['prices'][a])**2\n",
|
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" pr = (rates[a]+sq_gap)/(1+sq_gap) #rates when sq_gap =0, 1 when sq_gap -> infty\n",
|
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" rv = np.random.rand()\n",
|
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" if rv < pr:\n",
|
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" agents.append(a)\n",
|
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" \n",
|
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" #shuffle\n",
|
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" shuffled_agents =np.random.choice(agents,len(agents), False) \n",
|
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" \n",
|
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" return({'agents':shuffled_agents})"
|
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]
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def agent_actions(params, step, sL, s, _input):\n",
|
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" \n",
|
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" R = s['reserve']\n",
|
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" S = s['supply']\n",
|
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" F = s['funds']\n",
|
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" V0 = params['invariant']\n",
|
|
" P=s['spot_price']\n",
|
|
" \n",
|
|
" actions = []\n",
|
|
" for a in _input['agents']:\n",
|
|
" h_a = s['holdings'][a]\n",
|
|
" phat_a = s['prices'][a]\n",
|
|
" s_a = s['tokens'][a]\n",
|
|
" beta = params['beta']\n",
|
|
"\n",
|
|
" if P>phat_a: #equiv: pbar(0)>phat_a\n",
|
|
" mech = 'burn'\n",
|
|
" \n",
|
|
" #approx for burn s.t. p=phat\n",
|
|
" #armijo style\n",
|
|
" amt = s_a\n",
|
|
" \n",
|
|
" def pbar(amt):\n",
|
|
" output = withdraw_with_tax(amt, R,S, V0, params['phi'], params['kappa'])\n",
|
|
"\n",
|
|
" if not(output[2])>0:\n",
|
|
" return np.Infinity\n",
|
|
" else:\n",
|
|
" return output[2]\n",
|
|
"\n",
|
|
" if amt > 10**-8:\n",
|
|
" while pbar(amt)< phat_a:\n",
|
|
" amt = amt*beta\n",
|
|
"\n",
|
|
" else: # P<phat_a; #equiv pbar(0)<phat_a\n",
|
|
" mech = 'bond'\n",
|
|
" #approx for buy s.t. p=phat\n",
|
|
" #armijo style\n",
|
|
" amt = h_a\n",
|
|
" \n",
|
|
" def pbar(amt):\n",
|
|
" output = mint(amt, R,S, V0, params['kappa'])\n",
|
|
"\n",
|
|
" if not(output[1])>0:\n",
|
|
" return 0\n",
|
|
" else:\n",
|
|
" return output[1]\n",
|
|
" \n",
|
|
" if amt > params['dust']:\n",
|
|
" while pbar(amt)> phat_a:\n",
|
|
" amt = amt*beta\n",
|
|
" \n",
|
|
" action = {'agent':a, 'mech':mech, 'amt':amt, 'pbar':pbar(amt),'posterior':{}}\n",
|
|
" \n",
|
|
" if action['mech'] == 'bond':\n",
|
|
" h_a = h_a-amt\n",
|
|
" dS, pbar = mint(amt, R,S, V0, params['kappa'])\n",
|
|
" R = R+amt\n",
|
|
" S = S+dS\n",
|
|
" s_a = s_a+dS\n",
|
|
" P = spot_price(R, V0, kappa)\n",
|
|
" \n",
|
|
" elif action['mech'] == 'burn':\n",
|
|
" s_a = s_a-amt\n",
|
|
" dR, pbar = withdraw(amt, R,S, V0, params['kappa'])\n",
|
|
" R = R-dR\n",
|
|
" F = F + params['phi']*dR\n",
|
|
" S = S-amt\n",
|
|
" h_a = h_a + (1-params['phi'])*dR\n",
|
|
" P = spot_price(R, V0, kappa)\n",
|
|
" \n",
|
|
" action['posterior'] = {'F':F, 'S':S, 'R':R,'P':P, 'a':a,'s_a':s_a, 'h_a':h_a}\n",
|
|
" actions.append(action)\n",
|
|
" \n",
|
|
" key = 'actions'\n",
|
|
" value = actions\n",
|
|
" \n",
|
|
" return (key, value)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def resolve_actions(params, step, sL, s):\n",
|
|
" \n",
|
|
" H_a = s['holdings']\n",
|
|
" S_a = s['tokens']\n",
|
|
" \n",
|
|
" actions = s['actions']\n",
|
|
" \n",
|
|
" for action in actions:\n",
|
|
" a= action['agent']\n",
|
|
" H_a[a] = action['posterior']['h_a']\n",
|
|
" S_a[a] = action['posterior']['s_a']\n",
|
|
" \n",
|
|
" #last action only\n",
|
|
" F = action['posterior']['F']\n",
|
|
" R = action['posterior']['R']\n",
|
|
" P = action['posterior']['P']\n",
|
|
" S = action['posterior']['S']\n",
|
|
" \n",
|
|
" return({'F':F, 'S':S, 'R':R,'P':P, 'S_a':S_a, 'H_a':H_a})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def update_F(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" F = _input['F']\n",
|
|
" \n",
|
|
" key = 'funds'\n",
|
|
" value = F\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_S(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" S = _input['S']\n",
