conviction/abc_sim.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",
    "#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([45., 26., 12.,  7.,  2.,  5.,  1.,  1.,  0.,  1.]),\n",
       " array([ 1.11648467,  3.72460444,  6.33272421,  8.94084398, 11.54896375,\n",
       "        14.15708351, 16.76520328, 19.37332305, 21.98144282, 24.58956259,\n",
       "        27.19768236]),\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 0x110523630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "params= {\n",
    "    'kappa': [kappa],\n",
    "    'lambda': [lam],\n",
    "    'gains': [g],\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([1407.42378388,  299.70633843,  396.65230579,  196.14249301,\n",
       "         884.93325747, 1231.90821217,  366.91940531, 2976.31457906,\n",
       "        2370.34004907,  373.46902895, 2100.52356084, 1514.72741308,\n",
       "         969.48657293,  244.67933185, 2069.81198675,  807.56359911,\n",
       "        3913.77867022,  477.24540118,  425.2796218 ,  197.14533499,\n",
       "         366.66046245,  287.84548607,  962.32469305,  596.37999786,\n",
       "         263.58238395, 3393.38543714,  104.84359812,  811.56647004,\n",
       "         153.13817995, 2251.17724302,  515.7237366 , 2894.24898273,\n",
       "        1231.46651969, 1415.16971825, 2337.36621312,  146.83680664,\n",
       "        1140.38093187,  314.11308528, 2390.71283642, 2521.96223974,\n",
       "         904.60687324,  413.47723723,  473.24221748,  662.88195157,\n",
       "         694.85996477,  375.8844283 ,  124.05542311, 2110.10906034,\n",
       "         364.15418736, 3764.39369716,  940.74027548, 1194.02810593,\n",
       "         390.38987795,  119.13425397, 2548.86508288,  331.66034262,\n",
       "        2109.82297094, 1004.91160946,  428.37654564,  377.45682514,\n",
       "         363.49769486, 4284.38219556,  441.68622907,  163.47820118,\n",
       "        1499.2921237 , 1552.82666117, 2772.56751105,  434.42150825,\n",
       "        1121.25906649,  117.28911637, 3137.33694657, 1121.04725803,\n",
       "         295.6595818 ,  108.69385695, 2139.26557148,  278.01525657,\n",
       "         685.92939344,  409.39378165,  593.94271528,  217.00778857,\n",
       "         222.36003332, 1093.4763571 ,  324.46480008,  626.97414971,\n",
       "         369.83338952, 1752.48981041, 1105.77306206, 1135.54207895,\n",
       "        1800.05026086,  512.85431344, 2919.9115115 ,  512.78998657,\n",
       "         391.87447132, 1754.68002878, 1665.25200522,  195.85709918,\n",
       "         966.97894868, 1364.03667315, 1353.7002636 ,  158.42650627]),\n",
       " 'prices': array([0.05387796, 0.11223528, 0.12049151, 0.10145109, 0.14028548,\n",
       "        0.12480804, 0.08762101, 0.12464489, 0.07488059, 0.14070916,\n",
       "        0.10853256, 0.08115144, 0.07168449, 0.10086945, 0.09279432,\n",
       "        0.0721556 , 0.11076047, 0.11609498, 0.08904248, 0.11916217,\n",
       "        0.09918539, 0.10792066, 0.07911162, 0.14183998, 0.11178655,\n",
       "        0.09029453, 0.1163655 , 0.0735312 , 0.11353407, 0.06634057,\n",
       "        0.07111213, 0.06732109, 0.06649401, 0.1232114 , 0.09996001,\n",
       "        0.10317969, 0.05162374, 0.11534701, 0.05223777, 0.07885031,\n",
       "        0.12259579, 0.11386657, 0.10372434, 0.07877728, 0.08670117,\n",
       "        0.11673191, 0.10019051, 0.09007765, 0.09045102, 0.1277311 ,\n",
       "        0.0553184 , 0.09954474, 0.13687395, 0.08973569, 0.1072038 ,\n",
       "        0.07932926, 0.06115649, 0.0875754 , 0.07610166, 0.03264762,\n",
       "        0.11608305, 0.11596252, 0.09652204, 0.11815359, 0.08425111,\n",
       "        0.10996859, 0.1088119 , 0.1312609 , 0.09928347, 0.08660766,\n",
       "        0.11508931, 0.11226293, 0.07136445, 0.08788944, 0.10887738,\n",
       "        0.07788502, 0.10752935, 0.07286905, 0.10518769, 0.11367301,\n",
       "        0.13778276, 0.08335656, 0.09902322, 0.08638764, 0.07852511,\n",
       "        0.11997966, 0.10566721, 0.11752229, 0.05031931, 0.07845209,\n",
       "        0.09662076, 0.02018046, 0.07801026, 0.09829112, 0.10640853,\n",
       "        0.1022802 , 0.13589556, 0.11100126, 0.11206131, 0.07116552]),\n",
       " 'reserve': 50000.0,\n",
       " 'spot_price': 0.09999999999999999,\n",
       " 'supply': 1000000.0,\n",
       " 'tokens': array([12130.35520004,  5635.60908331,  8993.56765612,  7017.04692822,\n",
