conviction/.ipynb_checkpoints/abc_sim-Copy2-checkpoint.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 = 4\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([35., 17., 14., 14.,  8.,  0.,  4.,  4.,  2.,  2.]),\n",
       " array([ 1.00320027,  3.24360193,  5.4840036 ,  7.72440526,  9.96480692,\n",
       "        12.20520859, 14.44561025, 16.68601192, 18.92641358, 21.16681525,\n",
       "        23.40721691]),\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 0x1a1dd069e8>"
      ]
     },
     "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([ 154.73765045,  502.96265376, 1693.90924367,  775.44690856,\n",
       "         894.55808457,  644.86523757,  708.52400572, 1437.59009134,\n",
       "         537.71189858,  274.43916758,  492.67369288,  391.90535758,\n",
       "        1071.82222873,  465.94066865,  278.51396621, 2572.7021714 ,\n",
       "         185.15675026,  429.50606309,  445.91601201, 1323.74521866,\n",
       "         454.3401318 , 1522.6252209 , 4986.23576439,  645.56022439,\n",
       "        1768.31682209,  239.75982482, 2028.58063911,  467.57443491,\n",
       "        3247.24473635, 3236.91068432, 1467.74118084,  199.24795827,\n",
       "         159.06129453,  369.89047044, 2867.89106393, 1418.10437073,\n",
       "         775.51148829,  859.30777879, 4729.95481298,  983.67170402,\n",
       "         364.73703018,  623.58758958,  112.66696766,  441.38052406,\n",
       "        3697.38333854,  120.91013273, 2656.72766161, 1973.84798995,\n",
       "         651.54297467, 2344.62993192,  448.54569332,  551.36796892,\n",
       "         402.03516068,  725.09745262, 1422.06413268, 2067.50593915,\n",
       "         693.50321377,  139.52448265, 1747.1278693 ,  283.97443804,\n",
       "         379.0382758 ,  631.46501793, 3633.17019879, 1790.58852623,\n",
       "         645.45557575, 2291.29540913, 1224.58914664, 2924.32661505,\n",
       "         177.9731322 ,  814.803795  , 1795.03614235,  484.53557676,\n",
       "         256.54590251, 3881.67199573,  168.99527588, 1886.26759212,\n",
       "        1080.59613515,  642.8326204 ,  498.28427186,  649.07479341,\n",
       "         589.09220743, 1401.99051325, 1045.94903105, 1960.0446318 ,\n",
       "         648.19389309,  642.09524945, 1170.92691052,  982.14800137,\n",
       "        1047.23930962,  450.26800338, 3273.79909885, 2086.3331578 ,\n",
       "         513.50650248, 2234.42266986,  747.81648918,  633.88349391,\n",
       "         544.47951044,  158.46898077, 1554.49056234, 1076.51715678]),\n",
       " 'prices': array([0.1213738 , 0.13432867, 0.11222735, 0.07467466, 0.12368996,\n",
       "        0.08954977, 0.06550798, 0.09628566, 0.08891217, 0.10544226,\n",
       "        0.08471248, 0.11228806, 0.06369425, 0.11730586, 0.10998697,\n",
       "        0.04681   , 0.09329897, 0.08107   , 0.06548223, 0.09324955,\n",
       "        0.10596881, 0.13926625, 0.12541599, 0.1361578 , 0.09265616,\n",
       "        0.09226016, 0.09542201, 0.06768405, 0.12343726, 0.09920713,\n",
       "        0.09686712, 0.12618883, 0.03724175, 0.08854714, 0.06794041,\n",
       "        0.10383672, 0.07824921, 0.09006191, 0.10768772, 0.07900866,\n",
       "        0.08722659, 0.08022733, 0.07317946, 0.06899785, 0.10662008,\n",
       "        0.0481739 , 0.16860387, 0.13446379, 0.05698327, 0.06623915,\n",
       "        0.10624604, 0.10161397, 0.07964077, 0.12415236, 0.07041923,\n",
       "        0.11817846, 0.05427553, 0.11553984, 0.09320489, 0.08445738,\n",
       "        0.1258097 , 0.13451179, 0.04387886, 0.06255358, 0.10660563,\n",
       "        0.10403631, 0.08750516, 0.06193713, 0.13014692, 0.10490218,\n",
       "        0.09706312, 0.07813724, 0.10707981, 0.06341226, 0.07738785,\n",
       "        0.0804033 , 0.07891931, 0.06215608, 0.08254963, 0.1004005 ,\n",
       "        0.08218345, 0.1308386 , 0.05465287, 0.07613838, 0.08816439,\n",
       "        0.10151549, 0.08671337, 0.10927614, 0.12303076, 0.09782447,\n",
       "        0.08425152, 0.07113051, 0.1116795 , 0.05941108, 0.13430994,\n",
       "        0.10720763, 0.11302991, 0.14103919, 0.0526303 , 0.09438985]),\n",
       " 'reserve': 50000.0,\n",
       " 'spot_price': 0.2,\n",
       " 'supply': 1000000.0,\n",
       " 'tokens': array([12620.27877965, 11266.99906711,  5550.29198522,  6151.92922106,\n",
       "         6272.47340147,  7734.15365957,  6782.90091121, 16968.25560166,\n",
       "         9644.79278017,  7009.87891767,  5326.94367918,  5401.4885626 ,\n",
       "         5922.42921676,  6984.93353358,  7217.89254448,  9338.76119812,\n",
       "         6418.43717925,  5949.96668173,  7133.44818632, 12369.49641107,\n",
       "         6036.00542987,  6539.36706759, 34686.39424314,  9764.2871515 ,\n",
       "         5472.89039706,  6464.27478697,  7579.23183983,  5831.88094519,\n",
       "         5872.29494928, 12163.53546692,  9867.01891755,  6599.6243106 ,\n",
       "         5244.33907956,  9468.20058928,  9359.92788544, 13304.43962393,\n",
       "         8505.56363506,  6071.63447732, 11206.53382116,  6047.7709718 ,\n",
       "         6569.91530961, 11245.90050349,  5281.90604854, 15662.36428934,\n",
       "         6911.91031517, 15407.51069362, 11516.86449005, 17069.6092172 ,\n",