|
|
" \n",
|
|
" key = 'supply'\n",
|
|
" value = S\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_R(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" R = _input['R']\n",
|
|
" \n",
|
|
" key = 'reserve'\n",
|
|
" value = R\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_P(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" P = _input['P']\n",
|
|
" \n",
|
|
" key = 'spot_price'\n",
|
|
" value = P\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_holdings(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" H_a = _input['H_a']\n",
|
|
" \n",
|
|
" key = 'holdings'\n",
|
|
" value = H_a\n",
|
|
" \n",
|
|
" return (key, value)\n",
|
|
"\n",
|
|
"def update_tokens(params, step, sL, s, _input):\n",
|
|
" \n",
|
|
" S_a = _input['S_a']\n",
|
|
" \n",
|
|
" sumS = np.sum(S_a)\n",
|
|
" S = _input['S']\n",
|
|
" \n",
|
|
" tokens = S_a*S/sumS\n",
|
|
" \n",
|
|
" key = 'tokens'\n",
|
|
" value = tokens\n",
|
|
" \n",
|
|
" return (key, value)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
|
|
"# The Partial State Update Blocks\n",
|
|
"partial_state_update_blocks = [\n",
|
|
" { \n",
|
|
" 'policies': { \n",
|
|
" #new proposals or new participants\n",
|
|
" 'random': revenue_process\n",
|
|
" },\n",
|
|
" 'variables': {\n",
|
|
" 'funds': update_funds,\n",
|
|
" 'prices': update_prices\n",
|
|
" }\n",
|
|
" },\n",
|
|
" {\n",
|
|
" 'policies': {\n",
|
|
" 'random': choose_agents\n",
|
|
" },\n",
|
|
" 'variables': { \n",
|
|
" 'actions': agent_actions, \n",
|
|
" }\n",
|
|
" },\n",
|
|
" {\n",
|
|
" 'policies': {\n",
|
|
" 'act': resolve_actions,\n",
|
|
" },\n",
|
|
" 'variables': {\n",
|
|
" 'funds': update_F, #\n",
|
|
" 'supply': update_S, \n",
|
|
" 'reserve': update_R,\n",
|
|
" 'spot_price': update_P,\n",
|
|
" 'holdings': update_holdings,\n",
|
|
" 'tokens': update_tokens\n",
|
|
" }\n",
|
|
" }\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_periods_per_run = 100\n",
|
|
"monte_carlo_runs = 1\n",
|
|
"\n",
|
|
"from cadCAD.configuration.utils import config_sim\n",
|
|
"simulation_parameters = config_sim({\n",
|
|
" 'T': range(time_periods_per_run),\n",
|
|
" 'N': monte_carlo_runs,\n",
|
|
" 'M': params\n",
|
|
"})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[{'N': 1, 'T': range(0, 100), 'M': {'kappa': 2, 'lambda': 200, 'gains': array([1.79183936, 2.21312719, 1.36587453]), 'rates': array([0.34501963, 0.29451016, 0.28632385]), 'population': 3, 'beta': 0.9, 'phi': 0.05, 'invariant': 20000000.0, 'dust': 1e-08}}]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from cadCAD.configuration import append_configs\n",
|
|
"# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
|
|
"# The configurations above are then packaged into a `Configuration` object\n",
|
|
"append_configs(\n",
|
|
" initial_state=initial_conditions, #dict containing variable names and initial values\n",
|
|
" partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n",
|
|
" sim_configs=simulation_parameters #dict containing simulation parameters\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tabulate import tabulate\n",
|
|
"from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
|
|
"from cadCAD import configs\n",
|
|
"import pandas as pd\n",
|
|
"\n",
|
|
"exec_mode = ExecutionMode()\n",
|
|
"multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
|
|
"run = Executor(exec_context=multi_proc_ctx, configs=configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" __________ ____ \n",
|
|
" ________ __ _____/ ____/ | / __ \\\n",
|
|
" / ___/ __` / __ / / / /| | / / / /\n",
|
|
" / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
|
|
" \\___/\\__,_/\\__,_/\\____/_/ |_/_____/ \n",
|
|
" by BlockScience\n",
|
|
" \n",
|
|
"Execution Mode: multi_proc: [<cadCAD.configuration.Configuration object at 0x7f9a1a674c90>]\n",
|
|
"Configurations: [<cadCAD.configuration.Configuration object at 0x7f9a1a674c90>]\n",
|
|
"1.0059732834560666\n",
|
|
"1.0003979136632812\n",
|
|
"1.007758363792569\n",
|
|
"0.9995539944773082\n",
|
|
"0.9963116699702721\n",
|
|
"1.0001086336225762\n",
|
|
"0.997927941962102\n"
|
|
]
|
|
},
|
|
{
|
|
"ename": "UnboundLocalError",
|
|