       "        25389.28043999,  8645.47958553,  7292.57670401,  9633.03068719,\n",
       "        13691.88300218, 19499.93347059,  6186.27315322, 12204.63353664,\n",
       "         8434.96306663,  6802.0308037 ,  5724.26040177, 27004.23309929,\n",
       "         6020.9850113 , 11325.67262754,  6542.53828038,  5459.94241344,\n",
       "         8050.8337788 ,  8329.23460621, 13350.85298682,  5842.63236781,\n",
       "         8707.5055996 , 24946.88994969,  6016.35956955,  6668.69399533,\n",
       "         8746.86474821,  5720.24863961,  6823.45092518, 11127.07593007,\n",
       "         5988.58701523, 10079.82059526,  7117.30245016,  5301.5995707 ,\n",
       "         7567.83227837,  9651.39476033, 11548.56928664,  9811.59408697,\n",
       "         9869.70525603,  6896.79167184, 17024.3249427 ,  5773.68682415,\n",
       "        14269.83879508, 20677.52706504,  5629.88336701, 13700.80368728,\n",
       "         8058.50955584, 12669.30032172,  9571.27760501, 12176.59950303,\n",
       "         5525.70172882, 11258.78878513, 12335.27544544,  6705.54280953,\n",
       "         6178.77605368, 13386.0382777 ,  6498.85888303,  8144.32301314,\n",
       "         6931.01299252,  9711.73479848,  9296.71801891,  5643.7992017 ,\n",
       "         7749.56847729,  6386.93969183,  7185.59838831, 10750.86443276,\n",
       "         5850.79184848,  7266.11078774, 15747.24021513, 16596.36173452,\n",
       "         7192.83665285,  6118.04731637,  5172.9006494 , 18388.78906542,\n",
       "        13760.48651529, 17746.89164711,  7945.65369909,  6192.61460785,\n",
       "        10108.74206435,  6931.0938318 , 13771.25513023,  7750.18597225,\n",
       "        15810.39507282, 14374.01555236,  9012.77987868,  7890.16837569,\n",
       "         8412.83042424, 11827.10293058, 15941.98092799, 15883.42203315,\n",
       "         7408.98408806, 10327.80740996, 11138.44328657,  8034.39119121,\n",
       "         9926.35540645,  9336.72266903,  5957.50851228,  7137.66261843])}"
      ]
     },
     "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",
    "    g = params['gains']\n",
    "    phat = g*s['funds']/s['supply']\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([1.07755925, 2.24470557, 2.4098302 , 2.02902186, 2.80570956,\n",
      "       2.49616081, 1.75242024, 2.49289777, 1.49761171, 2.81418327,\n",
      "       2.17065121, 1.6230287 , 1.43368971, 2.01738904, 1.85588634,\n",
      "       1.4431121 , 2.21520935, 2.32189953, 1.78084952, 2.38324342,\n",
      "       1.98370785, 2.15841318, 1.58223243, 2.83679963, 2.23573104,\n",
      "       1.80589055, 2.32730994, 1.47062394, 2.27068148, 1.32681134,\n",
      "       1.42224261, 1.34642174, 1.32988017, 2.46422793, 1.99920026,\n",
      "       2.06359389, 1.03247482, 2.30694027, 1.04475536, 1.57700623,\n",
      "       2.45191579, 2.27733145, 2.07448679, 1.57554551, 1.73402343,\n",
      "       2.33463815, 2.00381026, 1.80155308, 1.80902048, 2.55462204,\n",
      "       1.10636809, 1.99089476, 2.73747909, 1.79471381, 2.14407607,\n",
      "       1.58658515, 1.22312976, 1.75150796, 1.52203317, 0.65295242,\n",
      "       2.32166091, 2.31925037, 1.93044075, 2.36307172, 1.68502226,\n",
      "       2.19937172, 2.17623803, 2.62521808, 1.98566936, 1.73215315,\n",
      "       2.30178626, 2.24525852, 1.42728903, 1.75778885, 2.17754753,\n",
      "       1.55770049, 2.15058699, 1.45738109, 2.10375375, 2.27346018,\n",
      "       2.75565524, 1.66713116, 1.98046436, 1.72775287, 1.57050211,\n",
      "       2.39959328, 2.11334413, 2.3504457 , 1.00638629, 1.56904186,\n",
      "       1.93241521, 0.40360923, 1.5602052 , 1.96582245, 2.12817061,\n",
      "       2.04560396, 2.71791114, 2.22002511, 2.24122622, 1.42331033]), 'rates': array([0.03676784, 0.4184739 , 0.13134559, 0.05110426, 0.06600856,\n",
      "       0.09818811, 0.35733873, 0.1404562 , 0.32295582, 0.52744608,\n",
      "       0.45674226, 0.3320613 , 0.06808929, 0.89566837, 0.24025785,\n",
      "       0.25315377, 0.08857617, 0.11632325, 0.30242331, 0.29127183,\n",
      "       0.75963604, 0.70688346, 0.10542011, 0.23495357, 0.08645059,\n",
      "       0.18813652, 0.09204447, 0.18100859, 0.65693004, 0.17415809,\n",
      "       0.36412345, 0.32385581, 0.26577646, 0.13959352, 0.42105749,\n",
      "       0.31914982, 0.19346055, 0.80467728, 0.10549928, 0.26102028,\n",
      "       0.30027149, 0.45300858, 0.09690448, 0.20528581, 0.22845946,\n",
