conviction/abc_sim-Copy2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#import conviction files\n",
    "#from conviction_helpers import *\n",
    "#from conviction_system_logic3 import *\n",
    "from bonding_curve_eq import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "System initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hatch_raise = 100000 # fiat units\n",
    "hatch_price = .1 #fiat per tokens\n",
    "theta = .5 #share of funds going to funding pool at launch\n",
    "\n",
    "R0 = hatch_raise*(1-theta)\n",
    "F0 = hatch_raise*theta\n",
    "S0 = hatch_raise/hatch_price\n",
    "\n",
    "kappa = 2\n",
    "V0 = invariant(R0,S0,kappa)\n",
    "P0 = spot_price(R0, V0, kappa)\n",
    "\n",
    "dust = 10**-8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "agent initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#number of agents\n",
    "n= 100\n",
    "\n",
    "#gain factors\n",
    "g = np.random.normal(2, .5, size=n)\n",
    "phat0 = g*F0/S0 #derivative, integral and proportion\n",
    "#agents as controllers, co-steering\n",
    "\n",
    "#wakeup rates\n",
    "gamma =  sts.expon.rvs(loc=1,scale=5, size=n)\n",
    "\n",
    "#copy_cat_sensitivity\n",
    "nu = .5*np.random.rand(n)\n",
    "\n",
    "#holdings fiat\n",
    "h = sts.expon.rvs( loc=100,scale=1000, size=n)\n",
    "\n",
    "#holdings tokens\n",
    "s_dist = sts.expon.rvs(loc=10, scale=10, size=n)\n",
    "s0 = s_dist/sum(s_dist)*S0\n",
    "\n",
    "#lambda for revenue process\n",
    "lam = 200\n",
    "\n",
    "#phi for exiting funds\n",
    "phi = .05\n",
    "\n",
    "#beta is param for armijo rule\n",
    "beta = .9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 7.,  9.,  7., 11., 11., 16., 12.,  9.,  8., 10.]),\n",
       " array([0.00088037, 0.05063574, 0.1003911 , 0.15014646, 0.19990182,\n",
       "        0.24965718, 0.29941254, 0.34916791, 0.39892327, 0.44867863,\n",
       "        0.49843399]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11162a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "params= {\n",
    "    'kappa': [kappa],\n",
    "    'lambda': [lam],\n",
    "    'gains': [g],\n",
    "    'copy_wt':[nu],\n",
    "    'rates':[1/gamma],\n",
    "    'population':[n],\n",
    "    'beta':[beta],\n",
    "    'phi': [phi],\n",
    "    'invariant': [V0],\n",
    "    'dust' : [dust]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_conditions = {'holdings': h,\n",
    "                      'tokens': s0,\n",
    "                      'supply': S0,\n",
    "                      'prices': phat0,\n",
    "                      'funds':F0,\n",
    "                      'reserve': R0,\n",
    "                      'spot_price': P0,\n",
    "                      'actions': {}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'actions': {},\n",
       " 'funds': 50000.0,\n",
       " 'holdings': array([ 398.61197663,  231.18219577, 1401.64449161,  616.5479314 ,\n",
       "        1335.53645361, 1697.32845907, 2422.37637468, 1526.07767456,\n",
       "         913.00714154, 1332.51288457,  809.16939937,  134.73103146,\n",
       "         674.62219586, 2199.73979819, 1161.07506634, 2661.89380992,\n",
       "         911.08058688, 1498.91709776, 1394.53492348,  691.92424253,\n",
       "        3528.45681726,  395.79860808,  653.51874878, 1639.63155415,\n",
       "         245.26997953, 2089.69518473,  237.24377992,  366.57160085,\n",
       "         755.91118235,  423.87060258, 1551.56808269,  873.12851232,\n",
       "         720.80533443, 1703.50154236,  935.83970037,  346.63590712,\n",
       "         428.16515378, 3032.79315568,  957.58599773,  125.29969801,\n",
       "        3179.74837237,  212.59877107, 1132.84275709,  233.67198425,\n",
       "        4440.17772769,  317.84012137,  814.16304942,  440.57701206,\n",
       "        1045.12713555, 1188.65700723,  139.48358513,  328.23290388,\n",
       "         215.43098791,  723.15285166, 1025.63102625, 2009.37550557,\n",
       "        1043.93929368,  842.61998959,  438.0117831 , 2495.01977511,\n",
       "         998.93787968,  405.83182938,  366.17793112,  941.09016651,\n",
       "        1595.03290347, 2918.04934039, 1298.20944976,  615.08845128,\n",
       "        1950.50551443,  974.70329105,  405.64837225, 2043.60310757,\n",
       "        1195.61055613,  458.19232171, 5004.56776363,  369.77705114,\n",
       "         613.52824949,  804.19194827,  809.71150006,  373.34640362,\n",
       "        3130.344098  ,  267.928461  ,  829.19463272,  265.98566614,\n",
       "         161.24391092, 1774.55348115,  733.28640721, 2273.27067515,\n",
       "         175.90525805,  779.61383345,  420.77420356, 3913.96022491,\n",
       "         690.11640211,  868.71931929, 1417.20757513, 1871.12668457,\n",
       "         848.23679743,  969.95726233,  352.41657713, 1114.83576255]),\n",
       " 'prices': array([0.11661061, 0.10906934, 0.11510875, 0.10027558, 0.13537256,\n",
       "        0.08194483, 0.08394621, 0.09919154, 0.14795103, 0.07880923,\n",
       "        0.12742079, 0.14724994, 0.09615324, 0.11140925, 0.08988076,\n",
       "        0.11791097, 0.11213419, 0.07618234, 0.14665482, 0.09113925,\n",
       "        0.08468717, 0.08911114, 0.10803306, 0.13725293, 0.09592434,\n",
       "        0.06863414, 0.11683042, 0.08779241, 0.11488902, 0.12127691,\n",
       "        0.10905669, 0.04141587, 0.08678493, 0.13609513, 0.0626545 ,\n",
       "        0.09449888, 0.09702468, 0.08505998, 0.12409395, 0.10471441,\n",
       "        0.04414669, 0.08956629, 0.13537348, 0.10579813, 0.13140058,\n",
       "        0.04457017, 0.092457  , 0.09145693, 0.09208763, 0.12135753,\n",