"evalue": "local variable 'action' referenced before assignment",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mRemoteTraceback\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;31mRemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/multiprocess/pool.py\", line 121, in worker\n result = (True, func(*args, **kwds))\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/multiprocess/pool.py\", line 44, in mapstar\n return list(map(*args))\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/pathos/helpers/mp_helper.py\", line 15, in <lambda>\n func = lambda args: f(*args)\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/__init__.py\", line 47, in <lambda>\n results = p.map(lambda t: t[0](t[1], t[2], t[3], t[4], t[5], t[6]), l)\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/simulation.py\", line 229, in simulation\n list(range(runs))\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/pathos/threading.py\", line 134, in map\n return _pool.map(star(f), zip(*args)) # chunksize\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/multiprocess/pool.py\", line 268, in map\n return self._map_async(func, iterable, mapstar, chunksize).get()\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/multiprocess/pool.py\", line 657, in get\n raise self._value\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/multiprocess/pool.py\", line 121, in worker\n result = (True, func(*args, **kwds))\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/multiprocess/pool.py\", line 44, in mapstar\n return list(map(*args))\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/pathos/helpers/mp_helper.py\", line 15, in <lambda>\n func = lambda args: f(*args)\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/simulation.py\", line 228, in <lambda>\n lambda run: execute_run(sweep_dict, states_list, configs, env_processes, time_seq, run),\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/simulation.py\", line 219, in execute_run\n sweep_dict, states_list_copy, configs, env_processes, time_seq, run\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/simulation.py\", line 190, in run_pipeline\n sweep_dict, simulation_list, configs, env_processes, time_step, run\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/simulation.py\", line 166, in state_update_pipeline\n sweep_dict, sub_step, states_list, simulation_list, s_conf, p_conf, env_processes, time_step, run\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/simulation.py\", line 113, in partial_state_update\n self.get_policy_input(sweep_dict, sub_step, sH, last_in_obj, policy_funcs)\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/simulation.py\", line 49, in get_policy_input\n col_results = get_col_results(sweep_dict, sub_step, sL, s, funcs)\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/simulation.py\", line 36, in get_col_results\n return list(map(lambda f: f(sweep_dict, sub_step, sL, s), funcs))\n File \"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/cadCAD/engine/simulation.py\", line 36, in <lambda>\n return list(map(lambda f: f(sweep_dict, sub_step, sL, s), funcs))\n File \"<ipython-input-13-c4285b60d5ba>\", line 14, in resolve_actions\n F = action['posterior']['F']\nUnboundLocalError: local variable 'action' referenced before assignment\n\"\"\"",
|
|
"\nThe above exception was the direct cause of the following exception:\n",
|
|
"\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-19-6e7972e764e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mraw_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_field\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_result\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/cadCAD/engine/__init__.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexec_context\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mExecutionMode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmulti_proc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;31m# if len(self.configs) > 1:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0msimulations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexec_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation_execs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_dict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_lists\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs_structs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartial_state_updates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartial_state_updates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/cadCAD/engine/__init__.py\u001b[0m