2196 lines
1.3 MiB
2196 lines
1.3 MiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Three Sided Market Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/jpeg": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from IPython.display import Image\n",
|
|
"Image(filename='threesidedmarket.jpeg')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from decimal import Decimal\n",
|
|
"from datetime import timedelta\n",
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"%matplotlib inline\n",
|
|
"import pandas as pd\n",
|
|
"from tabulate import tabulate\n",
|
|
"\n",
|
|
"from __future__ import print_function\n",
|
|
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
|
|
"import ipywidgets as widgets\n",
|
|
"from IPython.display import clear_output\n",
|
|
"\n",
|
|
"from SimCAD.configuration import Configuration\n",
|
|
"from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \\\n",
|
|
" ep_time_step\n",
|
|
"from SimCAD.engine import ExecutionMode, ExecutionContext, Executor"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#map for of differential game model configuration"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/jpeg": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from IPython.display import Image\n",
|
|
"Image(filename='3SM-mechsteps.jpeg')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Define the experiment\n",
|
|
"sim_config = {\n",
|
|
" 'N': 1, #number of repeated experiments\n",
|
|
" 'T': range(104) #number of discrete iterations in each experiement\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#define the time deltas for the discrete increments in the model\n",
|
|
"ts_format = '%Y-%m-%d'\n",
|
|
"#\n",
|
|
"t_delta = timedelta(days=7)\n",
|
|
"def time_model(step, sL, s, _input):\n",
|
|
" y = 'timestamp'\n",
|
|
" x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"exogenous_states = exo_update_per_ts(\n",
|
|
" {\n",
|
|
" 'timestamp': time_model\n",
|
|
" }\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#define generator processes that characterize the world outside the system\n",
|
|
"#in future versions ingest data and fit stochastic process models\n",
|
|
"#then draw different realizations of those stochastic process models for Monte Carlo Sims"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"eta = .075\n",
|
|
"def tx_volume_generator(step, sL, s, _input):\n",
|
|
" y = 'tx_volume'\n",
|
|
" x = s['tx_volume']*(1+eta*np.random.rand())\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"alpha = .9\n",
|
|
"beta = 1.0\n",
|
|
"def cost_of_production_generator(step, sL, s, _input):\n",
|
|
" y = 'cost_of_production'\n",
|
|
" x = alpha*s['cost_of_production']+beta*np.random.rand()\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#log quadratic overhead model; parameters\n",
|
|
"flat = 500\n",
|
|
"a = 1000.0\n",
|
|
"b = 100.0\n",
|
|
"c = 1.0\n",
|
|
"d = 1.0\n",
|
|
"def overhead_cost_generator(step, sL, s, _input):\n",
|
|
" #unit fiat\n",
|
|
" y = 'overhead_cost'\n",
|
|
" q = a+b*s['tx_volume']+c*s['volume_of_production']+d*s['tx_volume']*s['volume_of_production']\n",
|
|
" x = flat+np.log(q)\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#State Variables ~ declare initial conditions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"seed = {}\n",
|
|
"env_processes = {}\n",
|
|
"initial_condition = {\n",
|
|
" 'fiat_reserve': float(25000),#unit: fiat\n",
|
|
" 'overhead_cost': float(500), #unit: fiat\n",
|
|
" 'operational_budget': float(25000), #unit: fiat\n",
|
|
" 'token_reserve': float(25000),#unit: tok\n",
|
|
" 'token_supply': float(25000),#unit: tok\n",
|
|
" 'tx_volume': float(1000), #unit: fiat\n",
|
|
" #'txo_fiat': float(1000), #unit: fiat\n",
|
|
" #'txo_token': float(1000), #unit: tok\n",
|
|
" #'txi_fiat': float(1000), #unit: fiat\n",
|
|
" #'txi_token': float(1000), #unit: tok\n",
|
|
" 'conversion_rate': float(1), #unit: tok/fiat\n",
|
|
" 'cost_of_production': float(10), #unit: fiat/labor\n",
|
|
" 'volume_of_production': float(20), #unit: labor\n",
|
|
" 'producer_roi_estimate': float(1.1), #unitless //signal for informing policies\n",
|
|
" 'smooth_avg_fiat_reserve': float(25000), #unit: fiat //signal for informing policies\n",
|
|
" 'smooth_avg_token_reserve': float(25000), #unit: token //signal for informing policies\n",
|
|
" 'timestamp': '2019-01-01'\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#Behavoirs (two types: controlled and uncontrolled)\n",
|
|
"#user behavoir is uncontrolled (estimated or modeled),\n",
|
|
"#governance policies are controlled (enforced)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#governance decision ~ system policy for token/fiat unit of value conversion\n",
|
|
"#static policy\n",
|
|
"conversion_rate_gain=1#\n",
|
|
"\n",
|
|
"def conversion_policy(step, sL, s):\n",
|
|
" ncr = conversion_rate_gain*s['smooth_avg_token_reserve']/s['smooth_avg_fiat_reserve']\n",
|
|
" return {'new_conversion_rate': ncr}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#governance decision ~ determines the conditions or schedule of new tokens minted\n",
|
|
"\n",
|
|
"final_supply = 1000000.0 #1M #unit: tokens\n",
|
|
"release_rate = .01 #percent of remaining\n",
|
|
"\n",
|
|
"def minting_policy(step, sL, s):\n",
|
|
" mint = (final_supply-s['token_supply'])*release_rate\n",
|
|
" return {'mint': mint}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#these are uncontrollerd choices of users in the provider consumer\n",
|
|
"\n",
|
|
"def consumer_choice(step, sL, s):\n",
|
|
" #fiat paid by consumers\n",
|
|
" #note: balance of consumption vol covered in tokens (computed later)\n",
|
|
" \n",
|
|
" #simple heuristic ~ the fraction of token payment is proportion to free supply share\n",
|
|
" free_supply = s['token_supply']-s['token_reserve']\n",
|
|
" share_of_free_supply = free_supply/ s['token_supply']\n",
|
|
" \n",
|
|
" txi_fiat= (1.0-share_of_free_supply)*s['tx_volume']\n",
|
|
" return {'txi_fiat': txi_fiat}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#these are uncontrollerd choices of users in the provider role\n",
|
|
"\n",
|
|
"#simple heuristic ~ convex combination of 2 policies; parameterized by theta\n",
|
|
"#part1: fixed base rate ~ theta percent of service providers ALWAYS use fiat\n",
|
|
"theta = .6\n",
|
|
"#part2: remaining fraction is determined by the size of the platform community \"economy\"\n",
|
|
"#recall that both of the below states have 'fiat' as their unit, ratio is unitless\n",