       "        0.07750867, 0.10663998, 0.09840408, 0.07132769, 0.04786978,\n",
       "        0.11727576, 0.07041477, 0.08476159, 0.07684531, 0.08063796,\n",
       "        0.08083728, 0.12942807, 0.08863022, 0.12371414, 0.09632942,\n",
       "        0.08547849, 0.06481265, 0.10250517, 0.1321794 , 0.04731582,\n",
       "        0.09754577, 0.10834836, 0.10559867, 0.1039306 , 0.07167456,\n",
       "        0.11638981, 0.10930431, 0.11295949, 0.14198631, 0.08707837,\n",
       "        0.13408771, 0.08882886, 0.08271731, 0.10994807, 0.11769068,\n",
       "        0.13768987, 0.06647998, 0.08095149, 0.09541151, 0.11824457,\n",
       "        0.17955576, 0.07371649, 0.06612597, 0.07590202, 0.05514611,\n",
       "        0.11705652, 0.09532037, 0.13561785, 0.09758172, 0.10647569]),\n",
       " 'reserve': 50000.0,\n",
       " 'spot_price': 0.09999999999999999,\n",
       " 'supply': 1000000.0,\n",
       " 'tokens': array([ 8007.5170622 , 14120.93208144, 10297.96519215,  6902.78179862,\n",
       "        28343.43216214,  9523.19688126,  9174.45980131, 11615.58487957,\n",
       "        16407.95387049,  7578.02255575,  5259.28774055,  6871.22551034,\n",
       "         7829.71916046, 10282.74574426,  7217.1131258 ,  5512.6584568 ,\n",
       "         5016.11377331,  8679.05505914,  8996.99380592,  6906.2552171 ,\n",
       "         6306.6856375 ,  6592.4965753 , 10456.53911541, 10291.93411536,\n",
       "        11932.1014925 ,  8056.74998998,  9342.76448449,  8332.58888149,\n",
       "         5250.62236663, 12785.47257271, 10934.97053071,  9880.17674092,\n",
       "        13955.25846709,  9716.15708489,  8745.79571434, 11473.8392733 ,\n",
       "         5694.96646944,  4940.02596331,  5727.00690165,  5336.34986942,\n",
       "        26957.52569813, 13900.23296304,  6640.40742005, 13960.89204666,\n",
       "        10178.04687613,  6844.47803344,  4814.19634332,  9451.56648474,\n",
       "        19630.12309993, 10621.90751919,  5682.25960753,  8570.91178429,\n",
       "         5210.91521027,  7384.61503036, 18224.76732838, 13787.18190625,\n",
       "         8108.77581   , 22644.0129079 ,  7066.2442507 ,  8351.94053405,\n",
       "         5685.70624438,  8648.90833425, 11121.35443265,  9007.90808893,\n",
       "        20078.8528775 ,  6563.28580857, 10385.65133554,  5301.91487207,\n",
       "         8255.49204674,  5593.82862291, 16676.73027697, 11757.7521116 ,\n",
       "         7977.67921608, 11282.35336347, 13726.19787329, 13421.85330905,\n",
       "         6241.76296067,  5661.97935377,  5150.4414155 , 15230.43398791,\n",
       "         6570.76936524,  5498.12440512,  5865.29054658,  5060.94097463,\n",
       "        12928.81141751, 21121.92136994, 10902.51426911, 13768.3902016 ,\n",
       "         9654.15329802, 10782.87480206, 10694.68005407,  4919.17560968,\n",
       "        13039.08121198, 15258.53871462,  8852.25875184, 12433.45032435,\n",
       "         5503.96020864, 11627.20117694,  6522.93231045, 12898.32747642])}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change in F (revenue and spending accounted for)\n",
    "def revenue_process(params, step, sL, s):\n",
    "    lam = params['lambda']\n",
    "    rv = sts.expon.rvs(loc = 0, scale=1/lam)\n",
    "    delF= 1-1/lam+rv\n",
    "    \n",
    "    #avoid the crash (temporary hacks, tune martingale process better)\n",
    "    #if delF <1:\n",
    "    #    if s['funds'] <1000:\n",
    "    #        delF =100\n",
    "    \n",
    "    return({'delF':delF})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_funds(params, step, sL, s, _input):\n",
    "    \n",
    "    funds = s['funds']*_input['delF']\n",
    "    \n",
    "    key = 'funds'\n",
    "    value = funds\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_prices(params, step, sL, s, _input):\n",
    "    \n",
    "    #can also add a term for extrapolating the trend\n",
    "    g = params['gains']\n",
    "    nu = params['copy_wt']\n",
    "    phat = g*s['funds']/s['supply']*(1-nu)+ nu*s['spot_price']\n",
    "    \n",
    "    key = 'prices'\n",
    "    value = phat\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "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",
    "    rates = params['rates']\n",
    "    \n",
    "    agents = []\n",
    "    for a in range(n):\n",
    "        sq_gap = (s['spot_price']-s['prices'][a])**2\n",
    "        pr = (rates[a]+sq_gap)/(1+sq_gap) #rates when sq_gap =0, 1 when sq_gap -> infty\n",
    "        rv = np.random.rand()\n",
    "        if rv < pr:\n",
    "            agents.append(a)\n",
    "    \n",
    "    #shuffle\n",
    "    shuffled_agents =np.random.choice(agents,len(agents), False)     \n",
    "        \n",
    "    return({'agents':shuffled_agents})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agent_actions(params, step, sL, s, _input):\n",
    "    \n",
    "    R = s['reserve']\n",
    "    S = s['supply']\n",
    "    F = s['funds']\n",
    "    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 = 1000\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, 1000), 'M': {'kappa': 2, 'lambda': 200, 'gains': array([2.3322122 , 2.18138684, 2.30217504, 2.00551155, 2.70745129,\n",
      "       1.63889656, 1.67892422, 1.98383077, 2.95902062, 1.5761846 ,\n",
      "       2.5484157 , 2.94499875, 1.92306483, 2.22818492, 1.79761528,\n",
      "       2.3582194 , 2.24268385, 1.52364676, 2.93309642, 1.82278493,\n",
      "       1.69374342, 1.78222283, 2.1606613 , 2.74505866, 1.9184868 ,\n",
      "       1.37268277, 2.33660842, 1.75584823, 2.29778034, 2.42553826,\n",