in \u001b[0;36mparallelize_simulations\u001b[0;34m(simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, Ns)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation_execs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_dict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_lists\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs_structs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mPPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfigs_structs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 48\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pathos/multiprocessing.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, f, *args, **kwds)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0mAbstractWorkerPool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AbstractWorkerPool__map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0m_pool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_serve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_pool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# chunksize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractWorkerPool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mimap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/multiprocess/pool.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, func, iterable, chunksize)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ma\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mthat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m '''\n\u001b[0;32m--> 268\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapstar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstarmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/multiprocess/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 655\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 656\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 657\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 659\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'action' referenced before assignment"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"i = 0\n",
|
|
"verbose = False\n",
|
|
"results = {}\n",
|
|
"for raw_result, tensor_field in run.execute():\n",
|
|
" result = pd.DataFrame(raw_result)\n",
|
|
" if verbose:\n",
|
|
" print()\n",
|
|
" print(f\"Tensor Field: {type(tensor_field)}\")\n",
|
|
" print(tabulate(tensor_field, headers='keys', tablefmt='psql'))\n",
|
|
" print(f\"Output: {type(result)}\")\n",
|
|
" print(tabulate(result, headers='keys', tablefmt='psql'))\n",
|
|
" print()\n",
|
|
" results[i] = {}\n",
|
|
" results[i]['result'] = result\n",
|
|
" results[i]['simulation_parameters'] = simulation_parameters[i]\n",
|
|
" i += 1\n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"experiment_index = 0\n",
|
|
"df = results[experiment_index]['result']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.supply.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.funds.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"(df.funds.diff()/df.funds).hist()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf = df[df.substep == 3].copy()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['token_wts'] = (rdf.tokens/rdf.supply)\n",
|
|
"rdf['wt_mean_price'] = (rdf.token_wts*rdf.prices).apply(sum)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['holding_wts'] = (rdf.holdings/rdf.holdings.apply(sum))\n",
|
|
"rdf['h_wt_mean_price'] = (rdf.holding_wts*rdf.prices).apply(sum)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['wealth'] = rdf.holdings + rdf.spot_price*rdf.tokens"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['wealth_wts'] = rdf.wealth/rdf.wealth.apply(sum)\n",
|
|
"rdf['w_wt_mean_price'] = (rdf.wealth_wts*rdf.prices).apply(sum)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.prices.apply(np.min).plot()\n",
|
|
"rdf.prices.apply(np.median).plot()\n",
|
|
"rdf.prices.apply(np.mean).plot()\n",
|
|
"rdf.wt_mean_price.plot()\n",
|
|
"rdf.h_wt_mean_price.plot()\n",
|
|
"rdf.w_wt_mean_price.plot()\n",
|
|
"rdf.prices.apply(np.max).plot()\n",
|
|
"rdf.spot_price.plot()\n",
|
|
"plt.legend(['min', 'median','mean','tok wt mean','hold wt mean','wealth wt mean','max', 'spot'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.prices.apply(np.median).plot()\n",
|
|