|
|
"#parameter gamma is a tuning gain\n",
|
|
"gamma = .1\n",
|
|
"#simplifying math note:\n",
|
|
"#production_consumption_ratio = s['volume_of_production']*s['cost_of_production']/s['tx_volume'] \n",
|
|
"#production_consumption_ratio * s['tx_volume'] = s['volume_of_production']*s['cost_of_production']\n",
|
|
"\n",
|
|
"def provider_choice(step, sL, s):\n",
|
|
" #fiat claimed by providers\n",
|
|
" #note: balance of provided vol covered in tokens (computed later)\n",
|
|
" txo_fiat = theta*s['tx_volume']+ (1-theta)*gamma*s['volume_of_production']*s['cost_of_production']\n",
|
|
" return {'txo_fiat': txo_fiat}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#these are uncontrollerd choices of users in the producer role\n",
|
|
"\n",
|
|
"#estimatable parameters\n",
|
|
"roi_threshold = .2 \n",
|
|
"attrition_rate = .5\n",
|
|
"roi_gain = .025\n",
|
|
"\n",
|
|
"def producer_choice(step, sL, s):\n",
|
|
" #ROI heuristic\n",
|
|
" # add or remove resources based on deviation from threshold\n",
|
|
" if s['producer_roi_estimate'] < 0:\n",
|
|
" delta_labor = s['volume_of_production']*(attrition_rate-1.0)\n",
|
|
" else:\n",
|
|
" ratio = s['producer_roi_estimate']/roi_threshold\n",
|
|
" delta_labor = roi_gain*s['volume_of_production']*(ratio-1.0)\n",
|
|
" \n",
|
|
" return {'delta_labor': delta_labor}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#governance decision ~ system policy for compensating producers\n",
|
|
"#consider transaction volume, labor committed and token reserve and supply to determine payout\n",
|
|
"\n",
|
|
"#this function is a parameter of this policy which determines diminishing value of more labor\n",
|
|
"base_value = 1000.0\n",
|
|
"\n",
|
|
"def marginal_utility_function(x):\n",
|
|
" #this is how much the platform value a total amount of production (in fiat)\n",
|
|
" return base_value+np.sqrt(x)\n",
|
|
"\n",
|
|
"def producer_compensation_policy(step, sL, s):\n",
|
|
" tokens_paid = s['conversion_rate']*marginal_utility_function(s['volume_of_production'])\n",
|
|
" return {'tokens_paid': tokens_paid}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#governance decision ~ system policy for budgeting to cover overhead costs\n",
|
|
"\n",
|
|
"buffer_runway = 3.0\n",
|
|
"reserve_threshold = .25\n",
|
|
"min_budget_release = .03\n",
|
|
"\n",
|
|
"def budgeting_policy(step, sL, s):\n",
|
|
" #define an estimate of future overhead\n",
|
|
" proj_overhead = s['overhead_cost'] #simple naive \n",
|
|
" \n",
|
|
" #simple threshold based conditional logic\n",
|
|
" if s['operational_budget']< buffer_runway*proj_overhead:\n",
|
|
" target_release = buffer_runway*proj_overhead-s['operational_budget']\n",
|
|
" if s['fiat_reserve']-target_release > reserve_threshold*s['fiat_reserve']:\n",
|
|
" budget_released = target_release\n",
|
|
" else:\n",
|
|
" budget_released = (1.0-reserve_threshold)*s['fiat_reserve']\n",
|
|
" else:\n",
|
|
" budget_released = min_budget_release*s['fiat_reserve']\n",
|
|
" \n",
|
|
" return {'budget_released': budget_released}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#behavoirs used to trigger mechanisms with no explicit input\n",
|
|
"\n",
|
|
"#tiggers environment\n",
|
|
"def dummy_behavior(step, sL, s):\n",
|
|
" return {'value': 0}\n",
|
|
"\n",
|
|
"#triggers smooth weighted averages\n",
|
|
"def signal_trigger(step, sL, s):\n",
|
|
" return {'value': 0}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#Mechanisms"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def update_conversion_rate(step, sL, s, _input):\n",
|
|
" y = 'conversion_rate'\n",
|
|
" x = _input['new_conversion_rate']\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#smooth averaging signals\n",
|
|
"#forgetting rate parameter\n",
|
|
"rho = .1 #1 means you forget all past times, must be >0 \n",
|
|
"\n",
|
|
"def update_smooth_avg_fiat_reserve(step, sL, s, _input):\n",
|
|
" y = 'smooth_avg_fiat_reserve'\n",
|
|
" x = s['fiat_reserve']*rho+s['smooth_avg_fiat_reserve']*(1-rho)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def update_smooth_avg_token_reserve(step, sL, s, _input):\n",
|
|
" y = 'smooth_avg_token_reserve'\n",
|
|
" x = s['token_reserve']*rho+s['smooth_avg_token_reserve']*(1-rho)\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#minting process mints into the reserve\n",
|
|
"def mint1(step, sL, s, _input):\n",
|
|
" y = 'token_supply'\n",
|
|
" x = s['token_supply'] + _input['mint']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def mint2(step, sL, s, _input):\n",
|
|
" y = 'token_reserve'\n",
|
|
" x = s['token_reserve'] + _input['mint']\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def commit_delta_production(step, sL, s, _input):\n",
|
|
" y = 'volume_of_production'\n",
|
|
" x = s['volume_of_production']+_input['delta_labor']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def compensate_production(step, sL, s, _input):\n",
|
|
" y = 'token_reserve'\n",
|
|
" x = s['token_reserve']-_input['tokens_paid']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"#forgetting rate\n",
|
|
"rho2 = rho #same as for the other smoothed averages\n",
|
|
"def update_producer_roi_estimate(step, sL, s, _input):\n",
|
|
" revenue = _input['tokens_paid']/s['conversion_rate']\n",
|
|
" cost = s['cost_of_production']*s['volume_of_production']\n",
|
|
" spot_ROI_estimate = (revenue-cost)/cost\n",
|
|
" y = 'producer_roi_estimate'\n",
|
|
" x = rho2*spot_ROI_estimate + s['producer_roi_estimate']*(1.0-rho2)\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"conversion_fee = .03\n",
|
|
"\n",
|
|
"def capture_consumer_payments1(step, sL, s, _input):\n",
|
|
" #fiat inbound\n",
|
|
" y = 'fiat_reserve'\n",
|
|
" x = s['fiat_reserve']+_input['txi_fiat']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def capture_consumer_payments2(step, sL, s, _input):\n",
|
|
" #tokens inbound\n",
|
|
" y = 'token_reserve'\n",
|
|
" fiat_eq = s['tx_volume']-_input['txi_fiat']\n",
|
|
" x = s['token_reserve']+s['conversion_rate']*fiat_eq*(1.0+conversion_fee)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"platform_fee = 0.075\n",
|
|
"\n",
|
|
"def compensate_providers1(step, sL, s, _input):\n",
|
|
" #fiat outbound\n",
|
|
" y = 'fiat_reserve'\n",
|
|
" x = s['fiat_reserve']-_input['txo_fiat']*(1.0-platform_fee)\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def compensate_providers2(step, sL, s, _input):\n",
|
|
" #tokens outbound\n",
|
|
" y = 'token_reserve'\n",
|
|