       "         5478.06852008,  7075.09174437,  7993.54292249,  7560.11849768,\n",
       "         7703.33351161,  7890.01786913,  8272.89317598,  6685.45175789,\n",
       "         6573.91010551, 10086.1386074 ,  7726.4809972 , 11926.27186555,\n",
       "         5383.35546406, 17963.29540929, 19499.13909203,  7064.06346207,\n",
       "        13849.86255872,  6040.25698787, 25623.15430301,  6749.45414737,\n",
       "        12110.16791601, 32581.25561983, 15968.20948034, 11095.2947078 ,\n",
       "         8961.6840974 ,  6419.10931038,  6421.99292279, 10766.53967773,\n",
       "        14056.08645969,  9295.74408266, 12333.22926933, 12013.8631499 ,\n",
       "        14048.1908866 , 12223.82778558,  6119.84832944, 13737.77117994,\n",
       "        21800.53899528, 10466.68680701,  5700.42624272,  6274.87393016,\n",
       "        10884.2882081 ,  6186.373975  ,  9183.36608831, 17358.51618838,\n",
       "         8683.79722436,  7687.29641523, 21276.41997192,  9978.19457972,\n",
       "         9033.39416903, 10254.47797541,  6069.58878196,  6048.09659872])}"
      ]
     },
     "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': 4, 'lambda': 200, 'gains': array([2.42747606, 2.68657347, 2.24454705, 1.49349326, 2.47379923,\n",
      "       1.79099536, 1.31015956, 1.9257131 , 1.77824338, 2.10884522,\n",
      "       1.69424958, 2.24576117, 1.27388507, 2.34611718, 2.1997394 ,\n",
      "       0.9362    , 1.86597932, 1.62140001, 1.30964455, 1.86499098,\n",
      "       2.1193762 , 2.78532497, 2.50831976, 2.72315591, 1.8531232 ,\n",
      "       1.8452032 , 1.90844011, 1.35368092, 2.46874524, 1.98414263,\n",
      "       1.9373424 , 2.52377657, 0.74483501, 1.77094288, 1.3588082 ,\n",
      "       2.07673435, 1.56498416, 1.80123821, 2.15375431, 1.58017319,\n",
      "       1.74453173, 1.60454664, 1.46358911, 1.37995706, 2.13240168,\n",
      "       0.96347804, 3.37207745, 2.68927575, 1.13966534, 1.32478297,\n",
      "       2.12492076, 2.03227932, 1.59281542, 2.48304719, 1.40838453,\n",
      "       2.36356917, 1.08551054, 2.3107968 , 1.86409775, 1.68914768,\n",
      "       2.51619398, 2.6902358 , 0.87757711, 1.25107168, 2.13211265,\n",
      "       2.08072616, 1.75010318, 1.23874266, 2.60293834, 2.09804363,\n",
      "       1.94126248, 1.56274474, 2.14159614, 1.2682453 , 1.54775696,\n",
      "       1.60806591, 1.57838628, 1.24312157, 1.65099252, 2.00800993,\n",
      "       1.64366893, 2.61677199, 1.09305737, 1.52276757, 1.76328785,\n",
      "       2.03030974, 1.7342674 , 2.18552271, 2.46061514, 1.9564894 ,\n",
      "       1.68503041, 1.42261025, 2.23359   , 1.18822153, 2.68619879,\n",
      "       2.14415262, 2.26059813, 2.82078374, 1.05260602, 1.88779695]), 'rates': array([0.25659687, 0.19636811, 0.13385453, 0.10969502, 0.92567352,\n",
      "       0.17906033, 0.67953496, 0.12294838, 0.68280455, 0.8432976 ,\n",
      "       0.04272187, 0.08919568, 0.06791266, 0.09285917, 0.13350043,\n",
      "       0.04535445, 0.19486033, 0.05773913, 0.19795394, 0.15570514,\n",
      "       0.16378819, 0.37686495, 0.63887141, 0.50842362, 0.14603711,\n",
      "       0.05950486, 0.05516903, 0.19810676, 0.28431815, 0.44379174,\n",
      "       0.51491332, 0.10787612, 0.12677288, 0.12929329, 0.20735097,\n",
      "       0.06069805, 0.11924595, 0.32512825, 0.12316713, 0.65063394,\n",
      "       0.48852466, 0.66826542, 0.15018932, 0.35165557, 0.41950335,\n",
      "       0.13986929, 0.21510754, 0.30533444, 0.11695145, 0.32838351,\n",
      "       0.09370459, 0.12439813, 0.04795101, 0.17000463, 0.16697976,\n",
      "       0.26683715, 0.13366172, 0.31551067, 0.10796501, 0.09103964,\n",
      "       0.09095891, 0.06770127, 0.10176358, 0.9658195 , 0.11702599,\n",
      "       0.84022364, 0.42835864, 0.39931387, 0.47525641, 0.44323299,\n",
      "       0.20551024, 0.17504677, 0.06255394, 0.10723723, 0.25442381,\n",
      "       0.09940915, 0.74320252, 0.09948109, 0.19306006, 0.3010985 ,\n",
      "       0.24528577, 0.35329695, 0.53653021, 0.99680994, 0.28418461,\n",
      "       0.73245165, 0.04861454, 0.34145252, 0.14160696, 0.33552713,\n",
      "       0.35633385, 0.36075359, 0.11822025, 0.39796279, 0.33725283,\n",
      "       0.09960267, 0.17942132, 0.21567007, 0.0555612 , 0.9560708 ]), 'population': 100, 'beta': 0.9, 'phi': 0.05, 'invariant': 2e+19, '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 0x1a1fdd46d8>]\n",
      "Configurations: [<cadCAD.configuration.Configuration object at 0x1a1fdd46d8>]\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"
     ]
    }
   ],
   "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 0x1a1fdd41d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2002bb38>"
      ]
     },