      "       2.18113383, 0.82831746, 1.73569867, 2.72190266, 1.25309   ,\n",
      "       1.8899775 , 1.94049359, 1.70119962, 2.48187904, 2.09428825,\n",
      "       0.88293374, 1.79132572, 2.70746969, 2.11596264, 2.62801156,\n",
      "       0.8914034 , 1.84913997, 1.82913855, 1.84175269, 2.42715056,\n",
      "       1.55017345, 2.13279951, 1.96808158, 1.42655388, 0.95739562,\n",
      "       2.34551517, 1.4082954 , 1.6952318 , 1.53690627, 1.61275922,\n",
      "       1.61674564, 2.58856131, 1.77260435, 2.47428282, 1.92658832,\n",
      "       1.70956976, 1.296253  , 2.05010348, 2.64358796, 0.94631633,\n",
      "       1.95091534, 2.16696715, 2.11197341, 2.07861196, 1.43349124,\n",
      "       2.32779622, 2.18608621, 2.25918972, 2.83972617, 1.74156735,\n",
      "       2.68175412, 1.77657727, 1.65434623, 2.19896142, 2.35381363,\n",
      "       2.75379743, 1.3295996 , 1.61902976, 1.90823014, 2.36489139,\n",
      "       3.59111514, 1.47432973, 1.32251937, 1.51804041, 1.10292221,\n",
      "       2.34113036, 1.90640734, 2.71235691, 1.95163442, 2.12951382]), 'copy_wt': array([0.49843399, 0.26224474, 0.00088037, 0.30289089, 0.29819948,\n",
      "       0.48556453, 0.48768448, 0.21800217, 0.40060927, 0.43278885,\n",
      "       0.18370691, 0.11676093, 0.01105824, 0.2630553 , 0.36646532,\n",
      "       0.38290957, 0.28062969, 0.26590739, 0.23831607, 0.23091362,\n",
      "       0.13170993, 0.17110902, 0.23593298, 0.48119768, 0.2981067 ,\n",
      "       0.33808547, 0.27445962, 0.20166521, 0.0499079 , 0.47379984,\n",
      "       0.33112604, 0.04125486, 0.27561246, 0.17627766, 0.20236594,\n",
      "       0.24819507, 0.29656115, 0.44122361, 0.08311865, 0.33994819,\n",
      "       0.30554653, 0.34196045, 0.13338465, 0.40140726, 0.24110982,\n",
      "       0.32762137, 0.40015356, 0.29461057, 0.3240859 , 0.26804887,\n",
      "       0.36924718, 0.11165949, 0.12340518, 0.08722423, 0.4304584 ,\n",
      "       0.03095517, 0.21610917, 0.39002044, 0.4838372 , 0.06352616,\n",
      "       0.3957231 , 0.36263814, 0.08852832, 0.21131231, 0.11689228,\n",
      "       0.09037808, 0.18583078, 0.23000399, 0.37259172, 0.05131294,\n",
      "       0.05217709, 0.43409673, 0.19819331, 0.33810312, 0.31634577,\n",
      "       0.37783519, 0.15822216, 0.18111193, 0.44872321, 0.30866313,\n",
      "       0.25002543, 0.37203711, 0.27438602, 0.49393453, 0.25102798,\n",
      "       0.28283816, 0.27453181, 0.33020247, 0.04023769, 0.18490005,\n",
      "       0.08356144, 0.11730729, 0.47202018, 0.02339729, 0.44294748,\n",
      "       0.45729014, 0.19595544, 0.19072042, 0.19552655, 0.08865018]), 'rates': array([0.60606654, 0.20779539, 0.16242542, 0.45892603, 0.08969907,\n",
      "       0.10764185, 0.36102327, 0.12506067, 0.09216946, 0.5725304 ,\n",
      "       0.08630417, 0.34466757, 0.18431839, 0.18716614, 0.57103622,\n",
      "       0.08585204, 0.56883838, 0.4442011 , 0.14840202, 0.22742075,\n",
      "       0.51151743, 0.08370301, 0.46647862, 0.19283355, 0.48426519,\n",
      "       0.19412967, 0.39318703, 0.18846155, 0.39272717, 0.28867938,\n",
      "       0.48857784, 0.21573912, 0.13091908, 0.74550955, 0.20571988,\n",
      "       0.11466623, 0.78868655, 0.21391829, 0.12264963, 0.28229214,\n",
      "       0.08567633, 0.48830401, 0.32164526, 0.03378453, 0.12862516,\n",
      "       0.1243246 , 0.28861835, 0.1005855 , 0.26430786, 0.21322571,\n",
      "       0.94299571, 0.65697325, 0.74187604, 0.09711048, 0.30310562,\n",
      "       0.49170908, 0.12988529, 0.14858264, 0.70759663, 0.19151859,\n",
      "       0.04622148, 0.16068147, 0.82296755, 0.19069552, 0.23295957,\n",
      "       0.27293433, 0.09089568, 0.26617132, 0.09217415, 0.1113987 ,\n",
      "       0.52023332, 0.31613728, 0.43506174, 0.76922858, 0.06035221,\n",
      "       0.58163607, 0.31370963, 0.11226628, 0.25368837, 0.38589604,\n",
      "       0.83617021, 0.12320195, 0.30258814, 0.18259804, 0.57124187,\n",
      "       0.30191543, 0.17488426, 0.19228819, 0.64309287, 0.29051354,\n",
      "       0.06941028, 0.18645818, 0.30949814, 0.24705195, 0.14677732,\n",
      "       0.19029289, 0.46002768, 0.46241701, 0.09802429, 0.05279089]), 'population': 100, '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 0x1a132cac88>]\n",
      "Configurations: [<cadCAD.configuration.Configuration object at 0x1a132cac88>]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:37: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  realized_price = deltaR/deltaS\n",
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:56: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  realized_price = quantity_recieved/deltaS\n",
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:47: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  realized_price = deltaR/deltaS\n",
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:56: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  realized_price = quantity_recieved/deltaS\n",
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:47: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  realized_price = deltaR/deltaS\n",
      "/Users/zargham/Documents/GitHub/conviction/bonding_curve_eq.py:37: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  realized_price = deltaR/deltaS\n"
     ]
    }
   ],
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_index = 0\n",