" fiat_eq = s['tx_volume']-_input['txo_fiat']\n",
|
|
" x = s['token_reserve']-s['conversion_rate']*fiat_eq*(1.0-platform_fee-conversion_fee)\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def release_funds(step, sL, s, _input):\n",
|
|
" #tokens outbound\n",
|
|
" y = 'fiat_reserve'\n",
|
|
" x = s['fiat_reserve'] - _input['budget_released']\n",
|
|
" return (y, x)\n",
|
|
"\n",
|
|
"def update_budget(step, sL, s, _input):\n",
|
|
" #tokens outbound\n",
|
|
" y = 'operational_budget'\n",
|
|
" x = s['operational_budget'] + _input['budget_released']\n",
|
|
" return (y, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#build mechanism dictionary to \"wire up the circuit\"\n",
|
|
"mechanisms = {\n",
|
|
" #mechstep 0\n",
|
|
" 'evolve': \n",
|
|
" {\n",
|
|
" 'behaviors': \n",
|
|
" {\n",
|
|
" 'dummy': dummy_behavior\n",
|
|
" },\n",
|
|
" 'states':\n",
|
|
" {\n",
|
|
" 'cost_of_production': cost_of_production_generator,\n",
|
|
" 'tx_volume': tx_volume_generator,\n",
|
|
" 'overhead_cost': overhead_cost_generator\n",
|
|
" } \n",
|
|
" },\n",
|
|
" \n",
|
|
" #mechstep 1\n",
|
|
" 'producers_act': \n",
|
|
" {\n",
|
|
" 'behaviors': \n",
|
|
" {\n",
|
|
" 'action': producer_choice\n",
|
|
" },\n",
|
|
" 'states': \n",
|
|
" {\n",
|
|
" 'volume_of_production': commit_delta_production\n",
|
|
" }\n",
|
|
" },\n",
|
|
" \n",
|
|
" #mechstep 2\n",
|
|
" 'consumers_act': \n",
|
|
" {\n",
|
|
" 'behaviors': \n",
|
|
" {\n",
|
|
" 'action': consumer_choice\n",
|
|
" },\n",
|
|
" 'states': \n",
|
|
" {\n",
|
|
" 'fiat_reserve': capture_consumer_payments1,\n",
|
|
" 'token_reserve': capture_consumer_payments2\n",
|
|
" }\n",
|
|
" },\n",
|
|
" \n",
|
|
" #mechstep 3\n",
|
|
" 'providers_act': \n",
|
|
" {\n",
|
|
" 'behaviors': \n",
|
|
" {\n",
|
|
" 'action': provider_choice\n",
|
|
" },\n",
|
|
" 'states': \n",
|
|
" {\n",
|
|
" 'fiat_reserve': compensate_providers1,\n",
|
|
" 'token_reserve': compensate_providers2\n",
|
|
" }\n",
|
|
" },\n",
|
|
" \n",
|
|
" #mechstep 4 #governance category policy\n",
|
|
" 'pay_producers': \n",
|
|
" {\n",
|
|
" 'behaviors': \n",
|
|
" {\n",
|
|
" 'action': producer_compensation_policy\n",
|
|
" },\n",
|
|
" 'states': \n",
|
|
" {\n",
|
|
" 'token_reserve': compensate_production,\n",
|
|
" 'producer_roi_estimate': update_producer_roi_estimate\n",
|
|
" }\n",
|
|
" },\n",
|
|
" \n",
|
|
" #mechstep 5 #governance category policy\n",
|
|
" 'budgeting': \n",
|
|
" {\n",
|
|
" 'behaviors': \n",
|
|
" {\n",
|
|
" 'action': budgeting_policy\n",
|
|
" },\n",
|
|
" 'states': \n",
|
|
" {\n",
|
|
" 'fiat_reserve': release_funds,\n",
|
|
" 'operational_budget': update_budget\n",
|
|
" }\n",
|
|
" },\n",
|
|
" \n",
|
|
" #mechstep 6 #governance category policy\n",
|
|
" 'minting': \n",
|
|
" {\n",
|
|
" 'behaviors': \n",
|
|
" {\n",
|
|
" 'action': minting_policy\n",
|
|
" },\n",
|
|
" 'states': \n",
|
|
" {\n",
|
|
" 'token_reserve': mint1,\n",
|
|
" 'token_supply': mint2\n",
|
|
" }\n",
|
|
" },\n",
|
|
" \n",
|
|
" #mechstep 7 #simple signals computations\n",
|
|
" 'signal_updates': \n",
|
|
" {\n",
|
|
" 'behaviors': \n",
|
|
" {\n",
|
|
" 'action': signal_trigger\n",
|
|
" },\n",
|
|
" 'states': \n",
|
|
" {\n",
|
|
" 'smooth_avg_fiat_reserve': update_smooth_avg_fiat_reserve,\n",
|
|
" 'smooth_avg_token_reserve':update_smooth_avg_token_reserve\n",
|
|
" }\n",
|
|
" },\n",
|
|
" \n",
|
|
" #mechstep 8 #governance category policy\n",
|
|
" 'price_controller': \n",
|
|
" {\n",
|
|
" 'behaviors': \n",
|
|
" {\n",
|
|
" 'action': conversion_policy\n",
|
|
" },\n",
|
|
" 'states': \n",
|
|
" {\n",
|
|
" 'conversion_rate': update_conversion_rate\n",
|
|
" }\n",
|
|
" } \n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# def widget_handler(beta=float(0.05), gamma=float(0.20),\n",
|
|
"# subscription_fee=float(1.0), \n",
|
|
"# incentive_cost=float(10.0), \n",
|
|
"# stickiness_cost=float(5.0)):\n",
|
|
"# initial_condition['beta'] = beta\n",
|
|
"# initial_condition['gamma'] = gamma\n",
|
|
"# subscription_fee = subscription_fee\n",
|
|
"# incentive_cost = incentive_cost\n",
|
|
"# stickiness_cost = stickiness_cost\n",
|
|
" \n",
|
|
"# config = Configuration(\n",
|
|
"# sim_config=sim_config,\n",
|
|
"# state_dict=initial_condition,\n",
|
|
"# seed=seed,\n",
|
|
"# exogenous_states=exogenous_states,\n",
|
|
"# env_processes=env_processes,\n",
|
|
"# mechanisms=mechanisms)\n",
|
|
"\n",
|
|
"# exec_mode = ExecutionMode()\n",
|
|
"# exec_context = ExecutionContext(exec_mode.single_proc)\n",
|
|
"# executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
|
|
"# raw_result, tensor = executor.main()\n",
|
|
"# df = pd.DataFrame(raw_result)\n",
|
|
"# df['timestamp'] = pd.to_datetime(df['timestamp'], format=ts_format)\n",
|
|
" \n",
|
|
"# fig = plt.figure(figsize=(7, 14))\n",
|
|
" \n",
|
|
"# sir = fig.add_subplot(3, 1, 1)\n",
|
|
"# sir.plot('timestamp', 'Ps', data=df, marker='', color='C0', linewidth=2)\n",
|
|
"# sir.plot('timestamp', 'Pi', data=df, marker='', color='orange', linewidth=2)\n",
|
|
"# sir.plot('timestamp', 'Pr', data=df, marker='', color='green', linewidth=2)\n",
|
|
"# sir.legend()\n",
|
|
" \n",
|
|
"# beta_gamma = fig.add_subplot(3, 1, 2)\n",
|
|
"# beta_gamma.plot('timestamp', 'beta', data=df, marker='', color='C0', linewidth=2)\n",
|
|
"# beta_gamma.plot('timestamp', 'gamma', data=df, marker='', color='orange', linewidth=2)\n",
|
|
"# beta_gamma.legend()\n",
|
|
" \n",
|
|
"# budget_pi = fig.add_subplot(3, 1, 3)\n",
|
|
"# budget_pi.plot('timestamp', 'Budget', data=df, marker='', color='C0', linewidth=2)\n",
|
|
"# budget_pi.plot('timestamp', 'Pi', data=df, marker='', color='orange', linewidth=2)\n",
|
|
"# budget_pi.legend()\n",
|
|
" \n",
|
|
"# plt.show()\n",
|
|
" \n",
|
|
"# sliders = interact_manual(widget_handler, \n",
|
|
"# beta=(0, 1, 0.01),\n",
|
|
"# gamma=(0, 1, 0.01),\n",
|
|
"# subscription_fee=(0, 10, 0.1),\n",
|
|
"# incentive_cost=(0, 20, 0.5),\n",
|
|
"# stickiness_cost=(0, 20, 0.5)\n",
|
|
"# )\n",
|
|
"# sliders"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"single_proc: [<SimCAD.configuration.Configuration object at 0x11911e0b8>]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"config = Configuration(\n",
|
|
" sim_config=sim_config,\n",
|
|
" state_dict=initial_condition,\n",
|
|
" seed=seed,\n",
|
|
" exogenous_states=exogenous_states,\n",
|
|
" env_processes=env_processes,\n",
|
|
" mechanisms=mechanisms)\n",
|
|
"\n",
|
|
"from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
|
|