     "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 0x1a2ddc3be0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAEm9JREFUeJzt3H+s3XV9x/Hne1SBUQfFyk0txOLSLAOaob0DFrflVjYomAyMYcEQaFFTzcAsSxdX5wzEHxHd2KLDsdTZWTb1yjYNDa3DrqEzZEOhipRKGBUbbWnauNbOAnOpe++P87njcD+nPeeeH/d+q89HcnLP+ZzP93Ne93t6+rrn+z33RmYiSVK7n5vrAJKk5rEcJEkVy0GSVLEcJEkVy0GSVLEcJEkVy0GSVLEcJEkVy0GSVJk31wFOZOHChblkyZK+t3/uuec444wzhhdoiJqcDZqdr8nZoNn5zNa/Juebnm3Hjh0/yMxXDbRoZjb2snz58hzEgw8+OND2o9TkbJnNztfkbJnNzme2/jU53/RswKM54P+/HlaSJFUsB0lSxXKQJFUsB0lSxXKQJFUsB0lSxXKQJFUsB0lSxXKQJFUa/eczTlZL1m3uOmftsmOs7mHeTO25401DX1PSzx7fOUiSKpaDJKliOUiSKpaDJKliOUiSKpaDJKliOUiSKpaDJKliOUiSKpaDJKliOUiSKpaDJKliOUiSKpaDJKliOUiSKl3LISLOi4gHI+LJiNgVEb9fxs+OiK0R8XT5uqCMR0R8IiJ2R8TjEfH6trVWlflPR8Sq0X1bkqRB9PLO4RiwNjN/GbgMuCUiLgDWAdsycymwrdwGuApYWi5rgLuhVSbAbcClwCXAbVOFIklqlq7lkJn7M/Mb5fqPgCeBxcA1wMYybSNwbbl+DXBPtjwMnBURi4Arga2ZeSgzDwNbgZVD/W4kSUMxo3MOEbEEeB3wNWAsM/dDq0CAc8q0xcD32zbbW8aONy5JapjIzN4mRswH/hX4cGZ+MSJ+mJlntd1/ODMXRMRm4COZ+VAZ3wa8B3gjcGpmfqiMvx94PjPvnPY4a2gdjmJsbGz55ORk39/c0aNHmT9/ft/b92vnviNd54ydDgdeGP5jL1t85lDWmat914smZ4Nm5zNb/5qcb3q2FStW7MjM8UHWnNfLpIh4GfBPwGcz84tl+EBELMrM/eWw0cEyvhc4r23zc4Fny/jEtPHt0x8rM9cD6wHGx8dzYmJi+pSebd++nUG279fqdZu7zlm77Bh37uxp98/InhsmhrLOXO27XjQ5GzQ7n9n61+R8o8jWy6eVAvg08GRm/nnbXZuAqU8crQLuaxu/qXxq6TLgSDns9ABwRUQsKCeiryhjkqSG6eVH1zcANwI7I+KxMvbHwB3AvRHxduB7wHXlvi3A1cBu4HngZoDMPBQRHwQeKfM+kJmHhvJdSJKGqms5lHMHcZy7L+8wP4FbjrPWBmDDTAJKkmafvyEtSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkiuUgSapYDpKkStdyiIgNEXEwIp5oG7s9IvZFxGPlcnXbfe+NiN0R8VREXNk2vrKM7Y6IdcP/ViRJw9LLO4fPACs7jP9FZl5cLlsAIuIC4HrgwrLNX0XEKRFxCvBJ4CrgAuCtZa4kqYHmdZuQmV+NiCU9rncNMJmZPwa+GxG7gUvKfbsz8xmAiJgsc78948SSpJGLzOw+qVUO92fmReX27cBq4L+AR4G1mXk4Iu4CHs7Mvy/zPg18uSyzMjPfUcZvBC7NzFs7PNYaYA3A2NjY8snJyb6/uaNHjzJ//vy+t+/Xzn1Hus4ZOx0OvDD8x162+MyhrDNX+64XTc4Gzc5ntv41Od/0bCtWrNiRmeODrNn1ncNx3A18EMjy9U7gbUB0mJt0PnzVsZUycz2wHmB8fDwnJib6jAjbt29nkO37tXrd5q5z1i47xp07+939x7fnhomhrDNX+64XTc4Gzc5ntv41Od8osvX1v1NmHpi6HhGfAu4vN/cC57VNPRd4tlw/3rgkqWH6+ihrRCxqu/lmYOqTTJuA6yPi1Ig4H1gKfB14BFgaEedHxMtpnbTe1H9sSdIodX3nEBGfByaAhRGxF7gNmIiIi2kdGtoDvBMgM3dFxL20TjQfA27JzJ+UdW4FHgBOATZk5q6hfzeSpKHo5dNKb+0w/OkTzP8w8OEO41uALTNKJ0maE/6GtCSpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkiqWgySpYjlIkipdyyEiNkTEwYh4om3s7IjYGhFPl68LynhExCciYndEPB4Rr2/bZlWZ/3RErBrNtyNJGoZe3jl8Blg5bWwdsC0zlwLbym2Aq4Cl5bIGuBtaZQLcBlwKXALcNlUokqTm6VoOmflV4NC04WuAjeX6RuDatvF7suVh4KyIWARcCWzNzEOZeRjYSl04kqSGiMzsPiliCXB/Zl5Ubv8wM89qu/9wZi6IiPuBOzLzoTK+DfgjYAI4LTM/VMbfD7yQmX/W4bHW0HrXwdjY2PLJycm+v7mjR48yf/78vrfv1859R7rOGTsdDrww/MdetvjMoawzV/uuF03OBs3OZ7b+NTnf9GwrVqzYkZnjg6w5b+BULxUdxvIE4/Vg5npgPcD4+HhOTEz0HWb79u0Msn2/Vq/b3HXO2mXHuHPnsHc/7LlhYijrzNW+60WTs0Gz85mtf03ON4ps/X5a6UA5XET5erCM7wXOa5t3LvDsCcYlSQ3UbzlsAqY+cbQKuK9t/KbyqaXLgCOZuR94ALgiIhaUE9FXlDFJUgN1Pa4REZ+ndc5gYUTspfWpozuAeyPi7cD3gOvK9C3A1cBu4HngZoDMPBQRHwQeKfM+kJnTT3JLkhqiazlk5luPc9flHeYmcMtx1tkAbJhROknSnPA3pCVJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJFctBklSxHCRJlXmDbBwRe4AfAT8BjmXmeEScDXwBWALsAX43Mw9HRAAfB64GngdWZ+Y3Bnn8bnbuO8LqdZtH+RCS9FNpGO8cVmTmxZk5Xm6vA7Zl5lJgW7kNcBWwtFzWAHcP4bElSSMwisNK1wAby/WNwLVt4/dky8PAWRGxaASPL0ka0KDlkMBXImJHRKwpY2OZuR+gfD2njC8Gvt+27d4yJklqmMjM/jeOeHVmPhsR5wBbgXcDmzLzrLY5hzNzQURsBj6SmQ+V8W3AezJzx7Q119A67MTY2NjyycnJvvMdPHSEAy/0vflIjZ3OSLItW3zmUNY5evQo8+fPH8paw9bkbNDsfGbrX5PzTc+2YsWKHW2H+vsy0AnpzHy2fD0YEV8CLgEORMSizNxfDhsdLNP3Aue1bX4u8GyHNdcD6wHGx8dzYmKi73x/+dn7uHPnQN/iyKxddmwk2fbcMDGUdbZv384g+36UmpwNmp3PbP1rcr5RZOv7sFJEnBERr5i6DlwBPAFsAlaVaauA+8r1TcBN0XIZcGTq8JMkqVkG+dF1DPhS6xOqzAM+l5n/HBGPAPdGxNuB7wHXlflbaH2MdTetj7LePMBjS5JGqO9yyMxngF/pMP6fwOUdxhO4pd/HkyTNHn9DWpJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSRXLQZJUsRwkSZV5cx1Aw7Vk3eahrLN22TFWz2CtPXe8aSiPK6kZfOcgSapYDpKkiuUgSapYDpKkiuUgSar4aSUNxbA+JdWL9k9S+SkpaTR85yBJqsx6OUTEyoh4KiJ2R8S62X58SVJ3s3pYKSJOAT4J/DawF3gkIjZl5rdnM4d+eszm4azpPKSln2azfc7hEmB3Zj4DEBGTwDWA5aCTzvGKaaa/XT5TlpJmw2yXw2Lg+2239wKXznIG6aQ2yLulQYvLYvrZEZk5ew8WcR1wZWa+o9y+EbgkM9/dNmcNsKbc/CXgqQEeciHwgwG2H6UmZ4Nm52tyNmh2PrP1r8n5pmd7TWa+apAFZ/udw17gvLbb5wLPtk/IzPXA+mE8WEQ8mpnjw1hr2JqcDZqdr8nZoNn5zNa/JucbRbbZ/rTSI8DSiDg/Il4OXA9smuUMkqQuZvWdQ2Yei4hbgQeAU4ANmblrNjNIkrqb9d+QzswtwJZZerihHJ4akSZng2bna3I2aHY+s/WvyfmGnm1WT0hLkk4O/vkMSVLlpCyHiDg7IrZGxNPl64LjzFtV5jwdEavaxpdHxM7yJzw+ERFRxm+PiH0R8Vi5XD2DTCf8syARcWpEfKHc/7WIWNJ233vL+FMRcWWva85xtj1lHz4WEY/2m22QfBHxyoh4MCKORsRd07bp+Bw3JNv2subUv7NzZjnbb0fEjrJ/dkTEG9u2Gcp+G2G+ud53l7Q99rci4s29rtmAfDN7zWbmSXcBPgasK9fXAR/tMOds4JnydUG5vqDc93Xg14AAvgxcVcZvB/6wjzynAN8BXgu8HPgWcMG0Ob8H/HW5fj3whXL9gjL/VOD8ss4pvaw5V9nKfXuAhUN4LgfJdwbw68C7gLumbdPxOW5Itu3A+Bzut9cBry7XLwL2DXO/jTjfXO+7nwfmleuLgIO0zt0O5fU6qnzl9h5m8Jo9Kd850PqTGxvL9Y3AtR3mXAlszcxDmXkY2AqsjIhFwC9k5r9na4/dc5ztZ+L//yxIZv4PMPVnQY6X+R+By8tPZdcAk5n548z8LrC7rNfLmnOVbZj6zpeZz2XmQ8B/t08e4nM89GxDNEi2b2bm1O8X7QJOKz+JDvO1MfR8feYYdrbnM/NYGT8NmDppO6zX66jyzdjJWg5jmbkfoHzt9Nay05/qWFwuezuMT7k1Ih6PiA1xnMNVM3isjnPKk3cEeGWXnN3WnKts0PpH95Xytn8N/Rsk34nWPNFzPJfZpvxteXv//j4P3Qwr21uAb2bmjxnefhtVvilzuu8i4tKI2AXsBN5V7h/W63VU+WCGr9nGlkNE/EtEPNHh0msbd/pHkycYB7gb+EXgYmA/cOeAjzVInl7W7MUosgG8ITNfD1wF3BIRv9lHtkHzDbJmL0aRDeCGzFwG/Ea53DgX2SLiQuCjwDtnsGavRpEPGrDvMvNrmXkh8KvAeyPitB7XnMt8MMPXbGPLITN/KzMv6nC5DzhQ3gJPHUI42GGJ4/2pjr3l+vRxMvNAZv4kM/8X+BS9H0Lp+mdB2udExDzgTOBQl5zd1pyrbEy97c/Mg8CX6P9w0yD5TrRmx+e4AdnIzH3l64+Az9HfvhsoW0ScS+t5uykzv9M2fxj7bVT5GrHv2rI8CTxH67zIsF6vo8o389dsPydM5voC/CkvPSH9sQ5zzga+S+tk9IJy/exy3yPAZbx40u3qMr6obfs/oHW8vZc882id8D6fF08gXThtzi289ATSveX6hbz0pO8ztE5IdV1zDrOdAbyizDkD+DdgZZ/PZd/52u5fTX3St+NzPNfZypoLy/WX0Tpe/K5Zfl7PKvPf0mHdgffbqPI1ZN+dz4sneF9D6z/thb2sOcf5ZvyanXHwJlxoHVvbBjxdvk79pz8O/E3bvLfROom6G7i5bXwceILWJwLu4sVfBvw7WsfpHqf1N58WzSDT1cB/lDXfV8Y+APxOuX4a8A8ly9eB17Zt+76y3VO0fTqk05p97q+hZqP1KYpvlcuuQbINId8eWj8xHaX109QFJ3qO5zpbeWHuKP/GdgEfp3wCbLayAX9C6yfKx9ou5wxzv40iX0P23Y3lsR8DvgFcO+zX6yjy0cdr1t+QliRVGnvOQZI0dywHSVLFcpAkVSwHSVLFcpAkVSwHSVLFcpAkVSwHSVLl/wDzmNYM2kXJWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ff81b38>"