      "       0.07361526, 0.83913597, 0.06007361, 0.66748577, 0.16575599,\n",
      "       0.19691683, 0.09931376, 0.29369139, 0.31751057, 0.41654177,\n",
      "       0.22576409, 0.12980865, 0.1124185 , 0.19732721, 0.19960876,\n",
      "       0.50426269, 0.61953499, 0.58315631, 0.15946637, 0.56985417,\n",
      "       0.12056946, 0.18587597, 0.74315322, 0.2815063 , 0.06231943,\n",
      "       0.17976483, 0.05344928, 0.41609015, 0.30909729, 0.14440012,\n",
      "       0.16270971, 0.82609219, 0.53666963, 0.15808953, 0.42395929,\n",
      "       0.36821255, 0.11876546, 0.32035152, 0.16500848, 0.06248195,\n",
      "       0.14674931, 0.15640922, 0.39458918, 0.54374563, 0.21238414,\n",
      "       0.83048411, 0.1389236 , 0.18241402, 0.34493169, 0.20919638,\n",
      "       0.5968075 , 0.27472675, 0.2149945 , 0.38082905, 0.83938473]), '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 0x1a1d7c54a8>]\n",
      "Configurations: [<cadCAD.configuration.Configuration object at 0x1a1d7c54a8>]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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: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:37: RuntimeWarning: invalid value 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 0x110490748>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d6de780>"
      ]
     },
     "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 0x1a23ce1358>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116eb37b8>"
      ]
     },
     "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 0x1a24110c88>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2411e9e8>"
      ]
     },
     "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 0x1a2899fd30>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a24099470>"
      ]
     },
     "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 0x1a26e01fd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d990dd8>"
      ]
     },
     "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 0x1a2b5de668>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a240995c0>"
      ]
     },
     "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 0x1a2b608c18>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25e00cc0>"
      ]
     },
     "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 0x1a2e93b390>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25bb35f8>"
      ]
     },
     "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 0x1a2275eb00>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2934bf28>"
      ]
     },
     "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 0x1a2f016278>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2e91fb00>"
      ]
     },
     "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>burn</th>\n",
       "      <td>0.097280</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.213036e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.098440</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.997063e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.108184</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.966523e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102350</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.907633e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.103033</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.006300e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.849333e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102270</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.231908e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.101557</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.292577e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104592</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.976315e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095315</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.369188e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.098925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.734690e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104201</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.050262e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.101930</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.915944e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.094379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.220463e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.093262</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.434963e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">13</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100753</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.633410e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.100398</td>\n",