"exec_mode = ExecutionMode()\n",
|
|
"exec_context = ExecutionContext(exec_mode.single_proc)\n",
|
|
"executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
|
|
"raw_result, tensor = executor.main()\n",
|
|
"df = pd.DataFrame(raw_result)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# df.plot('timestamp', ['Ps','Pi', 'Pr'])\n",
|
|
"# df.plot('timestamp', ['beta', 'gamma'])\n",
|
|
"# df.plot('timestamp', ['Budget', 'Pi'])\n",
|
|
"# df[['Ps','Pi', 'Pr']].describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"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 th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>conversion_rate</th>\n",
|
|
" <th>cost_of_production</th>\n",
|
|
" <th>fiat_reserve</th>\n",
|
|
" <th>mech_step</th>\n",
|
|
" <th>operational_budget</th>\n",
|
|
" <th>overhead_cost</th>\n",
|
|
" <th>producer_roi_estimate</th>\n",
|
|
" <th>run</th>\n",
|
|
" <th>smooth_avg_fiat_reserve</th>\n",
|
|
" <th>smooth_avg_token_reserve</th>\n",
|
|
" <th>time_step</th>\n",
|
|
" <th>timestamp</th>\n",
|
|
" <th>token_reserve</th>\n",
|
|
" <th>token_supply</th>\n",
|
|
" <th>tx_volume</th>\n",
|
|
" <th>volume_of_production</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>10.00000</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>500.000000</td>\n",
|
|
" <td>1.1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2019-01-01</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>1000.000000</td>\n",
|
|
" <td>20.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>9.60629</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>511.703711</td>\n",
|
|
" <td>1.1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2019-01-08</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>1070.613127</td>\n",
|
|
" <td>20.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>9.60629</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>511.703711</td>\n",
|
|
" <td>1.1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2019-01-08</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>1070.613127</td>\n",
|
|
" <td>22.25</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>9.60629</td>\n",
|
|
" <td>26070.613127</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>511.703711</td>\n",
|
|
" <td>1.1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2019-01-08</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>1070.613127</td>\n",
|
|
" <td>22.25</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>9.60629</td>\n",
|
|
" <td>25468.514463</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>511.703711</td>\n",
|
|
" <td>1.1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2019-01-08</td>\n",
|
|
" <td>24624.372391</td>\n",
|
|
" <td>25000.0</td>\n",
|
|
" <td>1070.613127</td>\n",
|
|
" <td>22.25</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" conversion_rate cost_of_production fiat_reserve mech_step \\\n",
|
|
"0 1.0 10.00000 25000.000000 0 \n",
|
|
"1 1.0 9.60629 25000.000000 1 \n",
|
|
"2 1.0 9.60629 25000.000000 2 \n",
|
|
"3 1.0 9.60629 26070.613127 3 \n",
|
|
"4 1.0 9.60629 25468.514463 4 \n",
|
|
"\n",
|
|
" operational_budget overhead_cost producer_roi_estimate run \\\n",
|
|
"0 25000.0 500.000000 1.1 1 \n",
|
|
"1 25000.0 511.703711 1.1 1 \n",
|
|
"2 25000.0 511.703711 1.1 1 \n",
|
|
"3 25000.0 511.703711 1.1 1 \n",
|
|
"4 25000.0 511.703711 1.1 1 \n",
|
|
"\n",
|
|
" smooth_avg_fiat_reserve smooth_avg_token_reserve time_step timestamp \\\n",
|
|
"0 25000.0 25000.0 0 2019-01-01 \n",
|
|
"1 25000.0 25000.0 1 2019-01-08 \n",
|
|
"2 25000.0 25000.0 1 2019-01-08 \n",
|
|
"3 25000.0 25000.0 1 2019-01-08 \n",
|
|
"4 25000.0 25000.0 1 2019-01-08 \n",
|
|
"\n",
|
|
" token_reserve token_supply tx_volume volume_of_production \n",
|
|
"0 25000.000000 25000.0 1000.000000 20.00 \n",
|
|
"1 25000.000000 25000.0 1070.613127 20.00 \n",
|
|
"2 25000.000000 25000.0 1070.613127 22.25 \n",
|
|
"3 25000.000000 25000.0 1070.613127 22.25 \n",
|
|
"4 24624.372391 25000.0 1070.613127 22.25 "
|
|
]
|
|
},
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"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 th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>conversion_rate</th>\n",
|
|
" <th>cost_of_production</th>\n",
|
|
" <th>fiat_reserve</th>\n",
|
|
" <th>mech_step</th>\n",
|
|
" <th>operational_budget</th>\n",
|
|
" <th>overhead_cost</th>\n",
|
|
" <th>producer_roi_estimate</th>\n",
|
|
" <th>run</th>\n",
|
|
" <th>smooth_avg_fiat_reserve</th>\n",
|
|
" <th>smooth_avg_token_reserve</th>\n",
|
|
" <th>time_step</th>\n",
|
|
" <th>timestamp</th>\n",
|
|
" <th>token_reserve</th>\n",
|
|
" <th>token_supply</th>\n",
|
|
" <th>tx_volume</th>\n",
|
|
" <th>volume_of_production</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>1.034702</td>\n",
|
|
" <td>9.606290</td>\n",
|
|
" <td>24704.459029</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>25764.055434</td>\n",
|
|
" <td>511.703711</td>\n",
|
|
" <td>1.360065</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24970.445903</td>\n",
|
|
" <td>25836.965540</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2019-01-08</td>\n",
|
|
" <td>33369.655400</td>\n",
|
|
" <td>34750.000000</td>\n",
|
|
" <td>1070.613127</td>\n",
|
|
" <td>22.250000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>1.100521</td>\n",
|
|
" <td>9.376439</td>\n",
|
|
" <td>24387.122926</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>26518.296349</td>\n",
|
|
" <td>511.789835</td>\n",
|
|
" <td>1.544795</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24912.113605</td>\n",
|
|
" <td>27416.292133</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2019-01-15</td>\n",
|
|
" <td>41630.231465</td>\n",
|
|
" <td>44402.500000</td>\n",
|
|
" <td>1099.846197</td>\n",
|
|
" <td>25.476431</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>27</th>\n",
|
|
" <td>1.194015</td>\n",
|
|
" <td>8.556163</td>\n",
|
|
" <td>24078.990778</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>27263.007404</td>\n",
|
|
" <td>511.842447</td>\n",
|
|
" <td>1.685196</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24828.801323</td>\n",
|
|
" <td>29645.953569</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2019-01-22</td>\n",
|
|
" <td>49712.906496</td>\n",
|
|
" <td>53958.475000</td>\n",
|
|
" <td>1165.814731</td>\n",
|
|
" <td>29.759003</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>36</th>\n",
|
|
" <td>1.311996</td>\n",
|
|
" <td>8.441051</td>\n",
|
|