      ]
     },
     "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 0x1a21a31080>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3093d978>"
      ]
     },
     "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 0x1a29351668>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20e73470>"
      ]
     },
     "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 0x1a2e354fd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21bd35c0>"
      ]
     },
     "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 0x1a21a5c6a0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a286e1780>"
      ]
     },
     "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 0x1a28f34860>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a286fd3c8>"
      ]
     },
     "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 0x1a2ab4fd68>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D4HjzfvLdAZIO5zH26eVA9TF6gDemDmP5zgz+kZDE1ed25faLe9M8PJRubZpjjCGvuIyzHlpY7Xt6t4/mvDPaMGOiY/jnvekXsGHvUQZ3rXvMvkOrZgyLa8uirYf54/h+bHUO7yzYfIiJznmKJ7/cwezlKbRvGcnUEY4AmDlvK29NG4Yxhrh2Uew5UsC76/ZxYe92XDmkC6uSM/lwQxrLdmaQmef43Tx85SDaREcwaUhnzp65yBW83dtEuQKqtrPVF39+Hk8v2skrziG4DxLTuHlEHI/+b5trW6WLz2zPG1PPJzk9j/EDO7nO3k9U19bNiYoIpaCk3BHQZ3Wu1vivmjGWDi2bMW1kT2Yt2EF2QSmJe7KqNVTt6znDDAsNcZ21X9i7HbeO6klhaTkrkzOZ/vYG136Ltv3AlHO6up5n5BVXa/zfmHo+t7y+nicWOM6M03OL+N+mQ66vv71mL80jQrm3xpDgq9/s5s3Ve8kvKefv1w2p9rU/friJxdsO89DkgTz8+Ta3sq/bk8XgmYtYd9+4OnvDnqTnFtGhZTM++S6NgpJyfj78jGpfr1w6/dwNQ5k0uDPXxXcnPNQw/a0N/GH8mYwf1IktB3KY9C9HL2FQl/qHc0JCDG9NG86Nr67l4SsHcU731idV7hMVFHMAVc8ujhWVsT+rgIc/30abqHBaR1VvnDu1auYa1wMY9eRS7v1kM3Xp26HFSZera+vmDO3Rmh+ca8pzi8qwtvqE0Rur9gDQrU1zRvRuR4eWkeQWlTHj4818uCHNdWY5dWQcv76kD3nFZTz2xfZqyxOjq5yhjehdvStem1lXD+a3Y3ozqk8sAPFntKn29VtGxHFO99b837g+rPjTGP5+7RDO7NiS7m2jXL/rFpFhvHxTPLEtIrjOOSRyflxbnrp2iKsxbdksnEv6dfDYZR8/qCO70vNcQ10Av/7vt3zx/SHmbTrIS8scvYyi0nI6xTTjznF9+XpXJmt3Z7FudxZ7jhTQKzaalpFhzPjoe5LTc0lOd5xpVTb+AG2c5YqKCGPbIxP40+WOs/7LBroPc1TVPCKU+ycNZNdjExnaozUPzdvKqpTMao1/i8gwvnvgMsDRQ/zlRb1OuvGvtHnm5bRvGcnH3x7gTx9UX3nWsZWj4fvZ8B60auYI96mvr3etiJsxsT/jB1U/C61PSIghOjKM8YM6seOvE3j6uiF0bd2cDxIdDeHeI/lk5ZeQmuGYY/vrlEF8/ecxXNKvA/837vhE9MffHqDEuWJmzi/Oc/y/IpXMvGIKS8r5aEMaOYWlrhOWDzekuc3JVT6v2fh//JsR3FLlxOvZhLpX8dVnZXImwx77ioRth/n9+5u475MtbrdtycovoXl4KFcO6UJIiKFPhxac0S6ahb8f7fq9ntU1ht+O6c3/je3ToN5Ip5hmJNx9MSOdf3engvHXtdYA8fHx1hefCTzssQTSnW+oq4Z2Ze+RfL7dl40xcM/E/tXG7bq3be4686vpZ8N78M7afcS1i+KygR25pF8Hr1+sF5Ym8zfncsyaurZu7hqDfvKawfz0/B5s2p/NlBdWVtuvR9soVvx5DAAzPvreNXbbt0MLfnp+d/6xOIn8knJmTOzP7bUM/9Qlv7iMxdsOM+WcLvS8Z75r+wOTBnLrqJ4nVM+EbYcZ1qstrU6iW34gu5CRTyxxPW8eHlotpAE6tIzkuRuGckGvdhSUOHofFRbX0NjPhvdg6og4Ln92BeMHdiLpcC45haUM7dGGhO2Huf787jxxzdluP7u8wrrNN3iq5y/fOv6eHdazLXN/deEJ17mhnvpyBy8uOz7MduMFPQB49MeDq+339uo9PPCZY/L/95eeWefqoBPxbEISzybsIrZFpCtIrx7alf9tPsTae8a5AhUgcU8WLy1L4SvnCcs/rz+HKed0JTk9l0ufcQxJRUeEkl9SzkV9Y9l28BiDu8WwKvkIP7+gBw9NHgQ47ps0/PGviAgNcQVJpaRHJxIRFkJOQSl//HATS3ekM++OUcxensL9kwY0qDewOuUIN7y8BoCL+sa67hJwSb/2vH7L+Rhj2Lg/mx+/sJIQA6mzfuTlb7FxGGM2WGvjPe4XDAEw/PEE1/K2qmJbRJJ4/6Xsycznkr8vc/zMM9pUO9Os9NhVZ7l1A30hI7eYGR99z2UDO7J2dxZXDuniWi+8e9YVlJRXsO9IAT1jo10TsrUNr+x5wvFGrHxz1vTbMb35U5UVJCdqTeoRUjPyaR0VziX92p/ypX0jn1jiCsOv/zyGhO2Hq50BVta/0j8Tdrl6UrEtIvjmL2NpFh7KrPnbXcMgg7vGMOvqwUz61zfcMaYPf7y8H76wfk+WayXH6nvG0jmmuU+OW5u0owWMetIxnt6jbRQL7xpdbUy+UklZBXe88y3r92Txxf9dRJfW3pfpYHYho55cQs0FVgM6t2LBnRfV+j3vr9/Hwewi7hzX1zWc9vNX1rAy2X2l0F2X9mXHoVxWpx7h49+MoHf7FixPyuDm19bx1rRh7D2Sz+QhXXj561QOZRfxzE/PcX3vd/uOuq2SWnPPOH46ZzWDu8YQGRZKdkEJw3u15dIBHXk2YRd7j+SzKc394tM2UeEcdS7NvWVEHEt2pLMvq4Crz+3KMz85x21/f6AAqOLCWV9VG5es1KdDCxLuvpjCknIGPPglAOvuHccFs75i/MBOfLnVsdrlg9svdFt505ieXrSTmObh/PKiXnXuk5yexxMLtpOwPZ2Y5uFsemi862u7DudSXFZBz9ho7vtkM8kZebx68/muYYHTUXZBCS8uS2FA55ZcNdQxpLQ5LYfF237gzE4tmXR2l2r7l5VX8MziJOf3HG+QdmfmM8YZ9ot+P5ozO7Zkc1oOfTu2cFsl443b3kqktLyCN6Y27BoGb6xOOULP2Gg6xZz61/dYUSlPL9zJwC6tWJVyhM82HmR4z7a8fwK9nuKyckY9udQ17FPp4SsH0a9TS66fs4bWUeF8/OsRrEzO5IHPtrJyxli61hNiFRWWXvfOr7Zt8pAurgUBnnz06wu55iVHiH9w+4WuQK/UslkY3z80/pStODpRCoAqqp49VvXlXRfRv1Mrysor6HPfAuD4meSCzYdcS7I2zxzfqCsKvJGakUdURFiT/PH7u5zCUq7459dMG9Wz2pDViQ7rnCxrrd82EI1hVUomP3t5LVcM7sSLPz/vhL63pKyC7MISoiLCXL3bymGiV75OrbYIwRjY9ehEtyXKNe3PKuCFpcl0jmle6wKOmiqHlVpEhrF55njXsGdlm/DyilTXsufKk0d/1dAA8MtVQMaYycDkPn36+OR4EWG1v1H6d3LMzNfWGEQ5V8X8dkxvv238AXq1P/lJ6EAX0zycb/4yxq0RPhWNPxBUjT/AiN6xvHxTvNsFbQ0RERbiGqOfProXc1akuhYL3HRhHE99udM15m8tHht/gO5to1zzOlsP5rBo22G6tm7OyhljXfu8tXoPD362lfEDOzLnpngOZhcSGmIwxrDgzouq3arlttG9mHBWJy56aimTzq77SvnTSVD0AMb+fRmptdzsrOq4cdyML6ptq6iwrNiVwei+7b2+UEVEGq68wrIqJZORvWNdf3vWWlIz8xn39HJG9YnlP78cfkLH3J9VwEVPLaVX+2iW/OGSal8rKi0nLMQ0KFQq948MC/HrgD+tewC+VnnmMG1kT3rGRrlWQ9QnJMRwiYcrdEXE90JDjNs9cIwx9G7fgo0PXlZnj74+3dtG8cLPzqVvR/ce84nO/fhyrqipBXwArEk9QtrRQn4S340HJw/UPc9FTmO13XywoX4UIMM2vhTwF4JdP8expreyexcW6r/dNhGRUyngA6BS5T2AwkKCpsoiIvUK6Naw6odvHCt0PFYPQETEIaADYE+Ve/+f1dWx5DNMK3pERIAgmAQG+NPl/Vz3wGnoUi8RkUAX0AFQeYlDnw4tXBf/hNfRA/jPrcPd7gwqIhLIAjoAKlVt8uu6CnRU31N3C1YREX8QdOMhGgISEXEIitaw6iXbmgQWEXEI6ACo7TZHWgYqIuIQ0AFQqWqTH+68EOy8Gh9zKCISbAJ6Etji3gUICTHMu2MkcbHRTVAiERH/EdABUKnmXVvP7ta6aQoiIuJHAnoIyI8/6kBEpMmd0gAwxvzYGPOyMeYzY8x4z9/hq597qn6SiMjpo8EBYIx5zRiTbozZUmP7BGPMTmNMsjFmRn3HsNZ+aq29DbgF+OlJlfgEqAMgIlK3E5kDeAN4HnircoMxJhR4AbgMSAPWG2PmAaHArBrfP81am+58fL/z+04Jg7oAIiI1NTgArLUrjDFxNTYPA5KttakAxpj3gCnW2lnApJrHMI4rsp4AFlhrvz3ZQjeUP3/esYhIU/N2DqArsL/K8zTntrr8DrgUuNYYc3ttOxhjphtjEo0xiRkZGV4Wr/KgvjmMiEgg8XYZaG1Na52n3dba54Dn6jugtXYOMAcgPj5ep/AiIo3E2x5AGtC9yvNuwEEvj+kzSg8Rkbp5GwDrgb7GmJ7GmAjgemCet4Uyxkw2xszJycnx9lCO4/nkKCIigeVEloG+C6wG+hlj0owxt1pry4A7gIXAdmCutXart4Wy1n5urZ0eExPj5XG8LYmISOA6kVVAN9SxfT4w32clagRGV4KJiLgJ6FtBaBZARKRufhkAmgMQEWl8fhkAmgMQEWl8fhkAvrL90DFAN4MTEalNwAbA/qwCHvjM6wVJIiIByy8DwBdzANkFpcePp1kAERE3fhkAvpgDqO3jIEVE5Di/DABf0xyAiIi7gPxM4Ke+3MHbq/c2dTFERPxaQPYA0nOLyS0ucz1XB0BExJ1fBoC3k8Bq8EVEPPPLAPB2Elhj/iIinvllAHgrpGYCKBBERNwEZACoByAi4llABkDNU35dCCYi4i4gAyBE7b2IiEd+GQBerwKqOQWgQBARceOXAeD1KiAN+YiIeOSXAeCtmkNAigMREXcBGQD6DGAREc8CMgBqUiCIiLgLyABwuxBMRETcBGQAaBWQiIhnfhkAuhmciEjj88sA8PXN4BQIIiLu/DIAvKU5ABERzwIyAGqe8isPRETcBWQA6EpgERHPAjIAdDM4ERHPAjIA3Id8lAgiIjUFZgCowRcR8SggA8DtZnDKAxERN34ZAN5eCKYWX0TEM78MAO8/D6D+5yIi4qcB4C11AEREPAvIAKh5JbBuBy0i4i4gA0DNvYiIZ4EZALoZnIiIRwEaAGryRUQ8CdAAqP+5iIgEagBo0EdExKOADAC3K4EVCCIibgIyADTkIyLiWWAGADWvA2iigoiI+DG/DACvPxReDb6IiEd+GQDefyi8EkBExBO/DABvqfkXEfEsMANACSAi4lFABoD7zeCaqCAiIn4sIANADb6IiGeBGQBuz5UIIiI1BWYAqAsgIuJRgAZA/c9FRCRQA0BDPiIiHgVmAKgHICLiUUAGQM27gYqIiLuADAC3m8FpSEhExE1ABoDaexERzwIyAHQlsIiIZwEZAGrvRUQ8C8wAUAKIiHgUkAHgNgTUROUQEfFnpywAjDEDjDGzjTEfGmN+3bg/qzGPLiISGBoUAMaY14wx6caYLTW2TzDG7DTGJBtjZtR3DGvtdmvt7cBPgPiTL/KJUyCIiLhraA/gDWBC1Q3GmFDgBWAiMBC4wRgz0Bgz2Bjzvxr/Oji/50rgG+Arn9WgFjWHgERExF1YQ3ay1q4wxsTV2DwMSLbWpgIYY94DplhrZwGT6jjOPGCeMeYL4J2TLbQn7u2/AkFEpKYGBUAdugL7qzxPA4bXtbMx5hLgaiASmF/PftOB6QA9evQ4qYLpyl8REc+8CYDaWllb187W2mXAMk8HtdbOAeYAxMfH13m8egumm8GJiHjkzSqgNKB7lefdgIPeFcc3dDM4ERHPvAmA9UBfY0xPY0wEcD0wzxeFMsZMNsbMycnJOanvb9ksvPrxfFEoEZEA09BloO8Cq4F+xpg0Y8yt1toy4A5gIbAdmGut3eqLQllrP7fWTo+JiTmp749tEemLYoiIBLSGrgK6oY7t86lnQreptGsRUe25PiNYRMRdQN4KIqZ5uOedRESCnF8GgLdzALoXkIiIZ34ZAN7OAajBFxHxzC8DwFu6DkA/7n/MAAAI5klEQVRExLMADQC1+CIinvhlAHg7ByAiIp75ZQB4OwdQk+4NJCLizi8DQEREGl9QBICmBERE3AVFAIiIiDu/DABNAouIND6/DABfTwKLiIg7vwwAX9McgIiIu6AIABERcRcUAaArg0VE3AVFAIiIiDu/DABfrwLS+b+IiDu/DACtAhIRaXx+GQC+pikAERF3QREAIiLiLigCQHcDFRFxFxQBICIi7hQAIiJByi8DwOfLQDUCJCLixi8DQMtARUQan18GgK+pAyAi4i4oAkBERNwFRwCoCyAi4iY4AkBERNwERQDoQjAREXdBEQAiIuIuKAJA1wGIiLjzywDw9YVgIiLizi8DwNcXgqkDICLizi8DQEREGl9QBIA+FF5ExF1QBICIiLhTAIiIBKmgCAANAImIuAuKABAREXdBEQCaAxYRcRcUASAiIu6CIgB0MzgREXdBEQAiIuLOLwPA5/cCUgdARMSNXwaAPhReRKTx+WUA+JpWAYmIuAuKABAREXdBEQDqAIiIuAuKABAREXdBEQC6HbSIiLugCAAREXEXFAGg838REXdBEQAiIuJOASAiEqSCIgA0Bywi4i4oAkBERNwFRQDodtAiIu6CIgBERMRdUASA5gBERNwFRQCIiIg7BYCISJA6pQFgjIk2xmwwxkw6lT9XRETcNSgAjDGvGWPSjTFbamyfYIzZaYxJNsbMaMCh/gLMPZmCekNzACIi7sIauN8bwPPAW5UbjDGhwAvAZUAasN4YMw8IBWbV+P5pwNnANqCZd0UWERFfaFAAWGtXGGPiamweBiRba1MBjDHvAVOstbMAtyEeY8wYIBoYCBQaY+Zbayu8KHuD6ToAERF3De0B1KYrsL/K8zRgeF07W2vvAzDG3AJk1tX4G2OmA9MBevTo4UXxRESkPt5MAtd2Wm09fZO19g1r7f/q+foca228tTa+ffv2XhTvOM0BiIi48yYA0oDuVZ53Aw56VxwRETlVvAmA9UBfY0xPY0wEcD0wzxeFMsZMNsbMycnJ8cXhRESkFg1dBvousBroZ4xJM8bcaq0tA+4AFgLbgbnW2q2+KJS19nNr7fSYmBhfHE5TwCIitWjoKqAb6tg+H5jv0xKJiMgpoVtBiIgEKb8MAF/PAYRoGZCIiBu/DABfzgHseeJHhIQoAEREavLLABARkcanABARCVJ+GQC6DkBEpPH5ZQD4+joAERFx55cBICIijU8BICISpBQAIiJByi8DQJPAIiKNz5sPhGk01trPgc/j4+NvO9ljvDH1fPKLy31YKhGRwOKXAeALl/Tr0NRFEBHxa345BCQiIo1PASAiEqQUACIiQcovA0CrgEREGp9fBoBuBSEi0vj8MgBERKTxKQBERIKUAkBEJEgZa21Tl6FOxpgMYO9JfnsskOnD4jSlQKlLoNQDVBd/FSh18bYeZ1hr23vaya8DwBvGmERrbXxTl8MXAqUugVIPUF38VaDU5VTVQ0NAIiJBSgEgIhKkAjkA5jR1AXwoUOoSKPUA1cVfBUpdTkk9AnYOQERE6hfIPQAREalHwAWAMWaCMWanMSbZGDOjqcvTEMaYPcaYzcaYjcaYROe2tsaYxcaYXc7/2zi3G2PMc876fW+MObeJy/6aMSbdGLOlyrYTLrsx5mbn/ruMMTf7UV1mGmMOOF+bjcaYK6p87R5nXXYaYy6vsr1J34PGmO7GmKXGmO3GmK3GmDud20+716WeupyOr0szY8w6Y8wmZ10edm7vaYxZ6/wdv2+MiXBuj3Q+T3Z+Pc5THU+YtTZg/gGhQArQC4gANgEDm7pcDSj3HiC2xrangBnOxzOAJ52PrwAWAAa4AFjbxGUfDZwLbDnZsgNtgVTn/22cj9v4SV1mAn+sZd+BzvdXJNDT+b4L9Yf3INAZONf5uCWQ5Czvafe61FOX0/F1MUAL5+NwYK3z9z0XuN65fTbwa+fj3wCznY+vB96vr44nU6ZA6wEMA5KttanW2hLgPWBKE5fpZE0B3nQ+fhP4cZXtb1mHNUBrY0znpigggLV2BZBVY/OJlv1yYLG1NstaexRYDExo/NJXV0dd6jIFeM9aW2yt3Q0k43j/Nfl70Fp7yFr7rfNxLrAd6Mpp+LrUU5e6+PPrYq21ec6n4c5/FhgLfOjcXvN1qXy9PgTGGWMMddfxhAVaAHQF9ld5nkb9bxZ/YYFFxpgNxpjpzm0drbWHwPFHAFR+xuXpUMcTLbu/1+kO59DIa5XDJpwmdXEOGwzFcbZ5Wr8uNeoCp+HrYowJNcZsBNJxBGoKkG2tLaulXK4yO7+eA7TDh3UJtAAwtWw7HZY5jbTWngtMBH5rjBldz76nax2h7rL7c51eAnoD5wCHgKed2/2+LsaYFsBHwF3W2mP17VrLNn+vy2n5ulhry6215wDdcJy1D6htN+f/jV6XQAuANKB7lefdgINNVJYGs9YedP6fDnyC441xuHJox/l/unP306GOJ1p2v62Ttfaw84+2AniZ411tv66LMSYcR4P5X2vtx87Np+XrUltdTtfXpZK1NhtYhmMOoLUxJqyWcrnK7Px6DI4hSp/VJdACYD3Q1zmrHoFj4mReE5epXsaYaGNMy8rHwHhgC45yV666uBn4zPl4HnCTc+XGBUBOZbfej5xo2RcC440xbZxd+fHObU2uxvzKVTheG3DU5XrnSo2eQF9gHX7wHnSOE78KbLfWPlPlS6fd61JXXU7T16W9Maa183Fz4FIccxpLgWudu9V8XSpfr2uBJdYxC1xXHU/cqZwFPxX/cKxoSMIxtnZfU5enAeXthWNGfxOwtbLMOMb6vgJ2Of9va4+vJHjBWb/NQHwTl/9dHF3wUhxnJreeTNmBaTgms5KBqX5Ul7edZf3e+YfXucr+9znrshOY6C/vQWAUjiGB74GNzn9XnI6vSz11OR1fl7OB75xl3gI86NzeC0cDngx8AEQ6tzdzPk92fr2Xpzqe6D9dCSwiEqQCbQhIREQaSAEgIhKkFAAiIkFKASAiEqQUACIiQUoBICISpBQAIiJBSgEgIhKk/h+iXkr5NqUiMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2a770400>"
      ]
     },
     "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 0x1a2c0fa0f0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2b81c940>"
      ]
     },
     "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 0x1a2c8e57b8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2c8e5208>"
      ]
     },