       "      <td>737.0</td>\n",
       "      <td>7.772904e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.724260e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096343</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.700423e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.105294</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.913779e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.772454e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.100850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.542538e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.110949</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.971453e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101868</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.875830e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.100854</td>\n",
       "      <td>499.0</td>\n",
       "      <td>1.459177e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">21</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.105397</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.439227e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.103701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.533058e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097898</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.335085e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103776</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.963800e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104340</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.635824e+02</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 rowspan=\"2\" valign=\"top\">78</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104059</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.939427e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.106646</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.074052e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.106117</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.170078e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103381</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.223600e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096914</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.931094e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">82</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102154</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.914086e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.100035</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.280208e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.096588</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.750186e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.097687</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.581040e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100963</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.752490e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">86</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103238</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.105773e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.107730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.117344e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.105481</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.135542e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.100319</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.412830e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095648</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.182710e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">90</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100772</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.309016e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.102112</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.258986e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.099567</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.588342e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.095442</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.408984e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">93</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.103359</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.765758e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.103483</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.918647e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">94</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.102403</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.326260e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.101228</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.526479e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">95</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.100937</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.248715e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.104319</td>\n",
       "      <td>285.0</td>\n",
       "      <td>6.626013e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.669789e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.104439</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.364037e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">98</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.101628</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.932041e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.105200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.014343e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.102819</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.137663e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                pbar                  amt\n",
       "              median  count        median\n",
       "agent mech                               \n",
       "0     burn  0.097280    1.0  1.213036e+04\n",
       "1     bond  0.098440    1.0  2.997063e+02\n",
       "2     bond  0.108184    1.0  3.966523e+02\n",
       "3     bond  0.102350    8.0  4.907633e-10\n",
       "      burn  0.103033   35.0  1.006300e-09\n",
       "4     bond  0.102364    1.0  8.849333e+02\n",
       "5     bond  0.102270    1.0  1.231908e+03\n",
       "6     burn  0.101557    1.0  7.292577e+03\n",
       "7     bond  0.104592    1.0  2.976315e+03\n",
       "8     burn  0.095315    1.0  1.369188e+04\n",
       "9     bond  0.098925    1.0  3.734690e+02\n",
       "10    bond  0.104201    2.0  1.050262e+03\n",
       "      burn  0.101930    1.0  2.915944e-09\n",
       "11    burn  0.094379    1.0  1.220463e+04\n",
       "12    burn  0.093262    1.0  8.434963e+03\n",
       "13    bond  0.100753   36.0  5.633410e-10\n",
       "      burn  0.100398  737.0  7.772904e-10\n",
       "14    burn  0.095701    1.0  5.724260e+03\n",
       "15    burn  0.096343    1.0  2.700423e+04\n",
       "16    bond  0.105294    1.0  3.913779e+03\n",
       "17    bond  0.101469    1.0  4.772454e+02\n",
       "18    burn  0.100850    1.0  6.542538e+03\n",
       "19    bond  0.110949    1.0  1.971453e+02\n",
       "20    bond  0.101868    7.0  4.875830e+02\n",
       "      burn  0.100854  499.0  1.459177e-09\n",
       "21    bond  0.105397    2.0  1.439227e+02\n",
       "      burn  0.103701    1.0  1.533058e-09\n",
       "22    burn  0.097898    1.0  1.335085e+04\n",
       "23    bond  0.103776    1.0  5.963800e+02\n",
       "24    bond  0.104340    1.0  2.635824e+02\n",
       "...              ...    ...           ...\n",
       "78    bond  0.104059    1.0  5.939427e+02\n",
       "      burn  0.106646    1.0  2.074052e-09\n",
       "79    bond  0.106117    1.0  2.170078e+02\n",
       "80    bond  0.103381    1.0  2.223600e+02\n",
       "81    burn  0.096914    1.0  6.931094e+03\n",
       "82    bond  0.102154    2.0  9.914086e+02\n",
       "      burn  0.100035   31.0  2.280208e-09\n",
       "83    burn  0.096588    1.0  7.750186e+03\n",
       "84    burn  0.097687    1.0  1.581040e+04\n",
       "85    bond  0.100963    1.0  1.752490e+03\n",
       "86    bond  0.103238    1.0  1.105773e+03\n",
       "      burn  0.107730    1.0  2.117344e-09\n",
       "87    bond  0.105481    1.0  1.135542e+03\n",
       "88    burn  0.100319    1.0  8.412830e+03\n",
       "89    burn  0.095648    1.0  1.182710e+04\n",
       "90    bond  0.100772    1.0  9.309016e+02\n",
       "      burn  0.102112    2.0  1.258986e+04\n",
       "91    burn  0.099567    1.0  1.588342e+04\n",