" <td>23787.933002</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>27998.716672</td>\n",
|
|
" <td>511.933639</td>\n",
|
|
" <td>1.754430</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24724.714491</td>\n",
|
|
" <td>32438.717331</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>2019-01-29</td>\n",
|
|
" <td>57573.591189</td>\n",
|
|
" <td>63418.890250</td>\n",
|
|
" <td>1243.923179</td>\n",
|
|
" <td>35.283746</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>45</th>\n",
|
|
" <td>1.451572</td>\n",
|
|
" <td>7.968585</td>\n",
|
|
" <td>23509.132739</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>28725.803252</td>\n",
|
|
" <td>512.039533</td>\n",
|
|
" <td>1.778724</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24603.156315</td>\n",
|
|
" <td>35713.244830</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2019-02-05</td>\n",
|
|
" <td>65183.992318</td>\n",
|
|
" <td>72784.701347</td>\n",
|
|
" <td>1305.756929</td>\n",
|
|
" <td>42.139511</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" conversion_rate cost_of_production fiat_reserve mech_step \\\n",
|
|
"9 1.034702 9.606290 24704.459029 9 \n",
|
|
"18 1.100521 9.376439 24387.122926 9 \n",
|
|
"27 1.194015 8.556163 24078.990778 9 \n",
|
|
"36 1.311996 8.441051 23787.933002 9 \n",
|
|
"45 1.451572 7.968585 23509.132739 9 \n",
|
|
"\n",
|
|
" operational_budget overhead_cost producer_roi_estimate run \\\n",
|
|
"9 25764.055434 511.703711 1.360065 1 \n",
|
|
"18 26518.296349 511.789835 1.544795 1 \n",
|
|
"27 27263.007404 511.842447 1.685196 1 \n",
|
|
"36 27998.716672 511.933639 1.754430 1 \n",
|
|
"45 28725.803252 512.039533 1.778724 1 \n",
|
|
"\n",
|
|
" smooth_avg_fiat_reserve smooth_avg_token_reserve time_step timestamp \\\n",
|
|
"9 24970.445903 25836.965540 1 2019-01-08 \n",
|
|
"18 24912.113605 27416.292133 2 2019-01-15 \n",
|
|
"27 24828.801323 29645.953569 3 2019-01-22 \n",
|
|
"36 24724.714491 32438.717331 4 2019-01-29 \n",
|
|
"45 24603.156315 35713.244830 5 2019-02-05 \n",
|
|
"\n",
|
|
" token_reserve token_supply tx_volume volume_of_production \n",
|
|
"9 33369.655400 34750.000000 1070.613127 22.250000 \n",
|
|
"18 41630.231465 44402.500000 1099.846197 25.476431 \n",
|
|
"27 49712.906496 53958.475000 1165.814731 29.759003 \n",
|
|
"36 57573.591189 63418.890250 1243.923179 35.283746 \n",
|
|
"45 65183.992318 72784.701347 1305.756929 42.139511 "
|
|
]
|
|
},
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf=df[df.mech_step==9].copy()\n",
|
|
"rdf.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
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" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>conversion_rate</th>\n",
|
|
" <th>cost_of_production</th>\n",
|
|
" <th>fiat_reserve</th>\n",
|
|
" <th>mech_step</th>\n",
|
|
" <th>operational_budget</th>\n",
|
|
" <th>overhead_cost</th>\n",
|
|
" <th>producer_roi_estimate</th>\n",
|
|
" <th>run</th>\n",
|
|
" <th>smooth_avg_fiat_reserve</th>\n",
|
|
" <th>smooth_avg_token_reserve</th>\n",
|
|
" <th>time_step</th>\n",
|
|
" <th>token_reserve</th>\n",
|
|
" <th>token_supply</th>\n",
|
|
" <th>tx_volume</th>\n",
|
|
" <th>volume_of_production</th>\n",
|
|
" <th>price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.0</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.0</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>18.038010</td>\n",
|
|
" <td>5.313747</td>\n",
|
|
" <td>15555.004693</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>50423.295535</td>\n",
|
|
" <td>514.458177</td>\n",
|
|
" <td>0.436260</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>15418.591151</td>\n",
|
|
" <td>209699.048715</td>\n",
|
|
" <td>52.500000</td>\n",
|
|
" <td>241087.912835</td>\n",
|
|
" <td>398212.294837</td>\n",
|
|
" <td>12100.995112</td>\n",
|
|
" <td>167.341179</td>\n",
|
|
" <td>0.145617</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>12.495887</td>\n",
|
|
" <td>1.119161</td>\n",
|
|
" <td>7784.452776</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>11752.391870</td>\n",
|
|
" <td>1.156366</td>\n",
|
|
" <td>0.460784</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>6239.000987</td>\n",
|
|
" <td>105845.124212</td>\n",
|
|
" <td>30.166206</td>\n",
|
|
" <td>105560.540253</td>\n",
|
|
" <td>180823.117735</td>\n",
|
|
" <td>10624.312917</td>\n",
|
|
" <td>59.578909</td>\n",
|
|
" <td>0.197846</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>1.034702</td>\n",
|
|
" <td>3.705782</td>\n",
|
|
" <td>6319.578813</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>25764.055434</td>\n",
|
|
" <td>511.703711</td>\n",
|
|
" <td>-0.016302</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>7266.027136</td>\n",
|
|
" <td>25836.965540</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>33369.655400</td>\n",
|
|
" <td>34750.000000</td>\n",
|
|
" <td>1070.613127</td>\n",
|
|
" <td>22.250000</td>\n",
|
|
" <td>0.026173</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>6.397693</td>\n",
|
|
" <td>4.631049</td>\n",
|
|
" <td>8331.708170</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>42545.677105</td>\n",
|
|
" <td>513.802505</td>\n",
|
|
" <td>0.155362</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>9146.154990</td>\n",
|
|
" <td>128117.717089</td>\n",
|
|
" <td>26.750000</td>\n",
|
|
" <td>157628.001519</td>\n",
|
|
" <td>254838.873344</td>\n",
|
|
" <td>3278.599923</td>\n",
|
|
" <td>142.007477</td>\n",
|
|
" <td>0.033688</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>16.460403</td>\n",
|
|
" <td>5.069973</td>\n",
|
|
" <td>14648.845933</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>51975.359355</td>\n",
|
|
" <td>514.660656</td>\n",
|
|
" <td>0.300279</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>15076.737576</td>\n",
|
|
" <td>203840.294648</td>\n",
|
|
" <td>52.500000</td>\n",
|
|
" <td>237337.089296</td>\n",
|
|
" <td>424748.426185</td>\n",
|
|
" <td>9105.589498</td>\n",
|
|
" <td>161.784832</td>\n",
|
|
" <td>0.060753</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>29.685322</td>\n",
|
|
" <td>5.569544</td>\n",
|
|
" <td>21083.887648</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>57725.254975</td>\n",
|
|
" <td>515.239833</td>\n",
|
|
" <td>0.402245</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>21333.054255</td>\n",
|
|
" <td>301050.565295</td>\n",
|
|
" <td>78.250000</td>\n",
|
|
" <td>332307.585381</td>\n",
|
|
" <td>555918.482210</td>\n",
|
|
" <td>16585.062472</td>\n",
|
|
" <td>187.147862</td>\n",
|
|
