Freeze staking Monte Carlo simulation

freeze-staking-montecarlo.js

// Monte carlo simulation of long term staking

// Nodes distribution, regular, 5y, 10y
const CYCLES = 100000; // Increase this to run more simulations
const NODES = [1000, 1000, 1000]; // regular, 5y, 10y
const TARGET = 0.0012; // 0.12% of guesses win
// const COINAGE_EVERY = [24, 9, 5]; // 6 hours, 2h15m, 1h15m
const COINAGE_EVERY = [24, 24, 24]; // consistent
const SUBNODES = [2, 3, 4];

console.log('CYCLES', CYCLES);
console.log('NODES', NODES);
console.log('TARGET', TARGET);
console.log('COINAGE_EVERY', COINAGE_EVERY);
console.log();


const wins = [0, 0, 0];
const attempts = []; // successive failure attempts

// init
NODES.forEach((count, type) => {
  for (let node = 0; node < count; ++node) {
    for (let subnode = 0; subnode < SUBNODES[type]; ++subnode) {
      attempts[`${type}-${node}-${subnode}`] = 0;
    }
  }
});

for (let i = 0; i < CYCLES; ++i) {
  NODES.forEach((count, type) => {
    for (let node = 0; node < count; ++node) {
      for (let subnode = 0; subnode < SUBNODES[type]; ++subnode) {
        const subnodeKey = `${type}-${node}-${subnode}`;
        const target = (Math.floor(attempts[subnodeKey] / COINAGE_EVERY[type]) + 1) * TARGET;

        if (Math.random() <= target) {
          attempts[subnodeKey] = 0;
          ++wins[type];
        } else {
          ++attempts[subnodeKey];
        }
      }
    }
  });
}

console.log('Findings:', wins);

const rates = [0, 0, 0];

// Analyze results
wins.forEach((count, type) => {
  const pct = (count / (NODES[type] * CYCLES)) * 100;
  rates[type] = pct;
  const multiple = pct / rates[0];
  console.log(`Type ${type}: ${pct.toFixed(3)}% success rate. (${multiple.toFixed(3)}x)`);
});

results.txt

$ node freeze-staking-montecarlo.js
CYCLES 100000
NODES [ 1000, 1000, 1000 ]
TARGET 0.0012
COINAGE_EVERY [ 24, 24, 24 ]

Findings: [ 1206911, 1810558, 2413457 ]
Type 0: 1.207% success rate. (1.000x)
Type 1: 1.811% success rate. (1.500x)
Type 2: 2.413% success rate. (2.000x)

$ node freeze-staking-montecarlo.js
CYCLES 10000
NODES [ 1000, 1000, 1000 ]
TARGET 0.0012
COINAGE_EVERY [ 24, 24, 24 ]

Findings: [ 119901, 180127, 240000 ]
Type 0: 1.199% success rate. (1.000x)
Type 1: 1.801% success rate. (1.502x)
Type 2: 2.400% success rate. (2.002x)

$ node freeze-staking-montecarlo.js
CYCLES 1000
NODES [ 1000, 1000, 1000 ]
TARGET 0.0001
COINAGE_EVERY [ 24, 24, 24 ]

Findings: [ 2595, 3881, 5169 ]
Type 0: 0.260% success rate. (1.000x)
Type 1: 0.388% success rate. (1.496x)
Type 2: 0.517% success rate. (1.992x)

$ node freeze-staking-montecarlo.js
CYCLES 10000
NODES [ 1000, 1000, 1000 ]
TARGET 0.0001
COINAGE_EVERY [ 24, 24, 24 ]

Findings: [ 32318, 48721, 64973 ]
Type 0: 0.323% success rate. (1.000x)
Type 1: 0.487% success rate. (1.508x)
Type 2: 0.650% success rate. (2.010x)