    "df = results[experiment_index]['result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a134939b0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a132cafd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.funds.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24777cf8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a132b5400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df.funds.diff()/df.funds).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf = df[df.substep == 3].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['wealth'] = rdf.holdings + rdf.spot_price*rdf.tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1a2ada20>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18d222b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1bf78898>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b3d62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1cabf3c8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b3d6748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 31,
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1fd4c5f8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fd4cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f7cd0f0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fbfe978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.est_err.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20b725f8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fd4c898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.est_err.apply(np.abs).plot(logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a21f01b38>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a216b7358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a21f010b8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21ec2c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 38,
   "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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdf = pd.DataFrame(states)\n",
    "tdf = pd.DataFrame(transactions).drop('posterior', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">pbar</th>\n",
       "      <th>amt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent</th>\n",
       "      <th>mech</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.105442</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.986120e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101684</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.311822e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100876</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.401644e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100758</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.281013e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.099208</td>\n",
       "      <td>343.0</td>\n",
       "      <td>7.664643e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104732</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.335536e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097145</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.523197e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095980</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.174460e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102171</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.326889e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.101742</td>\n",
       "      <td>114.0</td>\n",
       "      <td>6.024571e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101853</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.130071e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.578023e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099455</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.091694e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104860</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.347310e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">12</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.105210</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.891141e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.105399</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.770688e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100395</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.199740e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.098685</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.217113e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.106560</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.661894e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102994</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.110806e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094676</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.679055e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.109309</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.394535e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094977</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.906255e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.306686e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.098091</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.592497e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099676</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.535187e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104994</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.639632e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">24</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103977</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.066104e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.099516</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.458612e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.099027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.056750e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.099056</