"rdf.prices.apply(np.mean).plot()\n",
|
|
"rdf.wt_mean_price.plot()\n",
|
|
"rdf.h_wt_mean_price.plot()\n",
|
|
"rdf.w_wt_mean_price.plot()\n",
|
|
"rdf.spot_price.plot()\n",
|
|
"plt.legend(['median','mean','tok wt mean','hold wt mean','wealth wt mean', 'spot'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.prices.apply(np.min).plot(logy=True)\n",
|
|
"rdf.prices.apply(np.median).plot(logy=True)\n",
|
|
"rdf.prices.apply(np.mean).plot(logy=True)\n",
|
|
"rdf.wt_mean_price.plot(logy=True)\n",
|
|
"rdf.h_wt_mean_price.plot(logy=True)\n",
|
|
"rdf.w_wt_mean_price.plot(logy=True)\n",
|
|
"rdf.prices.apply(np.max).plot(logy=True)\n",
|
|
"rdf.spot_price.plot(logy=True)\n",
|
|
"plt.legend(['min', 'median','mean','tok wt mean','hold wt mean','wealth wt mean','max', 'spot'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['median_price']=rdf.prices.apply(np.median)\n",
|
|
"rdf['mean_price']=rdf.prices.apply(np.mean)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"(np.sign(rdf['mean_price']-rdf['spot_price'])*(rdf['mean_price']-rdf['spot_price'])**2).apply(np.log10).plot(alpha=1)\n",
|
|
"(-np.sign(rdf['mean_price']-rdf['spot_price'])*(rdf['mean_price']-rdf['spot_price'])**2).apply(np.log10).plot(alpha=.5)\n",
|
|
"plt.legend(['over','under'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['est_err'] = rdf.spot_price - rdf.wt_mean_price\n",
|
|
"rdf['sq_est_err'] = rdf['est_err']**2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.est_err.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.est_err.apply(np.abs).plot(logy=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#head T\n",
|
|
"T = 50\n",
|
|
"rdf.head(T).prices.apply(np.min).plot()\n",
|
|
"rdf.head(T).prices.apply(np.median).plot()\n",
|
|
"rdf.head(T).prices.apply(np.mean).plot()\n",
|
|
"rdf.head(T).wt_mean_price.plot()\n",
|
|
"rdf.head(T).h_wt_mean_price.plot()\n",
|
|
"rdf.head(T).w_wt_mean_price.plot()\n",
|
|
"rdf.head(T).prices.apply(np.max).plot()\n",
|
|
"rdf.head(T).spot_price.plot()\n",
|
|
"plt.legend(['min', 'median','mean','tok wt mean','hold wt mean','wealth wt mean','max', 'spot'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"T = 50\n",
|
|
"rdf.tail(T).prices.apply(np.min).plot()\n",
|
|
"rdf.tail(T).prices.apply(np.median).plot()\n",
|
|
"rdf.tail(T).prices.apply(np.mean).plot()\n",
|
|
"rdf.tail(T).wt_mean_price.plot()\n",
|
|
"rdf.tail(T).h_wt_mean_price.plot()\n",
|
|
"rdf.tail(T).w_wt_mean_price.plot()\n",
|
|
"rdf.tail(T).prices.apply(np.max).plot()\n",
|
|
"rdf.tail(T).spot_price.plot()\n",
|
|
"plt.legend(['min', 'median','mean','tok wt mean','hold wt mean','wealth wt mean','max', 'spot'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tx_data = rdf.actions.values\n",
|
|
"transactions = []\n",
|
|
"states = []\n",
|
|
"for t in range(time_periods_per_run):\n",
|
|
" for tx in range(len(tx_data[t])):\n",
|
|
" states.append(tx_data[t][tx]['posterior'])\n",
|
|
" transactions.append(tx_data[t][tx])\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sdf = pd.DataFrame(states)\n",
|
|
"tdf = pd.DataFrame(transactions).drop('posterior', axis=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ind=tdf[tdf.amt==0].index\n",
|
|
"tdf.drop(ind, inplace=True)\n",
|
|
"sdf.drop(ind, inplace=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"txdf = sdf.merge(tdf, right_index=True, left_index=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"txdf.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"emas = ['P', 'pbar']\n",
|
|
"for com in [2,4,8,16]:\n",
|
|
" k = 'pbar_ewma'+str(com)\n",
|
|
" emas.append(k)\n",
|
|
" txdf[k] = txdf.pbar.ewm(com).mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"txdf[emas].plot(alpha=.8)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"txdf[emas].plot(alpha=.8)\n",
|
|
"plt.axis([0, 3000, .095, .115])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"txdf['dP'] = txdf.P.diff()\n",
|
|
"txdf['percent_dP'] = txdf.P.pct_change().apply(abs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"txdf.plot.scatter(x='R', y='dP', c='percent_dP', alpha=.7, logx=True)\n",
|
|
"plt.gca().set_facecolor('cyan') "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"txdf.plot.scatter(x='a',y='dP', c='percent_dP', alpha=.7)\n",
|
|