" <td>0.156352</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>38.207186</td>\n",
|
|
" <td>9.606290</td>\n",
|
|
" <td>38164.575141</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>75032.592416</td>\n",
|
|
" <td>516.263750</td>\n",
|
|
" <td>1.778724</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>26576.334263</td>\n",
|
|
" <td>387715.763169</td>\n",
|
|
" <td>104.000000</td>\n",
|
|
" <td>414982.125266</td>\n",
|
|
" <td>657181.023606</td>\n",
|
|
" <td>41543.209511</td>\n",
|
|
" <td>306.200332</td>\n",
|
|
" <td>0.966462</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" conversion_rate cost_of_production fiat_reserve mech_step \\\n",
|
|
"count 104.000000 104.000000 104.000000 104.0 \n",
|
|
"mean 18.038010 5.313747 15555.004693 9.0 \n",
|
|
"std 12.495887 1.119161 7784.452776 0.0 \n",
|
|
"min 1.034702 3.705782 6319.578813 9.0 \n",
|
|
"25% 6.397693 4.631049 8331.708170 9.0 \n",
|
|
"50% 16.460403 5.069973 14648.845933 9.0 \n",
|
|
"75% 29.685322 5.569544 21083.887648 9.0 \n",
|
|
"max 38.207186 9.606290 38164.575141 9.0 \n",
|
|
"\n",
|
|
" operational_budget overhead_cost producer_roi_estimate run \\\n",
|
|
"count 104.000000 104.000000 104.000000 104.0 \n",
|
|
"mean 50423.295535 514.458177 0.436260 1.0 \n",
|
|
"std 11752.391870 1.156366 0.460784 0.0 \n",
|
|
"min 25764.055434 511.703711 -0.016302 1.0 \n",
|
|
"25% 42545.677105 513.802505 0.155362 1.0 \n",
|
|
"50% 51975.359355 514.660656 0.300279 1.0 \n",
|
|
"75% 57725.254975 515.239833 0.402245 1.0 \n",
|
|
"max 75032.592416 516.263750 1.778724 1.0 \n",
|
|
"\n",
|
|
" smooth_avg_fiat_reserve smooth_avg_token_reserve time_step \\\n",
|
|
"count 104.000000 104.000000 104.000000 \n",
|
|
"mean 15418.591151 209699.048715 52.500000 \n",
|
|
"std 6239.000987 105845.124212 30.166206 \n",
|
|
"min 7266.027136 25836.965540 1.000000 \n",
|
|
"25% 9146.154990 128117.717089 26.750000 \n",
|
|
"50% 15076.737576 203840.294648 52.500000 \n",
|
|
"75% 21333.054255 301050.565295 78.250000 \n",
|
|
"max 26576.334263 387715.763169 104.000000 \n",
|
|
"\n",
|
|
" token_reserve token_supply tx_volume volume_of_production \\\n",
|
|
"count 104.000000 104.000000 104.000000 104.000000 \n",
|
|
"mean 241087.912835 398212.294837 12100.995112 167.341179 \n",
|
|
"std 105560.540253 180823.117735 10624.312917 59.578909 \n",
|
|
"min 33369.655400 34750.000000 1070.613127 22.250000 \n",
|
|
"25% 157628.001519 254838.873344 3278.599923 142.007477 \n",
|
|
"50% 237337.089296 424748.426185 9105.589498 161.784832 \n",
|
|
"75% 332307.585381 555918.482210 16585.062472 187.147862 \n",
|
|
"max 414982.125266 657181.023606 41543.209511 306.200332 \n",
|
|
"\n",
|
|
" price \n",
|
|
"count 104.000000 \n",
|
|
"mean 0.145617 \n",
|
|
"std 0.197846 \n",
|
|
"min 0.026173 \n",
|
|
"25% 0.033688 \n",
|
|
"50% 0.060753 \n",
|
|
"75% 0.156352 \n",
|
|
"max 0.966462 "
|
|
]
|
|
},
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"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 th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>conversion_rate</th>\n",
|
|
" <th>cost_of_production</th>\n",
|
|
" <th>fiat_reserve</th>\n",
|
|
" <th>mech_step</th>\n",
|
|
" <th>operational_budget</th>\n",
|
|
" <th>overhead_cost</th>\n",
|
|
" <th>producer_roi_estimate</th>\n",
|
|
" <th>run</th>\n",
|
|
" <th>smooth_avg_fiat_reserve</th>\n",
|
|
" <th>smooth_avg_token_reserve</th>\n",
|
|
" <th>time_step</th>\n",
|
|
" <th>timestamp</th>\n",
|
|
" <th>token_reserve</th>\n",
|
|
" <th>token_supply</th>\n",
|
|
" <th>tx_volume</th>\n",
|
|
" <th>volume_of_production</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>900</th>\n",
|
|
" <td>17.246983</td>\n",
|
|
" <td>4.471451</td>\n",
|
|
" <td>31426.205808</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>70640.458442</td>\n",
|
|
" <td>516.076055</td>\n",
|
|
" <td>0.336838</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>21778.706319</td>\n",
|
|
" <td>375616.968473</td>\n",
|
|
" <td>100</td>\n",
|
|
" <td>2020-12-01</td>\n",
|
|
" <td>403467.174511</td>\n",
|
|
" <td>643118.467259</td>\n",
|
|
" <td>35156.692021</td>\n",
|
|
" <td>190.704716</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>909</th>\n",
|
|
" <td>16.536117</td>\n",
|
|
" <td>4.674597</td>\n",
|
|
" <td>32992.574604</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>71660.847347</td>\n",
|
|
" <td>516.139995</td>\n",
|
|
" <td>0.314978</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>22900.093148</td>\n",
|
|
" <td>378678.622275</td>\n",
|
|
" <td>101</td>\n",
|
|
" <td>2020-12-08</td>\n",
|
|
" <td>406233.506489</td>\n",
|
|
" <td>646687.282586</td>\n",
|
|
" <td>36211.790984</td>\n",
|
|
" <td>193.966675</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>918</th>\n",
|
|
" <td>15.858142</td>\n",
|
|
" <td>4.376216</td>\n",
|
|
" <td>34584.171484</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>72730.460898</td>\n",
|
|
" <td>516.180719</td>\n",
|
|
" <td>0.301248</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24068.500981</td>\n",
|
|
" <td>381681.716027</td>\n",
|
|
" <td>102</td>\n",
|
|
" <td>2020-12-15</td>\n",
|
|
" <td>408709.559796</td>\n",
|
|
" <td>650220.409760</td>\n",
|
|
" <td>36802.566095</td>\n",
|
|
" <td>196.754422</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>927</th>\n",
|
|
" <td>15.211750</td>\n",
|
|
" <td>4.764640</td>\n",
|
|
" <td>36271.010598</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>73852.244731</td>\n",
|
|
" <td>516.206337</td>\n",
|
|
" <td>0.277948</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25288.751943</td>\n",
|
|
" <td>384686.167380</td>\n",
|
|
" <td>103</td>\n",
|
|
" <td>2020-12-22</td>\n",
|
|
" <td>411726.229564</td>\n",
|
|
" <td>653718.205663</td>\n",
|
|
" <td>38653.219841</td>\n",
|
|
" <td>199.244554</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>936</th>\n",
|
|
" <td>14.588760</td>\n",
|
|
" <td>4.626827</td>\n",
|
|
" <td>38164.575141</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>75032.592416</td>\n",
|
|
" <td>516.263750</td>\n",
|
|
" <td>0.259105</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>26576.334263</td>\n",
|
|
" <td>387715.763169</td>\n",
|
|
" <td>104</td>\n",
|
|
" <td>2020-12-29</td>\n",
|
|
" <td>414982.125266</td>\n",
|
|
" <td>657181.023606</td>\n",
|
|
" <td>41543.209511</td>\n",
|
|
" <td>201.185891</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" conversion_rate cost_of_production fiat_reserve mech_step \\\n",
|
|
"900 17.246983 4.471451 31426.205808 9 \n",