       "92    burn  0.095442    1.0  7.408984e+03\n",
       "93    bond  0.103359    1.0  5.765758e+02\n",
       "      burn  0.103483    6.0  1.918647e-09\n",
       "94    bond  0.102403    2.0  8.326260e+02\n",
       "      burn  0.101228    1.0  2.526479e-09\n",
       "95    bond  0.100937   36.0  3.248715e-10\n",
       "      burn  0.104319  285.0  6.626013e-10\n",
       "96    bond  0.104103    1.0  9.669789e+02\n",
       "97    bond  0.104439    1.0  1.364037e+03\n",
       "98    bond  0.101628    2.0  9.932041e+02\n",
       "      burn  0.105200    1.0  6.014343e+03\n",
       "99    burn  0.102819    1.0  7.137663e+03\n",
       "\n",
       "[125 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 0x1a22961630>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2289f8d0>"
      ]
     },
     "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 0x1a23224860>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2f097160>"
      ]
     },
     "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 0x1a23314e80>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2934bcc0>"
      ]
     },
     "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 0x1a23e96be0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23c5af60>"
      ]
     },
     "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 0x1a23bd0320>"
      ]
     },
     "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 0x1a253bfb70>"
      ]
     },
     "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 0x1a266edbe0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a24827dd8>"
      ]
     },
     "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([52., 12.,  8., 13.,  7.,  2.,  4.,  1.,  0.,  1.]),\n",
       " array([   0.        ,  579.18717261, 1158.37434522, 1737.56151782,\n",
       "        2316.74869043, 2895.93586304, 3475.12303565, 4054.31020825,\n",
       "        4633.49738086, 5212.68455347, 5791.87172608]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X18xsP632x9vXU0d+5mf5uGu1XAAstmPv7xlcHTPxnvzEqyR1bBamayRJIzLkJaljhrwkdcyQl6SOGfKS1DFDXpI6ZshLUscMeUnq2P8B7HlgPn1wUEIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a259f0f98>"
      ]
     },
     "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 0x1a259f0e80>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a26ae30f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini_h.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([45., 12., 14.,  7.,  8.,  5.,  3.,  2.,  1.,  3.]),\n",
       " array([    0.        ,  4991.14017414,  9982.28034827, 14973.42052241,\n",
       "        19964.56069655, 24955.70087068, 29946.84104482, 34937.98121896,\n",
       "        39929.12139309, 44920.26156723, 49911.40174136]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADUhJREFUeJzt3W+IXNd5x/HvU63/pHWoJGtthGW6MohiBVrbXVwblxKUmjqWif0iBZtSRCsQNCk4pJDKDRQCfWGn0JjQQixqUxXS2G6SIqMkJEK1aUuLnFUs21IVVbKqtkLCuyFR07xpq+TpizmyR/KuZmZ3VjP76PuBYc49c2bvc1Z3f3t1/8xGZiJJWvl+atQFSJKGw0CXpCIMdEkqwkCXpCIMdEkqwkCXpCIMdEkqwkCXpCIMdEkqYuJKrmzdunU5NTV1JVcpSSvewYMHv5eZk73GXdFAn5qaYmZm5kquUpJWvIj4937GechFkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoq4oneKLsXUzq+NZL2nntw6kvVK0qDcQ5ekIgx0SSrCQJekIgx0SSrCQJekIgx0SSrCQJekIgx0SSrCQJekIgx0SSrCQJekIgx0SSrCQJekIgx0SSrCQJekIgx0SSrCQJekIgx0SSqi70CPiFUR8VpE7G3LGyPiQEQcj4gXIuLa5StTktTLIHvojwNHu5afAj6XmZuAHwDbh1mYJGkwfQV6RGwAtgJ/0ZYD2AJ8uQ3ZDTyyHAVKkvrT7x7608CngJ+05RuBc5l5vi2fBm4Zcm2SpAH0DPSIeAiYzcyD3d3zDM0F3r8jImYiYmZubm6RZUqSeulnD/0+4CMRcQp4ns6hlqeB1REx0cZsAM7M9+bM3JWZ05k5PTk5OYSSJUnz6RnomflEZm7IzCngUeDvMvM3gZeBj7Zh24A9y1alJKmnpVyH/gfAJyPiBJ1j6s8OpyRJ0mJM9B7yrsx8BXiltU8Cdw+/JEnSYninqCQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQVYaBLUhEGuiQV0TPQI+L6iHg1Il6PiCMR8ZnWvzEiDkTE8Yh4ISKuXf5yJUkL6WcP/X+ALZn5i8AdwAMRcQ/wFPC5zNwE/ADYvnxlSpJ66Rno2fGjtnhNeySwBfhy698NPLIsFUqS+tLXMfSIWBURh4BZYB/wFnAuM8+3IaeBW5anRElSP/oK9Mz8cWbeAWwA7gZun2/YfO+NiB0RMRMRM3Nzc4uvVJJ0WQNd5ZKZ54BXgHuA1REx0V7aAJxZ4D27MnM6M6cnJyeXUqsk6TL6ucplMiJWt/b7gF8DjgIvAx9tw7YBe5arSElSbxO9h7Ae2B0Rq+j8AngxM/dGxL8Az0fEHwOvAc8uY52SpB56BnpmvgHcOU//STrH0yVJY8A7RSWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpCANdkoow0CWpiJ6BHhG3RsTLEXE0Io5ExOOtf21E7IuI4+15zfKXK0laSD976OeB38/M24F7gI9HxGZgJ7A/MzcB+9uyJGlEegZ6Zp7NzO+09n8DR4FbgIeB3W3YbuCR5SpSktTbQMfQI2IKuBM4ANycmWehE/rATcMuTpLUv74DPSJuAL4CfCIzfzjA+3ZExExEzMzNzS2mRklSH/oK9Ii4hk6YfzEzv9q6346I9e319cDsfO/NzF2ZOZ2Z05OTk8OoWZI0j36ucgngWeBoZv5p10svAdtaexuwZ/jlSZL6NdHHmPuA3wLejIhDre8PgSeBFyNiO/AfwG8sT4mSpH70DPTM/EcgFnj5Q8MtR5K0WN4pKklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFGOiSVMTEqAvQwqZ2fm0k6z315NaRrFfS0riHLklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFGOiSVISBLklFeKdoD6O6W1OSBuUeuiQVYaBLUhEGuiQVYaBLUhE9Az0inouI2Yg43NW3NiL2RcTx9rxmecuUJPXSzx76XwIPXNK3E9ifmZuA/W1ZkjRCPQM9M/8e+P4l3Q8Du1t7N/DIkOuSJA1oscfQb87MswDt+abhlSRJWoxlPykaETsiYiYiZubm5pZ7dZJ01VpsoL8dEesB2vPsQgMzc1dmTmfm9OTk5CJXJ0nqZbGB/hKwrbW3AXuGU44kabH6uWzxS8A/Az8fEacjYjvwJHB/RBwH7m/LkqQR6vnhXJn52AIvfWjItUiSlsA7RSWpCANdkoow0CWpCANdkorwLxZprIzqL0SdenLrSNYrDZN76JJUhIEuSUUY6JJUhIEuSUV4UlTvMaoTk5KWxj10SSrCQJekIgx0SSrCQJekIgx0SSrCQJekIgx0SSrCQJekIryxSGK0N1P5SY8aFvfQJakIA12SijDQJakIA12SijDQJakIA12SijDQJakIA12SivDGImnERnVT09V4Q1P177V76JJUhIEuSUUY6JJUhIEuSUV4UlS6SvkJk/W4hy5JRRjoklSEgS5JRXgMXdIVN8rj95UtaQ89Ih6IiGMRcSIidg6rKEnS4BYd6BGxCvhz4MPAZuCxiNg8rMIkSYNZyh763cCJzDyZmf8LPA88PJyyJEmDWkqg3wL8Z9fy6dYnSRqBpZwUjXn68j2DInYAO9rijyLi2CLXtw743iLfu1I556uDcy4ungKWNuef62fQUgL9NHBr1/IG4MylgzJzF7BrCesBICJmMnN6qV9nJXHOVwfnfHW4EnNeyiGXbwObImJjRFwLPAq8NJyyJEmDWvQeemaej4jfA74JrAKey8wjQ6tMkjSQJd1YlJlfB74+pFp6WfJhmxXIOV8dnPPVYdnnHJnvOY8pSVqB/CwXSSpiRQT6Sv6IgYh4LiJmI+JwV9/aiNgXEcfb85rWHxHx+TbPNyLirq73bGvjj0fEtq7+X4qIN9t7Ph8R811OekVFxK0R8XJEHI2IIxHxeOsvO++IuD4iXo2I19ucP9P6N0bEgVb/C+0CAiLiurZ8or0+1fW1nmj9xyLi17v6x/LnICJWRcRrEbG3LZeec0ScatveoYiYaX3jsW1n5lg/6JxwfQu4DbgWeB3YPOq6Bqj/V4G7gMNdfZ8Fdrb2TuCp1n4Q+Aada/zvAQ60/rXAyfa8prXXtNdeBe5t7/kG8OExmPN64K7Wfj/wr3Q+HqLsvFsdN7T2NcCBNpcXgUdb/xeA323tjwFfaO1HgRdae3Pbxq8DNrZtf9U4/xwAnwT+GtjblkvPGTgFrLukbyy27ZFvDH188+4Fvtm1/ATwxKjrGnAOU1wc6MeA9a29HjjW2s8Aj106DngMeKar/5nWtx74blf/RePG5QHsAe6/WuYN/DTwHeCX6dxIMtH639mW6Vwddm9rT7Rxcen2fWHcuP4c0Ln/ZD+wBdjb5lB9zqd4b6CPxba9Eg65VPyIgZsz8yxAe76p9S8018v1n56nf2y0/1bfSWePtfS826GHQ8AssI/O3uW5zDzfhnTX+c7c2uv/BdzI4N+LUXsa+BTwk7Z8I/XnnMC3IuJgdO6EhzHZtlfC56H39REDRSw010H7x0JE3AB8BfhEZv7wMocCS8w7M38M3BERq4G/BW6fb1h7HnRu8+18jXTOEfEQMJuZByPigxe65xlaZs7NfZl5JiJuAvZFxHcvM/aKbtsrYQ+9r48YWGHejoj1AO15tvUvNNfL9W+Yp3/kIuIaOmH+xcz8ausuP2+AzDwHvELnmOnqiLiw49Rd5ztza6//LPB9Bv9ejNJ9wEci4hSdT1vdQmePvfKcycwz7XmWzi/uuxmXbXvUx6P6OF41QeeEwUbePTHygVHXNeAcprj4GPqfcPEJlM+29lYuPoHyautfC/wbnZMna1p7bXvt223shRMoD47BfAP4K+DpS/rLzhuYBFa39vuAfwAeAv6Gi08Qfqy1P87FJwhfbO0PcPEJwpN0Tg6O9c8B8EHePSlads7AzwDv72r/E/DAuGzbI98Q+vwmPkjnSom3gE+Pup4Ba/8ScBb4Pzq/fbfTOW64Hzjeni/8QwadPxryFvAmMN31dX4HONEev93VPw0cbu/5M9rNYiOe86/Q+W/iG8Ch9niw8ryBXwBea3M+DPxR67+NzlULJ1rQXdf6r2/LJ9rrt3V9rU+3eR2j6wqHcf454OJALzvnNrfX2+PIhZrGZdv2TlFJKmIlHEOXJPXBQJekIgx0SSrCQJekIgx0SSrCQJekIgx0SSrCQJekIv4fueRe9/779D4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a26caa198>"
      ]
     },
     "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 0x1a26cfeef0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a26d73400>"
      ]
     },
     "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 0x1a27025630>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kJ4GbgS9SDfsvAl8Cfmv6ByVtBDYCvOtd7zqmYpcsqpb7xvA4vT1dvHzgMIsqJf78X50HwHcfe5m/eXgPw2PjLKqUj2lbZmbHYvD1IwDc+GvncHxPFz938juavs2GBkUjYm/2uB+4Gzg/IvZFxHhETAC3AOfnfHZTRPRHRH9fX98xFbu0pxroh4ZHATh4ZIx3HNfF+rNXsf7sVZxz6jsml5uZtdPBI9WcuuxnV7P+7FWsXLqo6dssDHRJSyT11p4DlwLbJa2estpHgO3NKfGo3izQX88C++CRscmQB1iaHcEfcqCbWZsdOjJGuSR6ulp3dngjXS6rgLtVHZCsAN+MiHsl/ZWkdVS7XHYDn2halZneaYF9cHiM3imDDLXnh4Yd6GbWXoeGx+jtqZBlZ0sUBnpEPAucU2f5x5pS0Qxqgf2ZOx7llUPDAFz4MydMvl87Qr9284P0dLWmD72nUubLH/t5Tlu5pCXbM7POtfXF1/jstx9ldHyCnxwaYfmS5p7VMl1Spy2e0beEay5cw6tvjPA/t+4F4Kp1J0++v+7UZfzGBe9qWZfLoeEx7t+xn+0vHXCgmxkPP/8qu/Yf4l+8bzVdJfGBM1a2dPtJBXqlXOL6X/lZgMlAv/y9R7vyj+su85+vem/L6nn5wBHu37HFg7BmBhw9IeOmX1tHpdz6mVWSmsulnu42/KXV1AZka6PZZvb2dvDIKMd1ldsS5rAAAr2r3LoBh+mWdJcpyYOwZlZVGwhtl6S6XOpp5QhyvW0v7q5w/479/N6lZ87rn/343gN897HBef0z58svv+8kzl59fLvLsMzQwWH++sfPZ1dQWzs99PyrDvSUjY5PsPuVN+b9z/2Lf3iWex7dOzndQacYmwheevUw/33Due0uxTL/e9tebtqyk3JJdNa35e3pyiknarSaA/0Yffyfn87N//AMETGvvy0cHh3nrHf2cu+nL5q3P3M+/PKf/Wjywi7rDAcOV/fHU1+8rG19t9YZvPePUW9PhfGJ4M2R8Xn9c4fHJlhU6bzds3RRxYPAHabdA3HWOfwNOEZH55eZ36PWkQ6dYKy3p8unaXaYdg/EWefwt+AY1a5e3bbnAKeuGGnoM6csXzx5VWuekbEJFnd33u7p7anwwk/fbPl2I4Jnht6Y9cDf0kUVTlm+uElVNc+R0XF2/6SxsZm9B468ZU4je/vyt+AYrVzSDcDHp8x7XOQDZ5zANz9+wYzrDI9NsGxx5/0C1V0u8ebIODsGX2/pmS5/u22QT932yJw++4PP/iJrEruS9w/vfoy7Hn6p4fXPX7OiidVYKpIN9Ac/d0lHnKb1T08/ga9f834ON9iHfuv/fY7BA0cK1xvp0D70K967mm89+CKDBw63NND3vnYYgJs2rGv4YrKn9x3ixvufZvDAkeQCfe9rh1l74lI+88F3N7R+K+bats6XbKD39TZ/buFGlEua1V1IfrjzFZ575eXC9UbGJ+juwEA/eflxQOvnnK9NRfrhc05q+GyiU5Yf4Mb7n05yEPfQ8Binrlj8lqktzIp0XmIscMf3VBoKw+HRibZOa5CnNoVxqwP94JFRli6a3