"plt.gca().set_facecolor('cyan') "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sns.scatterplot(x=\"pbar\", y=\"P\", data=txdf) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#txdf.plot(x='pbar',y='P')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sns.jointplot(x=\"pbar\", y=\"P\",kind=\"reg\", data=txdf.replace([np.inf, -np.inf], np.nan).dropna(how=\"all\")) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"txdf['P_err'] = txdf.P-txdf.pbar"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sns.jointplot(x=\"agent\", y=\"P_err\",kind=\"reg\", data=txdf.replace([np.inf, -np.inf], np.nan).dropna(how=\"all\")) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sns.jointplot(x='index',y=\"P\",kind=\"reg\", data=txdf.replace([np.inf, -np.inf], np.nan).dropna(how=\"all\").reset_index()) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sns.scatterplot(x='index',y=\"P_err\", hue = \"pbar\", data=txdf.replace([np.inf, -np.inf], np.nan).dropna(how=\"all\").reset_index()) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sns.scatterplot(x='index',y=\"P\", hue = \"pbar\", data=txdf.replace([np.inf, -np.inf], np.nan).dropna(how=\"all\").reset_index()) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"txdf['P_err_factor'] = txdf.P/txdf.pbar"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sns.scatterplot(x='index',y=\"P_err_factor\", hue = \"pbar\", data=txdf.replace([np.inf, -np.inf], np.nan).dropna(how=\"all\").reset_index()) \n",
|
|
"plt.gca().set_yscale('log')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tx_summary=tdf[['agent','mech','pbar','amt']].groupby(['agent','mech']).agg(['median','count']).T.iloc[:-1].T"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tx_summary"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tx_summary.pbar['median'].plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sdf['P'].plot(logx=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sdf['P'].plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sdf.F.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bond_amts = [tdf.iloc[k].amt for k in range(time_periods_per_run) if tdf.iloc[k].mech=='bond']\n",
|
|
"burn_amts = [tdf.iloc[k].amt for k in range(time_periods_per_run) if tdf.iloc[k].mech=='burn']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.hist(bond_amts, bins=20)\n",
|
|
"plt.yscale('log')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.hist(burn_amts, bins=20)\n",
|
|
"plt.yscale('log')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['invariant'] = rdf.supply.apply(lambda x: x**kappa)/rdf.reserve"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.plot(x='reserve', y='supply', kind='scatter', alpha=.5)\n",
|
|
"axis = plt.axis()\n",
|
|
"xrange = np.arange(axis[0], axis[1], (axis[1]-axis[0])/100)\n",
|
|
"yrange = np.array([supply(x, V0, kappa) for x in xrange ])\n",
|
|
"plt.plot(xrange, yrange, 'y')\n",
|
|
"plt.title('Bonding Curve Invariant')\n",
|
|
"plt.legend(['Invariant', 'Observed Data'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def gini(x):\n",
|
|
"\n",
|
|
" # Mean absolute difference\n",
|
|
" mad = np.abs(np.subtract.outer(x, x)).mean()\n",
|
|
" # Relative mean absolute difference\n",
|
|
" rmad = mad/np.mean(x)\n",
|
|
" # Gini coefficient\n",
|
|
" g = 0.5 * rmad\n",
|
|
" return g"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.hist(rdf.iloc[-1].holdings)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['gini_h'] = rdf.holdings.apply(gini)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.gini_h.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.hist(rdf.iloc[-1].tokens)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['gini_s'] = rdf.tokens.apply(gini)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.gini_s.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.tokens.apply(np.count_nonzero).plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['asset_value'] = rdf.holdings + rdf.spot_price*rdf.tokens"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.hist(rdf.iloc[-1].asset_value)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['gini'] = rdf.asset_value.apply(gini)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf.gini.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rdf['pref_gap'] = (rdf.prices - rdf.spot_price)/rdf.spot_price"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.hist(rdf.iloc[-7:].pref_gap, bins=7)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|