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.372620e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099621</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.697771e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102448</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.135282e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099477</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.041919e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.097115e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094842</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.523043e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101541</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.130344e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.498124e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.093433</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.865291e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">83</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100039</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.329928e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.109385</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.074245e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.098757</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.612439e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.107848</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.774553e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095707</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.090251e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.103796</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.376839e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097806</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.654153e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102680</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.796138e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.207742e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096459</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.919176e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.303908e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097947</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.525854e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.852259e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104415</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.871127e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">96</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103146</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.364674e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.103581</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.156298e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.098549</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.699573e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">98</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104456</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.824810e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.100236</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.702680e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">99</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.099711</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.574179e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.101389</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.453960e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                pbar                  amt\n",
       "              median  count        median\n",
       "agent mech                               \n",
       "0     bond  0.105442    1.0  3.986120e+02\n",
       "1     bond  0.101684    1.0  2.311822e+02\n",
       "2     bond  0.100876    1.0  1.401644e+03\n",
       "3     bond  0.100758   31.0  2.281013e-10\n",
       "      burn  0.099208  343.0  7.664643e-10\n",
       "4     bond  0.104732    1.0  1.335536e+03\n",
       "5     burn  0.097145    1.0  9.523197e+03\n",
       "6     burn  0.095980    1.0  9.174460e+03\n",
       "7     bond  0.102171   10.0  7.326889e-10\n",
       "      burn  0.101742  114.0  6.024571e-10\n",
       "8     bond  0.101853    1.0  9.130071e+02\n",
       "9     burn  0.096991    1.0  7.578023e+03\n",
       "10    bond  0.099455    1.0  8.091694e+02\n",
       "11    bond  0.104860    1.0  1.347310e+02\n",
       "12    bond  0.105210    2.0  2.891141e+02\n",
       "      burn  0.105399    7.0  1.770688e-09\n",
       "13    bond  0.100395    1.0  2.199740e+03\n",
       "14    burn  0.098685    1.0  7.217113e+03\n",
       "15    bond  0.106560    1.0  2.661894e+03\n",
       "16    bond  0.102994    1.0  9.110806e+02\n",
       "17    burn  0.094676    1.0  8.679055e+03\n",
       "18    bond  0.109309    1.0  1.394535e+03\n",
       "19    burn  0.094977    1.0  6.906255e+03\n",
       "20    burn  0.094011    1.0  6.306686e+03\n",
       "21    burn  0.098091    1.0  6.592497e+03\n",
       "22    bond  0.099676    1.0  6.535187e+02\n",
       "23    bond  0.104994    1.0  1.639632e+03\n",
       "24    bond  0.103977    4.0  3.066104e+02\n",
       "      burn  0.099516  100.0  1.458612e-09\n",
       "25    burn  0.099027    1.0  8.056750e+03\n",
       "...              ...    ...           ...\n",
       "74    burn  0.099056    1.0  1.372620e+04\n",
       "75    bond  0.099621    1.0  3.697771e+02\n",
       "76    bond  0.102448    1.0  6.135282e+02\n",
       "77    bond  0.099477    1.0  8.041919e+02\n",
       "78    bond  0.103451    1.0  8.097115e+02\n",