|
|
"909 16.536117 4.674597 32992.574604 9 \n",
|
|
"918 15.858142 4.376216 34584.171484 9 \n",
|
|
"927 15.211750 4.764640 36271.010598 9 \n",
|
|
"936 14.588760 4.626827 38164.575141 9 \n",
|
|
"\n",
|
|
" operational_budget overhead_cost producer_roi_estimate run \\\n",
|
|
"900 70640.458442 516.076055 0.336838 1 \n",
|
|
"909 71660.847347 516.139995 0.314978 1 \n",
|
|
"918 72730.460898 516.180719 0.301248 1 \n",
|
|
"927 73852.244731 516.206337 0.277948 1 \n",
|
|
"936 75032.592416 516.263750 0.259105 1 \n",
|
|
"\n",
|
|
" smooth_avg_fiat_reserve smooth_avg_token_reserve time_step timestamp \\\n",
|
|
"900 21778.706319 375616.968473 100 2020-12-01 \n",
|
|
"909 22900.093148 378678.622275 101 2020-12-08 \n",
|
|
"918 24068.500981 381681.716027 102 2020-12-15 \n",
|
|
"927 25288.751943 384686.167380 103 2020-12-22 \n",
|
|
"936 26576.334263 387715.763169 104 2020-12-29 \n",
|
|
"\n",
|
|
" token_reserve token_supply tx_volume volume_of_production \n",
|
|
"900 403467.174511 643118.467259 35156.692021 190.704716 \n",
|
|
"909 406233.506489 646687.282586 36211.790984 193.966675 \n",
|
|
"918 408709.559796 650220.409760 36802.566095 196.754422 \n",
|
|
"927 411726.229564 653718.205663 38653.219841 199.244554 \n",
|
|
"936 414982.125266 657181.023606 41543.209511 201.185891 "
|
|
]
|
|
},
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.tail()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1193712b0>"
|
|
]
|
|
},
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.plot(x='timestamp', y = [\"fiat_reserve\", \"token_reserve\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x11967cba8>"
|
|
]
|
|
},
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.plot(x='timestamp', y = \"operational_budget\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1196fd240>"
|
|
]
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf[\"price\"] = rdf.conversion_rate.apply(lambda r: 1/r)\n",
|
|
"rdf.plot(x='timestamp', y = \"price\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1197c37f0>"
|
|
]
|
|
},
|
|
"execution_count": 39,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.plot(x='timestamp', \n",
|
|
" y = \"volume_of_production\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x119887630>"
|
|
]
|
|
},
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.plot(x='timestamp', \n",
|
|
" y = \"cost_of_production\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x119111c50>"
|
|
]
|
|
},
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
|
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}
|
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],
|
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"source": [
|
|
"rdf.producer_roi_estimate.hist(bins=100)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>conversion_rate</th>\n",
|
|
" <th>cost_of_production</th>\n",
|
|
" <th>fiat_reserve</th>\n",
|
|
" <th>mech_step</th>\n",
|
|
" <th>operational_budget</th>\n",
|
|
" <th>overhead_cost</th>\n",
|
|
" <th>producer_roi_estimate</th>\n",
|
|
" <th>run</th>\n",
|
|
" <th>smooth_avg_fiat_reserve</th>\n",
|
|
" <th>smooth_avg_token_reserve</th>\n",
|
|
" <th>time_step</th>\n",
|
|
" <th>timestamp</th>\n",
|
|
" <th>token_reserve</th>\n",
|
|
" <th>token_supply</th>\n",
|
|
" <th>tx_volume</th>\n",
|
|
" <th>volume_of_production</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>9.606290</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>511.703711</td>\n",
|
|
" <td>1.100000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2019-01-08</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>25000.000000</td>\n",
|
|
" <td>1070.613127</td>\n",
|
|
" <td>20.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>1.034702</td>\n",
|
|
" <td>9.376439</td>\n",
|
|
" <td>24704.459029</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25764.055434</td>\n",
|
|
" <td>511.789835</td>\n",
|
|
" <td>1.360065</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24970.445903</td>\n",
|
|
" <td>25836.965540</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2019-01-15</td>\n",
|
|
" <td>33369.655400</td>\n",
|
|
" <td>34750.000000</td>\n",
|
|
" <td>1099.846197</td>\n",
|
|
" <td>22.250000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>1.100521</td>\n",
|
|
" <td>8.556163</td>\n",
|
|
" <td>24387.122926</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>26518.296349</td>\n",
|
|
" <td>511.842447</td>\n",
|
|
" <td>1.544795</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24912.113605</td>\n",
|
|
" <td>27416.292133</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2019-01-22</td>\n",
|
|
" <td>41630.231465</td>\n",
|
|
" <td>44402.500000</td>\n",
|
|
" <td>1165.814731</td>\n",
|
|
" <td>25.476431</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28</th>\n",
|
|
" <td>1.194015</td>\n",
|
|
" <td>8.441051</td>\n",
|
|
" <td>24078.990778</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>27263.007404</td>\n",
|
|
" <td>511.933639</td>\n",
|
|
" <td>1.685196</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24828.801323</td>\n",
|
|
" <td>29645.953569</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>2019-01-29</td>\n",
|
|
" <td>49712.906496</td>\n",
|
|
" <td>53958.475000</td>\n",
|
|
" <td>1243.923179</td>\n",
|
|
" <td>29.759003</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>37</th>\n",
|
|
" <td>1.311996</td>\n",
|
|
" <td>7.968585</td>\n",
|
|
" <td>23787.933002</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>27998.716672</td>\n",
|
|
" <td>512.039533</td>\n",
|
|
" <td>1.754430</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24724.714491</td>\n",
|
|
" <td>32438.717331</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2019-02-05</td>\n",
|
|
" <td>57573.591189</td>\n",
|
|
" <td>63418.890250</td>\n",
|
|
" <td>1305.756929</td>\n",
|
|
" <td>35.283746</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>46</th>\n",
|
|
" <td>1.451572</td>\n",
|
|
" <td>8.130411</td>\n",
|
|
" <td>23509.132739</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>28725.803252</td>\n",
|
|
" <td>512.136947</td>\n",
|
|
" <td>1.778724</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24603.156315</td>\n",
|
|
" <td>35713.244830</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>2019-02-12</td>\n",
|
|
" <td>65183.992318</td>\n",
|
|
" <td>72784.701347</td>\n",
|
|
" <td>1339.676883</td>\n",
|
|
" <td>42.139511</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>55</th>\n",