VSkvU0asG6Fg0c80Gmz13mJscD19lQYHpuYvHFsnpHxCRa1cGL8RtUGgVse6MNjhQPJ0/X2tOc/n/lw6Mjs22vmb0yL1f6RXnbTDynPcLT56psjdJc777TFnq4SlZK45UfPctfDe1q23cEDRzgl6+5pVO3Mjz/7+5389Y+fb0ZZTfPTN0d85orNmr8xLXbxWat45MXXGB2f+Qj93e/s5cPrTmpRVY2TxKcvWcsTg6+3dLtrVy3l4rNWzeoziyplPnXxz7Br6FCTqmqeM9/Zy6+8r/P2v3U2RUTLNtbf3x8DA42f3mdmZiDpoYjoL1qvoSN0SbuBg8A4MBYR/ZJWALcDa6jeU/RfRsSrcy3YzMyOzWxG3X4pItZN+V/iOmBLRKwFtmSvzcysTY7lNIorgc3Z883AVcdejpmZzVWjgR7A9yQ9JGljtmxVRAwCZI91r66RtFHSgKSBoaGhY6/YzMzqavQslwsjYq+kE4H7JD3Z6AYiYhOwCaqDonOo0czMGtDQEXpE7M0e9wN3A+cD+yStBsge9zerSDMzK1YY6JKWSOqtPQcuBbYD9wBXZ6tdDXynWUWamVmxRrpcVgF3Z3NoVIBvRsS9kh4E7pB0LfAC8KvNK9PMzIq09MIiSUPAXK/BXgm8Mo/ltNNCactCaQe4LZ3Kban6JxHRV7RSSwP9WEgaaORKqRQslLYslHaA29Kp3JbZ6bzp/MzMbE4c6GZmC0RKgb6p3QXMo4XSloXSDnBbOpXbMgvJ9KGbmdnMUjpCNzOzGXR8oEu6TNJTknZJSmJGR0m7JT0maaukgWzZCkn3SdqZPS7PlkvSn2bt2ybpvDbXfquk/ZK2T1k269olXZ2tv1PS1fW21aa2fEHSS9m+2Srpiinv/UHWlqckfWjK8rZ+ByWdKun7knZIelzS72TLk9svM7Qlxf3SI+kfJT2ateU/ZctPk/RA9nd8u6TubPmi7PWu7P01RW2ctYjo2B+gDDwDnA50A48C72l3XQ3UvRtYOW3ZnwDXZc+vA/44e34F8HeAgAuAB9pc+0XAecD2udYOrACezR6XZ8+Xd0hbvgB8ts6678m+X4uA07LvXbkTvoPAauC87Hkv8HRWb3L7ZYa2pLhfBCzNnncBD2R/33cAG7LlXwb+Xfb83wNfzp5vAG6fqY1zqanTj9DPB3ZFxLMRMQJ8i+q0vSnKm274SuAvo+rHwDJlc+S0Q0T8EPjptMWzrf1DwH0R8dOo3vTkPuCy5lf/VjltyXMl8K2IGI6I54BdVL9/bf8ORsRgRDycPT8I7ABOJsH9MkNb8nTyfomIqN3fsCv7CeBi4M5s+fT9UttfdwLrJYn8Ns5apwf6ycCLU17vYead3ylmM91wCm2cbe2d3qZPZl0Rt9a6KUikLdmv6edSPRpMer9MawskuF8klSVtpTo54X1Uj65fi4ixOnVN1py9fwA4gXlsS6cHuuosS+G0nAsj4jzgcuA/SLpohnVTbSPk197JbboZOANYBwwCX8qWd3xbJC0F/gb4dETMdJfuFNuS5H6JiPGIWAecQvWo+ux6q2WPTW9Lpwf6HuDUKa9PAfa2qZaGxeymG06hjbOtvWPbFBH7sn+EE8AtHP3VtqPbIqmLagB+IyLuyhYnuV/qtSXV/VITEa8BP6Dah75MUm3iw6l1Tdacvf8Oql2C89aWTg/0B4G12ahxN9WBhHvaXNOMNPvphu8BfjM7M+EC4EDt1+gOMtva/w9wqaTl2a/Ol2bL2m7a+MRHqO4bqLZlQ3YmwmnAWuAf6YDvYNbP+lVgR0T8tylvJbdf8tqS6H7pk7Qse34ccAnVMYHvAx/NVpu+X2r766PA30d1VDSvjbPXylHhufxQHbF/mmrf1OfaXU8D9Z5OdcT6UeDxWs1U+8q2ADuzxxVxdKT8f2Ttewzob3P9t1H9lXeU6pHDtXOpHfgtqoM7u4BrOqgtf5XVui37h7R6yvqfy9ryFHB5p3wHgX9G9VfwbcDW7OeKFPfLDG1Jcb+8D3gkq3k78Pls+elUA3kX8G1gUba8J3u9K3v/9KI2zvbHV4qamS0Qnd7lYmZmDXKgm5ktEA50M7MFwoFuZrZAONDNzBYIB7qZ2QLhQDczWyAc6GZmC8T/B8bTpKPs/qpsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25fa6ba8>"
      ]
     },
     "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([29., 15., 17., 14.,  8.,  8.,  3.,  2.,  2.,  2.]),\n",
       " array([ 686.08043782, 1196.65956664, 1707.23869547, 2217.81782429,\n",
       "        2728.39695312, 3238.97608195, 3749.55521077, 4260.1343396 ,\n",
       "        4770.71346842, 5281.29259725, 5791.87172608]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eBva027U99gz8GvC11ute4M/a+IsZBdcTwD8CZ7Tx57f1J9rjL172Wu9t/waPAa+bdW/r6P0V/P9ZN1322/r6ers9Cry3jXf3Xm41bgYW2/v5nxidNTP1Xv1mrCR1rudDN5IkDHpJ6p5BL0mdM+glqXMGvSR1zqCXpM4Z9JLUOYNekjr3f+d4qJb6JBE2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a283dc588>"
      ]
     },
     "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 0x1a2884a400>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a26d835f8>"
      ]
     },
     "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([ 2.,  4., 10., 22., 22., 30., 10.]),\n",
       "  array([ 2.,  4.,  9., 23., 22., 30., 10.]),\n",
       "  array([ 2.,  4., 10., 23., 22., 30.,  9.]),\n",
       "  array([ 2.,  4., 10., 24., 22., 30.,  8.]),\n",
       "  array([ 2.,  5., 10., 25., 21., 29.,  8.]),\n",
       "  array([ 2.,  5., 10., 25., 23., 27.,  8.]),\n",
       "  array([ 2.,  5., 11., 24., 24., 26.,  8.])],\n",
       " array([-0.80437477, -0.63190418, -0.4594336 , -0.28696302, -0.11449244,\n",
       "         0.05797815,  0.23044873,  0.40291931]),\n",
       " <a list of 7 Lists of Patches objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x4FRgRQJ9AuPZDdxWVfd2z/l35sezIoE+gfH8LnB6klcBjwYOT/JwVa3IevkTGA9JHgNcB/x1Vd28TKWOos/yG/v67E5yKPBY4DvTKW9ovZYTSfIc5j+Qn1VVP55SbaMYNJ4jmf874Ke6yc8TgGuTvLCqdoyywdW2y+VaYGN3eyNwzSJ9vgE8K8mhSQ5j/hN6te5y6TOeLwBHJdm3X/ZM4EtTqG0UA8dTVX9SVWurahb4S+C9KxXmPQwcT7esxdXMj+ODU6ytjz7Lb+w/xhcDN9bqPflk4Hi6XRTvBF5YVav14IF9fu54qup7VXVMVc1275ebmR/XSGG+74eumgvz+/a2A/d010d37XPAv3S3D2H+Bd3JfPBdttJ1jzOe7v5zgTuAO4H3AIevdO3jjGe//q8A3r7SdY/5+/ZS4H+B2/e7bFjp2vcbw/OBrzK/X/9NXdvfdcEA8wcQfBD4GvB54NdWuuYxx/NJ5g+C2PdaXLvSNY8zngP6for5I3hG3p5nikpSI1bbLhdJ0ogMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGvF/xYusKe5V2FYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a28b72748>"
      ]
     },
     "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
}