       "79    burn  0.094842    1.0  1.523043e+04\n",
       "80    bond  0.101541    1.0  3.130344e+03\n",
       "81    burn  0.094002    1.0  5.498124e+03\n",
       "82    burn  0.093433    1.0  5.865291e+03\n",
       "83    bond  0.100039    2.0  1.329928e+02\n",
       "      burn  0.109385    1.0  1.074245e-09\n",
       "84    bond  0.098757    1.0  1.612439e+02\n",
       "85    bond  0.107848    1.0  1.774553e+03\n",
       "86    burn  0.095707    1.0  1.090251e+04\n",
       "87    burn  0.103796    1.0  1.376839e+04\n",
       "88    burn  0.097806    1.0  9.654153e+03\n",
       "89    bond  0.102680    1.0  7.796138e+02\n",
       "90    bond  0.102529    1.0  4.207742e+02\n",
       "91    burn  0.096459    1.0  4.919176e+03\n",
       "92    burn  0.094030    1.0  1.303908e+04\n",
       "93    burn  0.097947    1.0  1.525854e+04\n",
       "94    burn  0.097189    1.0  8.852259e+03\n",
       "95    bond  0.104415    1.0  1.871127e+03\n",
       "96    bond  0.103146    1.0  7.364674e+02\n",
       "      burn  0.103581   25.0  3.156298e-09\n",
       "97    bond  0.098549    1.0  9.699573e+02\n",
       "98    bond  0.104456    3.0  1.824810e-09\n",
       "      burn  0.100236   75.0  1.702680e-09\n",
       "99    bond  0.099711    2.0  5.574179e+02\n",
       "      burn  0.101389    1.0  5.453960e-10\n",
       "\n",
       "[118 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24a2f3c8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20c23ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tx_summary.pbar['median'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24cc2240>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a24dae0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf['P'].plot(logx=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f18e940>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a222019b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf['P'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22201ac8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a24770fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf.F.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18292e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bond_amts, bins=20)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18719f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(burn_amts, bins=20)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['invariant'] = rdf.supply.apply(lambda x: x**kappa)/rdf.reserve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a19f3c908>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a199a36d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 52,
   "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": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([53.,  9., 16.,  7.,  5.,  6.,  2.,  0.,  1.,  1.]),\n",
       " array([   0.        ,  636.42292584, 1272.84585167, 1909.26877751,\n",
       "        2545.69170334, 3182.11462918, 3818.53755502, 4454.96048085,\n",
       "        5091.38340669, 5727.80633252, 6364.22925836]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tk7LvtPVeCsy2/ecfGVylMjH5F3v4TVFJ6sQknHKRJI3AQpekTljoktQJC12SOmGhS1InLHRJ6oSFLkmdsNAlqRP/D2+WL5x9qYv1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19827710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-1].holdings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['gini_h'] = rdf.holdings.apply(gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a1c5160>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K+rmkh5LhoFNIWg60AU+OtthqHRvIDN13T9/M7BXVjOmXOt+xeC65FmAZcDWwEHhA0sX5YRxJ84GvAx+KiNMO70taDawGWLx4cdXFl3Oi/5XQn+qevpnZkGp6+r3AooLlhcDeEtt8LyIGIuIpYDu5NwEkzQB+APxpRDxU6h+IiHUR0RURXZ2dYx/9OTmYZemcqTx4yzWeG9fMrEA1ob8ZWCZpqaQ24EZgfdE29wJvA5A0h9xwz+5k++8Cd0bEt2tXdmUDmSztLU3MnznlTP2TZmaTwrChHxGDwBpgI/AE8K2I2CbpNkkrk802Agck9QD3A5+KiAPADcBVwCpJjya3S8elJQU8r6aZWWlVnacfERuADUXrbi24H8Ank1vhNt8AvjH2MkdmIJP1vJpmZiWkMhkHMlnPq2lmVkIqQ38wE55tx8yshFQm40A2PJmymVkJqUzGwUyWVl9O2czsNKkM/YFM1mfvmJmVkMrQ95i+mVlpqUzGgWzWoW9mVkIqk3EwE7R4TN/M7DSpDP2BjM/eMTMrJZXJOJDJ0uYDuWZmp0lt6Lunb2Z2utQl46Fj/Rzrz3DujI6JLsXMrO6kLvSfPnAMgPPPOWuCKzEzqz+pC/3DxwcAOHuqJ08xMyuWutDvH8zNxtjWkrqmmZmNWeqScSCTC31/OcvM7HSpS8b+jHv6ZmblpC4ZT+aHd9zTNzM7TVXJKGmFpO2Sdkm6ucw2N0jqkbRN0jcL1n9I0s7k9qFaFV7OgHv6ZmZlDTtHrqRmYC1wLdALbJa0PiJ6CrZZBtwCXBkRByXNTdafDfwZ0AUEsCXZ92Dtm5LT756+mVlZ1STjcmBXROyOiH7gLuD6om0+CqzNh3lE7E/WXwdsiogXk8c2AStqU3ppQwdy3dM3MztNNcm4ANhTsNybrCt0IXChpJ9LekjSihHsW1Pu6ZuZlTfs8A5Q6splUeJ5lgFXAwuBByRdXOW+SFoNrAZYvHhxFSWVlw/9Vl9wzczsNNV0h3uBRQXLC4G9Jbb5XkQMRMRTwHZybwLV7EtErIuIrojo6uzsHEn9p+nPBG3NTUgOfTOzYtWE/mZgmaSlktqAG4H1RdvcC7wNQNIccsM9u4GNwNslzZY0G3h7sm7c9A9mfeaOmVkZww7vRMSgpDXkwroZuCMitkm6DeiOiPW8Eu49QAb4VEQcAJD0OXJvHAC3RcSL49GQvP5MxqFvZlZGNWP6RMQGYEPRulsL7gfwyeRWvO8dwB1jK7N6A4Ph8XwzszJS1yXuz3h4x8ysnNSlY38m64utmZmVkbp07B/M+hx9M7MyUpeO/YNZ2j28Y2ZWUurSccDDO2ZmZaUuHX2evplZealLxwGfvWNmVlbq0vHkoId3zMzKSV06+jx9M7PyUpeOA5ksrU3+Rq6ZWSmpC/1sFpqbUtcsM7OaSF06ZrKBh/TNzEpLXTxmImj28I6ZWUmpC/1sNmjyBCpmZiWlL/TDoW9mVk7qQj83pu/QNzMrJXWhnw3c0zczKyN1oe+zd8zMyktdPGYiaPLwjplZSVWFvqQVkrZL2iXp5hKPr5LUJ+nR5PaRgse+IGmbpCck/a00vmMv2WzQ7OEdM7OShp0YXVIzsBa4FugFNktaHxE9RZveHRFrivZ9M3AlcEmy6l+BtwI/GWPdZfk8fTOz8qrp6S8HdkXE7ojoB+4Crq/y+QPoANqAdqAVeH40hVb1j0UQAeP8YcLMbNKqJvQXAHsKlnuTdcXeI+kxSfdIWgQQEQ8C9wP7ktvGiHiieEdJqyV1S+ru6+sbcSPyspH76eEdM7PSqgn9UgkaRcvfB5ZExCXAj4CvAUh6NfBrwEJybxTXSLrqtCeLWBcRXRHR1dnZOZL6T5FJUt9n75iZlVZNPPYCiwqWFwJ7CzeIiAMRcTJZvB24Irn/buChiDgaEUeBfwbeOLaSy8tGLvR99o6ZWWnVhP5mYJmkpZLagBuB9YUbSJpfsLgSyA/h/Ap4q6QWSa3kDuKeNrxTK0M9fQ/vmJmVNOzZOxExKGkNsBFoBu6IiG2SbgO6I2I98HFJK4FB4EVgVbL7PcA1wOPkhoR+GBHfr30zcjKRH95x6JuZlTJs6ANExAZgQ9G6Wwvu3wLcUmK/DPCHY6yxatmkp+/LMJiZlZaqQ575s3fc0TczKy1Vof/K2TtOfTOzUlIV+j57x8ysslSFvs/eMTOrLJWh756+mVlpqQr9/PCOe/pmZqWlKvR3v/AyAE2papWZWe2kKh4/cdejAOw9dGKCKzEzq0+pCv3O6e0ADGaKrwdnZmaQstB/58XnAvCHb71ggisxM6tPqQr9/KxZHa3NE12KmVldSlfoZ33mjplZJakK/WyEz9wxM6sgVRGZyYZ7+mZmFaQv9P1tXDOzshz6ZmYNJF2hHw59M7NKUhX62Wx41iwzswqqCn1JKyRtl7RL0s0lHl8lqU/So8ntIwWPLZZ0n6QnJPVIWlK78k/l4R0zs8qGnSNXUjOwFrgW6AU2S1ofET1Fm94dEWtKPMWdwF9ExCZJ04DsWIsuJxPu6ZuZVVJNT385sCsidkdEP3AXcH01Ty7pIqAlIjYBRMTRiDg26mqHkc0GLc0OfTOzcqoJ/QXAnoLl3mRdsfdIekzSPZIWJesuBA5J+o6kRyT9VfLJYVwM+jx9M7OKqgn9UilafBnL7wNLIuIS4EfA15L1LcBbgJuA3wAuAFad9g9IqyV1S+ru6+ursvTT5b6R69A3MyunmtDvBRYVLC8E9hZuEBEHIuJksng7cEXBvo8kQ0ODwL3A5cX/QESsi4iuiOjq7OwcaRuG+Bu5ZmaVVRP6m4FlkpZKagNuBNYXbiBpfsHiSuCJgn1nS8on+TVA8QHgmslkPT+umVklw569ExGDktYAG4Fm4I6I2CbpNqA7ItYDH5e0EhgEXiQZwomIjKSbgB9LErCF3CeBcZGNoMWhb2ZW1rChDxARG4ANRetuLbh/C3BLmX03AZeMocaqDWY9pm9mVknqvpHrMzbNzMpLVej7G7lmZpWlK/T9jVwzs4pSFfpZ9/TNzCpKVej70spmZpWlKvSzgYd3zMwqSFXoRwTu6JuZlZeq0M9GIPf0zczKSlXoR+CevplZBakK/Wzgnr6ZWQWpCn2P6ZuZVZaq0M9GoJKX/zczM0hZ6EdAU6paZGZWW6mKSJ+9Y2ZWWapCP/zlLDOzilIV+rkxfTMzKydVoR/4PH0zs0pSFfpZX1rZzKyiqkJf0gpJ2yXtknRzicdXSeqT9Ghy+0jR4zMkPSvp72pVeCnZrL+cZWZWybBz5EpqBtYC1wK9wGZJ6yOip2jTuyNiTZmn+Rzw0zFVWoWIwJlvZlZeNT395cCuiNgdEf3AXcD11f4Dkq4A5gH3ja7E6nlM38yssmpCfwGwp2C5N1lX7D2SHpN0j6RFAJKagC8CnxpzpVXwmL6ZWWXVhH6pFI2i5e8DSyLiEuBHwNeS9R8DNkTEHiqQtFpSt6Tuvr6+KkoqzRdcMzOrbNgxfXI9+0UFywuBvYUbRMSBgsXbgc8n998EvEXSx4BpQJukoxFxc9H+64B1AF1dXcVvKFXzmL6ZWWXVhP5mYJmkpcCzwI3A+ws3kDQ/IvYliyuBJwAi4vcKtlkFdBUHfi35evpmZpUNG/oRMShpDbARaAbuiIhtkm4DuiNiPfBxSSuBQeBFYNU41lyWx/TNzCqrpqdPRGwANhStu7Xg/i3ALcM8x1eBr464whHwxOhmZpWl7hu5ZmZWXqpC31fZNDOrLGWh7+kSzcwqSVXoZwOanPpmZmWlLPR9PX0zs0pSFfrhb+SamVWUrtDHY/pmZpWkKvR9nr6ZWWUpC3339M3MKklV6EeAr7hmZlZeakI/km/juqdvZlZeakI/m1yBwWP6ZmblpSj03dM3MxtO6kLf5+mbmZWXmtDPX2DTmW9mVl7qQt9j+mZm5aUm9D2mb2Y2vNSFvnzJNTOzsqoKfUkrJG2XtEvSaRObS1olqU/So8ntI8n6SyU9KGmbpMck/W6tG5CXnzPLoztmZuUNO0eupGZgLXAt0AtslrQ+InqKNr07ItYUrTsGfDAidko6D9giaWNEHKpF8YUim/vpMX0zs/Kq6ekvB3ZFxO6I6AfuAq6v5skjYkdE7Ezu7wX2A52jLbYSj+mbmQ2vmtBfAOwpWO5N1hV7TzKEc4+kRcUPSloOtAFPjqrSYfg8fTOz4VUT+qVSNIqWvw8siYhLgB8BXzvlCaT5wNeBD0fkB2JOeXy1pG5J3X19fdVVXqS1pYnf+fX5nH/OWaPa38ysEVQT+r1AYc99IbC3cIOIOBARJ5PF24Er8o9JmgH8APjTiHio1D8QEesioisiujo7Rzf6M6OjlbW/dzlXv2buqPY3M2sE1YT+ZmCZpKWS2oAbgfWFGyQ9+byVwBPJ+jbgu8CdEfHt2pRsZmajNezZOxExKGkNsBFoBu6IiG2SbgO6I2I98HFJK4FB4EVgVbL7DcBVwDmS8utWRcSjtW2GmZlVQ/nr0NeLrq6u6O7unugyzMwmFUlbIqJruO1S841cMzMbnkPfzKyBOPTNzBqIQ9/MrIE49M3MGkjdnb0jqQ94ZpS7zwFeqGE5E8ltqU9paUta2gFuS975ETHst1vrLvTHQlJ3NacsTQZuS31KS1vS0g5wW0bKwztmZg3EoW9m1kDSFvrrJrqAGnJb6lNa2pKWdoDbMiKpGtM3M7PK0tbTNzOzClIT+sNN3l6PJD0t6fFkMvnuZN3ZkjZJ2pn8nJ2sl6S/Tdr3mKTLJ7DuOyTtl7S1YN2I65b0oWT7nZI+VEdt+YykZ5PX5VFJ7yx47JakLdslXVewfsL//iQtknS/pCckbZP035P1k+q1qdCOSfe6SOqQ9O+Sfpm05bPJ+qWSHk5+v3cnl6FHUnuyvCt5fMlwbRyxiJj0N3KXfH4SuIDclIy/BC6a6LqqqPtpYE7Rui8ANyf3bwY+n9x/J/DP5GYyeyPw8ATWfRVwObB1tHUDZwO7k5+zk/uz66QtnwFuKrHtRcnfVjuwNPmba66Xvz9gPnB5cn86sCOpeVK9NhXaMelel+R3Oy253wo8nPyuvwXcmKz/MvBHyf2PAV9O7t8I3F2pjaOpKS09/VFP3l6HrueV6Sa/BryrYP2dkfMQMEunTl5zxkTEz8jNm1BopHVfB2yKiBcj4iCwCVgx/tWfqkxbyrkeuCsiTkbEU8Aucn97dfH3FxH7IuIXyf0j5CYzWsAke20qtKOcun1dkt/t0WSxNbkFcA1wT7K++DXJv1b3AL8lSZRv44ilJfSrnby93gRwn6QtklYn6+ZFxD7I/fED+fkf672NI6273tuzJhnyuCM/HMIkaksyLHAZuZ7lpH1titoBk/B1kdQs6VFgP7k30CeBQxExWKKuoZqTx18CzqGGbUlL6FczeXs9ujIiLgfeAfw3SVdV2HaytrFc3fXcnr8HXgVcCuwDvpisnxRtkTQN+CfgExFxuNKmJdbVTXtKtGNSvi4RkYmIS8nNL74c+LVSmyU/x70taQn9YSdvr0cRsTf5uZ/cXMLLgefzwzbJz/3J5vXexpHWXbftiYjnk/+oWeB2XvkYXfdtkdRKLij/MSK+k6yedK9NqXZM5tcFICIOAT8hN6Y/S1J+utrCuoZqTh6fSW74sWZtSUvoDzt5e72RNFXS9Px94O3AVnJ158+W+BDwveT+euCDyRkXbwReyn9krxMjrXsj8HZJs5OP6W9P1k24omMl7yb3ukCuLTcmZ1gsBZYB/06d/P0lY7//B3giIr5U8NCkem3KtWMyvi6SOiXNSu5PAX6b3DGK+4H3JpsVvyb51+q9wL9E7khuuTaO3Jk8kj2eN3JnIuwgN1726Ymup4p6LyB3NP6XwLZ8zeTG734M7Ex+nh2vnAWwNmnf40DXBNb+f8l9vB4g1wP5L6OpG/gDcgekdgEfrqO2fD2p9bHkP9v8gu0/nbRlO/COevr7A36T3Ef+x4BHk9s7J9trU6Edk+51AS4BHklq3grcmqy/gFxo7wK+DbQn6zuS5V3J4xcM18aR3vyNXDOzBpKW4R0zM6uCQ9/MrIE49M3MGohD38ysgTj0zcwaiEPfzKyBOPTNzBqIQ9/MrIH8fwoLuyqMZyhpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a199a3320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini_h.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([45.,  9., 16., 11.,  8.,  6.,  3.,  1.,  0.,  1.]),\n",
       " array([    0.        ,  5369.33776706, 10738.67553412, 16108.01330117,\n",
       "        21477.35106823, 26846.68883529, 32216.02660235, 37585.36436941,\n",
       "        42954.70213647, 48324.03990352, 53693.37767058]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a1f3cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-1].tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['gini_s'] = rdf.tokens.apply(gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b0d1198>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mmZmlyVboR7hP38wsRaZCv9fdO2ZmqTIV+hGegsHMLE22Qp/wmb6ZWYpshX74Qq6ZWZqMhb6nYTAzS5Ot0Mdn+mZmabIV+h69Y2aWKluhj8fpm5mlyVTo9/pCrplZqkyFfu7GWU59M7NSMhX6EL5zlplZirJCX9JCSesldUi6s8jrt0jqkrQm+bo177WbJW1Ivm6uZPGFenvdvWNmlqZhoA0k1QPLgWuATmC1pLaIWFew6cMRsbRg38nAnwKt5EZUPp/su7si1RfIfSLXqW9mVko5Z/rzgY6I2BQRR4CHgEVlvv91wOMRsSsJ+seBhSdX6sD8iVwzs3TlhP40YEvecmeyrtCHJb0k6duSZgxy34rw7RLNzNKVE/rFUjQKlr8HzIqIS4AngAcHsS+Slkhql9Te1dVVRknF9cYJb21mZnnKCf1OYEbe8nRga/4GEbEzIg4ni18D3lvuvsn+D0REa0S0Njc3l1v7idy9Y2aWqpzQXw20SJotqQlYDLTlbyBpat7ijcAryfPHgGslTZI0Cbg2WTcsPPeOmVm6AUfvRES3pKXkwroeWBERayXdDbRHRBtwh6QbgW5gF3BLsu8uSfeQ+8UBcHdE7BqGdvTV6j59M7MUA4Y+QESsBFYWrLsr7/kyYFmJfVcAK4ZQY9l8u0Qzs3SZ+kRurnvHsW9mVkq2Qj/CffpmZikyFvru3jEzS5Ot0Me3SzQzS5Ot0PeZvplZqsyFvodsmpmVlqnQ7/WFXDOzVJkKfc+8Y2aWLluhHx6nb2aWJlOh79slmpmly1To93qWTTOzVJkK/QjfLtHMLE22Qh+f6ZuZpclW6PtCrplZqkyFfm+EO3fMzFJkKvTB3TtmZmkyFfqehsHMLF2mQt/dO2Zm6TIV+uFx+mZmqbIV