     "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>0.133161</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.547377e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.123043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.029627e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.138111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.693909e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.119747</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.151929e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.119051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.038364e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.182353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.272473e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.169431</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.734154e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.115476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.782901e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.132960</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.251904e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.136776</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.456796e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.107701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.644793e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">9</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.127865</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.919352e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.134434</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.504939e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.120321</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.326944e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.127692</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.919054e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.129883</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.922429e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">13</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.133065</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.354270e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.127178</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.984934e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.131496</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.785140e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.125028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.338761e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.119402</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.418437e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.128352</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.949967e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.124479</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.133448e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.124845</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.184748e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.132559</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.543401e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.119432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.522625e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.117668</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.986236e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">23</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.132165</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.975753e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.136230</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.764287e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.125968</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.472890e+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>76</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.141746</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.405609e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.122631</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.295744e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.120858</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.233323e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">79</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.132721</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.214154e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.139691</td>\n",
       "      <td>119.0</td>\n",
       "      <td>5.443000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.124794</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.404819e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.141008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.401991e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.129557</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.119848e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.119729</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.373777e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.122472</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.180054e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">85</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.132596</td>\n",
       "      <td>33.0</td>\n",
       "      <td>4.216417e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.136660</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1.002178e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.127334</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.700426e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">87</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.128471</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.821480e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.132346</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.834164e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.128923</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.047239e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">89</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.135554</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.481465e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.134314</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.027454e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">90</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.107531</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.368720e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.155436</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.018007e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.116157</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.735852e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.131763</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.135065e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.173536</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.687296e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.132353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.478165e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.128866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.338835e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.132912</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.444795e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.135208</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.584690e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <th>burn</th>\n",
       "      <td>0.125645</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.069589e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">99</th>\n",
       "      <th>bond</th>\n",
       "      <td>0.125424</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.895439e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>0.124833</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.594568e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                pbar                  amt\n",
       "              median  count        median\n",
       "agent mech                               \n",
       "0     bond  0.133161    1.0  1.547377e+02\n",
       "1     bond  0.123043    1.0  5.029627e+02\n",
       "2     bond  0.138111    1.0  1.693909e+03\n",
       "3     burn  0.119747    1.0  6.151929e+03\n",
       "4     bond  0.119051    1.0  2.038364e+03\n",
       "      burn  0.182353    1.0  6.272473e+03\n",
       "5     burn  0.169431    1.0  7.734154e+03\n",
       "6     burn  0.115476    1.0  6.782901e+03\n",
       "7     bond  0.132960    2.0  6.251904e+02\n",
       "      burn  0.136776   10.0  2.456796e-09\n",
       "8     burn  0.107701    1.0  9.644793e+03\n",
       "9     bond  0.127865    4.0  5.919352e+02\n",
       "      burn  0.134434    4.0  3.504939e+03\n",
       "10    burn  0.120321    1.0  5.326944e+03\n",
       "11    bond  0.127692    1.0  3.919054e+02\n",
       "12    burn  0.129883    1.0  5.922429e+03\n",
       "13    bond  0.133065    1.0  1.354270e+03\n",
       "      burn  0.127178    1.0  6.984934e+03\n",
       "14    bond  0.131496    1.0  2.785140e+02\n",
       "15    burn  0.125028    1.0  9.338761e+03\n",
       "16    burn  0.119402    1.0  6.418437e+03\n",
       "17    burn  0.128352    1.0  5.949967e+03\n",
       "18    burn  0.124479    1.0  7.133448e+03\n",
       "19    burn  0.124845    2.0  6.184748e+03\n",
       "20    bond  0.132559    1.0  4.543401e+02\n",
       "21    bond  0.119432    1.0  1.522625e+03\n",
       "22    bond  0.117668    1.0  4.986236e+03\n",
       "23    bond  0.132165    1.0  1.975753e+03\n",
       "      burn  0.136230    1.0  9.764287e+03\n",
       "24    burn  0.125968    1.0  5.472890e+03\n",
       "...              ...    ...           ...\n",
       "76    burn  0.141746    1.0  1.405609e+04\n",
       "77    burn  0.122631    1.0  9.295744e+03\n",
       "78    burn  0.120858    1.0  1.233323e+04\n",
       "79    bond  0.132721   23.0  3.214154e-10\n",
       "      burn  0.139691  119.0  5.443000e-10\n",
       "80    burn  0.124794    1.0  1.404819e+04\n",
       "81    bond  0.141008    1.0  1.401991e+03\n",
       "82    burn  0.129557    1.0  6.119848e+03\n",
       "83    burn  0.119729    1.0  1.373777e+04\n",
       "84    burn  0.122472    1.0  2.180054e+04\n",
       "85    bond  0.132596   33.0  4.216417e-10\n",
       "      burn  0.136660  142.0  1.002178e-09\n",
       "86    burn  0.127334    1.0  5.700426e+03\n",
       "87    bond  0.128471    1.0  9.821480e+02\n",
       "      burn  0.132346    1.0  7.834164e-10\n",
       "88    bond  0.128923    1.0  1.047239e+03\n",
       "89    bond  0.135554   17.0  2.481465e-09\n",