|
|
" <td>1.610184</td>\n",
|
|
" <td>8.153768</td>\n",
|
|
" <td>23231.705532</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>29444.309608</td>\n",
|
|
" <td>512.219048</td>\n",
|
|
" <td>1.746353</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24466.011237</td>\n",
|
|
" <td>39394.776936</td>\n",
|
|
" <td>7</td>\n",
|
|
" <td>2019-02-19</td>\n",
|
|
" <td>72528.565890</td>\n",
|
|
" <td>82056.854334</td>\n",
|
|
" <td>1370.584757</td>\n",
|
|
" <td>50.455342</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>64</th>\n",
|
|
" <td>1.785495</td>\n",
|
|
" <td>7.459641</td>\n",
|
|
" <td>22954.372820</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>30154.238665</td>\n",
|
|
" <td>512.304283</td>\n",
|
|
" <td>1.676996</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24314.847395</td>\n",
|
|
" <td>43414.049116</td>\n",
|
|
" <td>8</td>\n",
|
|
" <td>2019-02-26</td>\n",
|
|
" <td>79587.498740</td>\n",
|
|
" <td>91236.285791</td>\n",
|
|
" <td>1444.884644</td>\n",
|
|
" <td>60.208063</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>73</th>\n",
|
|
" <td>1.975161</td>\n",
|
|
" <td>6.782330</td>\n",
|
|
" <td>22691.386417</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>30856.034121</td>\n",
|
|
" <td>512.423659</td>\n",
|
|
" <td>1.598836</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>24152.501298</td>\n",
|
|
" <td>47705.069904</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>2019-03-05</td>\n",
|
|
" <td>86324.256993</td>\n",
|
|
" <td>100323.922933</td>\n",
|
|
" <td>1506.829271</td>\n",
|
|
" <td>71.323948</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>82</th>\n",
|
|
" <td>2.177047</td>\n",
|
|
" <td>6.643686</td>\n",
|
|
" <td>22436.708566</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>31549.952943</td>\n",
|
|
" <td>512.535491</td>\n",
|
|
" <td>1.516518</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>23980.922024</td>\n",
|
|
" <td>52207.587867</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>2019-03-12</td>\n",
|
|
" <td>92730.249539</td>\n",
|
|
" <td>109320.683703</td>\n",
|
|
" <td>1558.804847</td>\n",
|
|
" <td>83.795261</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>91</th>\n",
|
|
" <td>2.389261</td>\n",
|
|
" <td>6.003507</td>\n",
|
|
" <td>22183.730776</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>32236.047709</td>\n",
|
|
" <td>512.641401</td>\n",
|
|
" <td>1.420634</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>23801.202900</td>\n",
|
|
" <td>56867.286387</td>\n",
|
|
" <td>11</td>\n",
|
|
" <td>2019-03-19</td>\n",
|
|
" <td>98804.573063</td>\n",
|
|
" <td>118227.476866</td>\n",
|
|
" <td>1622.079699</td>\n",
|
|
" <td>97.585007</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>100</th>\n",
|
|
" <td>2.609987</td>\n",
|
|
" <td>5.618266</td>\n",
|
|
" <td>21935.667582</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>32914.470418</td>\n",
|
|
" <td>512.753509</td>\n",
|
|
" <td>1.328236</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>23614.649368</td>\n",
|
|
" <td>61633.937409</td>\n",
|
|
" <td>12</td>\n",
|
|
" <td>2019-03-26</td>\n",
|
|
" <td>104533.796602</td>\n",
|
|
" <td>127045.202098</td>\n",
|
|
" <td>1685.736680</td>\n",
|
|
" <td>112.474450</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" conversion_rate cost_of_production fiat_reserve mech_step \\\n",
|
|
"1 1.000000 9.606290 25000.000000 1 \n",
|
|
"10 1.034702 9.376439 24704.459029 1 \n",
|
|
"19 1.100521 8.556163 24387.122926 1 \n",
|
|
"28 1.194015 8.441051 24078.990778 1 \n",
|
|
"37 1.311996 7.968585 23787.933002 1 \n",
|
|
"46 1.451572 8.130411 23509.132739 1 \n",
|
|
"55 1.610184 8.153768 23231.705532 1 \n",
|
|
"64 1.785495 7.459641 22954.372820 1 \n",
|
|
"73 1.975161 6.782330 22691.386417 1 \n",
|
|
"82 2.177047 6.643686 22436.708566 1 \n",
|
|
"91 2.389261 6.003507 22183.730776 1 \n",
|
|
"100 2.609987 5.618266 21935.667582 1 \n",
|
|
"\n",
|
|
" operational_budget overhead_cost producer_roi_estimate run \\\n",
|
|
"1 25000.000000 511.703711 1.100000 1 \n",
|
|
"10 25764.055434 511.789835 1.360065 1 \n",
|
|
"19 26518.296349 511.842447 1.544795 1 \n",
|
|
"28 27263.007404 511.933639 1.685196 1 \n",
|
|
"37 27998.716672 512.039533 1.754430 1 \n",
|
|
"46 28725.803252 512.136947 1.778724 1 \n",
|
|
"55 29444.309608 512.219048 1.746353 1 \n",
|
|
"64 30154.238665 512.304283 1.676996 1 \n",
|
|
"73 30856.034121 512.423659 1.598836 1 \n",
|
|
"82 31549.952943 512.535491 1.516518 1 \n",
|
|
"91 32236.047709 512.641401 1.420634 1 \n",
|
|
"100 32914.470418 512.753509 1.328236 1 \n",
|
|
"\n",
|
|
" smooth_avg_fiat_reserve smooth_avg_token_reserve time_step timestamp \\\n",
|
|
"1 25000.000000 25000.000000 1 2019-01-08 \n",
|
|
"10 24970.445903 25836.965540 2 2019-01-15 \n",
|
|
"19 24912.113605 27416.292133 3 2019-01-22 \n",
|
|
"28 24828.801323 29645.953569 4 2019-01-29 \n",
|
|
"37 24724.714491 32438.717331 5 2019-02-05 \n",
|
|
"46 24603.156315 35713.244830 6 2019-02-12 \n",
|
|
"55 24466.011237 39394.776936 7 2019-02-19 \n",
|
|
"64 24314.847395 43414.049116 8 2019-02-26 \n",
|
|
"73 24152.501298 47705.069904 9 2019-03-05 \n",
|
|
"82 23980.922024 52207.587867 10 2019-03-12 \n",
|
|
"91 23801.202900 56867.286387 11 2019-03-19 \n",
|
|
"100 23614.649368 61633.937409 12 2019-03-26 \n",
|
|
"\n",
|
|
" token_reserve token_supply tx_volume volume_of_production \n",
|
|
"1 25000.000000 25000.000000 1070.613127 20.000000 \n",
|
|
"10 33369.655400 34750.000000 1099.846197 22.250000 \n",
|
|
"19 41630.231465 44402.500000 1165.814731 25.476431 \n",
|
|
"28 49712.906496 53958.475000 1243.923179 29.759003 \n",
|
|
"37 57573.591189 63418.890250 1305.756929 35.283746 \n",
|
|
"46 65183.992318 72784.701347 1339.676883 42.139511 \n",
|
|
"55 72528.565890 82056.854334 1370.584757 50.455342 \n",
|
|
"64 79587.498740 91236.285791 1444.884644 60.208063 \n",
|
|
"73 86324.256993 100323.922933 1506.829271 71.323948 \n",
|
|
"82 92730.249539 109320.683703 1558.804847 83.795261 \n",
|
|
"91 98804.573063 118227.476866 1622.079699 97.585007 \n",
|
|
"100 104533.796602 127045.202098 1685.736680 112.474450 "
|
|
]
|
|
},
|
|
"execution_count": 42,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pdf=df[df.mech_step==1].copy()\n",
|
|
"pdf.head(12)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x119a31518>"
|
|
]
|
|
},
|
|
"execution_count": 43,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rdf.token_supply.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|