+nicvplZmmyFvs/0zcxSOfTNzEaRbIW+u3fMzFJlK/R9pm9mlipboY/H6ZuZpclU6Pt2iWZm6TIV+uH7JZqZpcpW6OPuHTOzNGWFvqSFktZL6pB0Z8p2N0kKSa3J8ixJByWtSb7ur1ThxYS7d8zMUjUMtIGkemA5cA3QCayW1BYR6wq2Gw/cATxb8BYbI+LSCtWbKgIP2DQzS1HOmf58oCMiNkXEEeAhYFGR7e4BvgAcqmB9gxKEb6JiZpainNCfBmzJW+5M1vWTNBeYERHfL7L/bEkvSPonSe8v9g0kLZHULqm9q6ur3NpPkJta+aR3NzPLvHJCv1iM9o+TkVQHfAn4gyLbbQNmRsRc4PeBb0qacMKbRTwQEa0R0drc3Fxe5UX0RqlyzcwMygv9TmBG3vJ0YGve8njgImCVpM3AZUCbpNaIOBwROwEi4nlgI3BBJQovxhdyzczSlRP6q4EWSbMlNQGLgba+FyNib0RMiYhZETELeAa4MSLaJTUnF4KRNAdoATZVvBV5nPlmZqUNOHonIrolLQUeA+qBFRGxVtLdQHtEtKXsfiVwt6RuoAf49xGxqxKFF6/V4/TNzNIMGPoAEbESWFmw7q4S2y7Ie/4I8MgQ6hsUT8NgZpYuc5/IdeibmZWWrdAPz6dvZpYmW6GPz/TNzNJkK/QDfyLXzCxFxkI/3LljZpYiW6GPp2EwM0uTqdDPDdl06puZlZKp0PfUymZm6TIX+k59M7PSMhX64GkYzMzSZCr0ez16x8wsVaZCPzdOv9pVmJnVrmyFPp6GwcwsTbZCP6AuUy0yM6usTEWkb5doZpYuU6EPnk/fzCxNpkI/d+esaldhZla7MhX6vZ5P38wsVaZC3/Ppm5mly1boe+4dM7NUGQt9z7JpZpYmY6Hv7h0zszTZCn3whVwzsxTZCv3wOH0zszTZCn08Tt/MLE2mQt+3SzQzS5ep0PeQTTOzdGWFvqSFktZL6pB0Z8p2N0kKSa1565Yl+62XdF0lii7F862ZmaVrGGgDSfXAcuAaoBNYLaktItYVbDceuAN4Nm/dhcBi4D3AucATki6IiJ7KNSFP+HaJZmZpyjnTnw90RMSmiDgCPAQsKrLdPcAXgEN56xYBD0XE4Yj4BdCRvN+w8O0SzczSlRP604Atecudybp+kuYCMyLi+4Pdt5I8946ZWbpyQr9YjEb/i1Id8CXgDwa7b957LJHULqm9q6urjJKKiwh375iZpSgn9DuBGXnL04GtecvjgYuAVZI2A5cBbcnF3IH2BSAiHoiI1ohobW5uHlwL8vR69I6ZWapyQn810CJptqQmchdm2/pejIi9ETElImZFxCzgGeDGiGhPtlssaYyk2UAL8FzFW5HPZ/pmZiUNOHonIrolLQUeA+qBFRGxVtLdQHtEtKXsu1bS3wPrgG7g9uEauROR6zVy5JuZlTZg6ANExEpgZcG6u0psu6Bg+XPA506yvrIlme8+fTOzFJn5RG5v35m+M9/MrKTMhH7fkCBnvplZadkJ/ST1faZvZlZadkKfvu4dp76ZWSnZCX2f6ZuZDSh7oe9efTOzkrIT+nj0jpnZQDIT+r394/SrW4eZWS3LTOgf+0SuU9/MrJTshH7y6O4dM7PSshP6/aN3nPpmZqVkKPQ94ZqZ2UAyFPq5R5/om5mVlp3QTx6d+WZmpWUn9JNT/TqP2TQzKykzod/YUMcNF5/DzMmnV7sUM7OaVdZNVEaCCWMb+erH31vtMszMalpmzvTNzGxgDn0zs1HEoW9mNoo49M3MRhGHvpnZKOLQNzMbRRz6ZmajiEPfzGwUUd/0BbVCUhfwy5PcfQrwZgXLqSa3pTZlpS1ZaQe4LX1+LSKaB9qo5kJ/KCS1R0RrteuoBLelNmWlLVlpB7gtg+XuHTOzUcShb2Y2imQt9B+odgEV5LbUpqy0JSvtALdlUDLVp29mZumydqZvZmYpMhP6khZKWi+pQ9Kd1a6nHJI2S/q5pDWS2pN1kyU9LmlD8jgpWS9JX07a95KkeVWse4WkHZJezls36Lol3Zxsv0HSzTXUls9K+lVyXNZIuiHvtWVJW9ZLui5vfdV//iTNkPSkpFckrZX06WT9iDo2Ke0YccdF0lhJz0l6MWnLf0vWz5b0bPLv+7CkpmT9mGS5I3l91kBtHLSIGPFfQD2wEZgDNAEvAhdWu64y6t4MTClY9wXgzuT5ncCfJc9vAH5A7jbAlwHPVrHuK4F5wMsnWzcwGdiUPE5Knk+qkbZ8FvjPRba9MPnZGgPMTn7m6mvl5w+YCsxLno8HXktqHlHHJqUdI+64JP+245LnjcCzyb/13wOLk/X3A59Knv8H4P7k+WLg4bQ2nkxNWTnTnw90RMSmiDgCPAQsqnJNJ2sR8GDy/EHgQ3nr/y5yngHOlDS1GgVGxP8FdhWsHmzd1wGPR8SuiNgNPA4sHP7qj1eiLaUsAh6KiMMR8Qugg9zPXk38/EXEtoj4WfJ8P/AKMI0RdmxS2lFKzR6X5N/2QLLYmHwF8C+AbyfrC49J37H6NnC1JFG6jYOWldCfBmzJW+4k/YekVgTwQ0nPS1qSrDs7IrZB7ocfOCtZX+ttHGzdtd6epUmXx4q+7hBGUFuSboG55M4sR+yxKWgHjMDjIqle0hpgB7lfoBuBPRHRXaSu/pqT1/cC76CCbclK6KvIupEwLOnyiJgHXA/cLunKlG1HahtL1V3L7bkPOA+4FNgG/I9k/Yhoi6RxwCPAZyJiX9qmRdbVTHuKtGNEHpeI6ImIS4Hp5M7O311ss+Rx2NuSldDvBGbkLU8HtlaplrJFxNbkcQfwf8j9QGzv67ZJHnckm9d6Gwdbd822JyK2J/9Re4GvcezP6Jpvi6RGckH5jYj4h2T1iDs2xdoxko8LQETsAVaR69M/U1JDkbr6a05en0iu+7FibclK6K8GWpIr4k3kLoC0VbmmVJLOkDS+7zlwLfAyubr7RkvcDHw3ed4G/JtkxMVlwN6+P9lrxGDrfgy4VtKk5M/0a5N1VVdwreS3yR0XyLVlcTLCYjbQAjxHjfz8JX2/fwO8EhF/nvfSiDo2pdoxEo+LpGZJZybPTwM+SO4axZPATclmhcek71jdBPw4cldyS7Vx8E7llezh/CI3EuE1cv1lf1ztesqodw65q/EvAmv7aibXf/cjYEPyODmOjQJYnrTv50BrFWv/Frk/r4+SOwP55MnUDfxbchekOoBP1FBbvp7U+lLyn21q3vZ/nLRlPXB9Lf38AVeQ+5P/JWBN8nXDSDs2Ke0YcccFuAR4Ian5ZeCuZP0ccqHdAfxvYEyyfmyy3JG8PmegNg72y5/INTMbRbLSvWNmZmVw6JuZjSIOfTOzUcShb2Y2ijj0zcxGEYe+mdko4tA3MxtFHPpmZqPI/wdWO5vzQt3mwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b062710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini_s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a253470>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ad01710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.tokens.apply(np.count_nonzero).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['asset_value'] = rdf.holdings + rdf.spot_price*rdf.tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([16., 33., 19., 10., 10.,  6.,  3.,  0.,  2.,  1.]),\n",
       " array([ 677.78411386, 1246.42862831, 1815.07314276, 2383.71765721,\n",
       "        2952.36217166, 3521.00668611, 4089.65120056, 4658.29571501,\n",
       "        5226.94022946, 5795.58474391, 6364.22925836]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HgP3tdl0Hff0y8I3W1wHgT9r4SxiE12PA3wHPa+PPb+uPtcdfsuy13tP6fRS4dtrvw1bTq/npWS4z3VOr/8F2ewR4Txuf9ffgdmCxvQf/nsFZKjPd0/KbV4pKUid6PeQiST9zDHRJ6oSBLkmdMNAlqRMGuiR1wkCXpE4Y6JLUCQNdkjrx/59ha98FeotWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19985ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-1].asset_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['gini'] = rdf.asset_value.apply(gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b6497f0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b924128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf['pref_gap'] = (rdf.prices - rdf.spot_price)/rdf.spot_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 3., 12., 38., 29., 16.,  1.,  1.]),\n",
       "  array([ 3., 12., 37., 30., 16.,  1.,  1.]),\n",
       "  array([ 3., 12., 38., 29., 16.,  1.,  1.]),\n",
       "  array([ 4., 11., 38., 30., 15.,  1.,  1.]),\n",
       "  array([ 3., 12., 38., 29., 16.,  1.,  1.]),\n",
       "  array([ 4., 11., 39., 30., 14.,  1.,  1.]),\n",
       "  array([ 4., 11., 39., 31., 13.,  1.,  1.])],\n",
       " array([-0.55724979, -0.36763523, -0.17802066,  0.01159391,  0.20120847,\n",
       "         0.39082304,  0.5804376 ,  0.77005217]),\n",
       " <a list of 7 Lists of Patches objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b9ccd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rdf.iloc[-7:].pref_gap, bins=7)"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}