       "      burn  0.134314  300.0  1.027454e-09\n",
       "90    bond  0.107531    1.0  7.368720e+02\n",
       "      burn  0.155436    2.0  8.018007e+03\n",
       "91    burn  0.116157    1.0  1.735852e+04\n",
       "92    bond  0.131763    1.0  5.135065e+02\n",
       "93    burn  0.173536    1.0  7.687296e+03\n",
       "94    bond  0.132353    1.0  7.478165e+02\n",
       "95    bond  0.128866    1.0  6.338835e+02\n",
       "96    bond  0.132912    1.0  5.444795e+02\n",
       "97    bond  0.135208    1.0  1.584690e+02\n",
       "98    burn  0.125645    1.0  6.069589e+03\n",
       "99    bond  0.125424    3.0  3.895439e+02\n",
       "      burn  0.124833    7.0  2.594568e+03\n",
       "\n",
       "[120 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 0x1a2dfafeb8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2dfaf828>"
      ]
     },
     "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 0x1a30999550>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2e0f5c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdf['P'].plot(logx=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a27405ac8>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a273448d0>"
      ]
     },
     "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 0x1a2d82e278>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2a770c88>"
      ]
     },
     "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 0x1a2e1814a8>"
      ]
     },
     "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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x8yzs9z61z8J+/wngn5oajwG/3299wJub54vN69esd5um4eE3YyWpuFnuo5ckDcCgl6TiDHpJKs6gl6TiDHpJKs6gl6TiDHpJKs6gl6Ti/h8Hcd6scGtEggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20907b00>"
      ]
     },
     "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 0x1a21c6fac8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21377518>"
      ]
     },
     "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([43.,  9., 13., 13.,  6.,  7.,  6.,  1.,  1.,  1.]),\n",
       " array([   0.        ,  598.84651177, 1197.69302353, 1796.5395353 ,\n",
       "        2395.38604706, 2994.23255883, 3593.07907059, 4191.92558236,\n",
       "        4790.77209412, 5389.61860589, 5988.46511766]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21c537b8>"
      ]
     },
     "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 0x1a21e68d30>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21ed3518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.gini_h.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([57., 22., 10.,  4.,  4.,  2.,  0.,  0.,  0.,  1.]),\n",
       " array([    0.        ,  7706.19515479, 15412.39030957, 23118.58546436,\n",
       "        30824.78061915, 38530.97577393, 46237.17092872, 53943.3660835 ,\n",
       "        61649.56123829, 69355.75639308, 77061.95154786]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IFcBfAXdPGW9LvhFgUZk+Bfg2cD3NEVfnBapPl+lbOfoC1UNl+sMcfYFqH83Fqb7sB8DP8c5F21ZkoznyO71j+p+Ba1r03n4bOL9M/37J1YpsHRkfAH6lTX8vaK5h7aS5phU0F79/c5j73VDLfBZv5nU0n0p5GfjcALdzP825th/S/PZcQ3MObSuwpzwe2QmC5j+AeRl4ERjreJ1fBfaWn86dcAzYUdb5U6ZcCJsh28/S/HPtBeC58nNdi/L9JPBsybcD+L0yfh7Npxz2lh39pDJ+cpnfW54/r+O1Plcy7KbjExH92A84uvBbka3keL787Dyyfove24uA8fLe/i1NIbYiW1n/A8B3gQ91jLUiH3An8FJZ/69pSnto+5132kpSJY6Hc/iSpD6w8CWpEha+JFXCwpekSlj4klQJC1+SKmHhS1IlLHxJqsT/A0KLBzxgGCHiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2214fb38>"
      ]
     },
     "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 0x1a21ed3ac8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a26240518>"
      ]
     },
     "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 0x1a265e4cc0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a263cce10>"
      ]
     },
     "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([32., 33., 17., 11.,  3.,  3.,  0.,  0.,  0.,  1.]),\n",
       " array([  719.94368949,  1667.02664053,  2614.10959157,  3561.19254262,\n",
       "         4508.27549366,  5455.3584447 ,  6402.44139575,  7349.52434679,\n",
       "         8296.60729784,  9243.69024888, 10190.77319992]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b5hAT+lX/FK33dmC+fQ/+kcEVC5vmOwC8C/gacAD4ewZXqnS9/4FbGfxm8EMGR9Q3THKfA3Ntez4K/DVLfnRf6+CdopLUCU+5SFInDHRJ6oSBLkmdMNAlqRMGuiR1wkCXpE4Y6JLUCQNdkjrx/yeIjTlWAcXAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2660d668>"
      ]
     },
     "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 0x1a26677588>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a268785c0>"
      ]
     },
     "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([ 7., 17., 29., 26., 13.,  7.,  1.]),\n",
       "  array([ 7., 17., 28., 26., 14.,  7.,  1.]),\n",
       "  array([ 7., 17., 28., 27., 13.,  7.,  1.]),\n",
       "  array([ 7., 17., 29., 26., 13.,  7.,  1.]),\n",
       "  array([ 7., 17., 28., 26., 14.,  7.,  1.]),\n",
       "  array([ 7., 17., 28., 26., 14.,  7.,  1.]),\n",
       "  array([ 7., 16., 26., 27., 16.,  7.,  1.])],\n",
       " array([-0.62734788, -0.43510644, -0.242865  , -0.05062356,  0.14161788,\n",
       "         0.33385931,  0.52610075,  0.71834219]),\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 0x1a272178d0>"
      ]
     },
     "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
}