Order-flow forecast

// A model that learns, bar by bar, what the next bars' return has followed: taker imbalance, the
// change in open interest and funding, all from 0xArchive's data. Each input is a z-score so the
// three weigh alike, and a missing one counts as 0. `ml.forward` holds each bar's inputs until their return is known, so the
// model never learns from the future. Skill is the correlation of each forecast with the return
// that followed it, over the last 500: the honest test of whether it knows anything.
indicator('Order-flow forecast', { shortName: 'OF forecast', precision: 1 });

const horizon = input.int('Bars ahead', 12, { min: 1, max: 200 });
const memory = input.float('Memory', 0.998, { min: 0.9, max: 1, step: 0.001, tooltip: 'Each older bar weighs this much less: 1 remembers every bar alike' });

const chart = bars();
const oi = openInterest();
const fund = funding();

const imbalance = stats.zscore(200);
const oiChange = stats.zscore(200);
const rate = stats.zscore(200);
const labels = ml.forward(horizon, 3);
const model = ml.rls(3, { forget: memory, ridge: 1 });
const graded = ml.forward(horizon, 1);
const skill = stats.corr(500);

const forecast = plot.histogram('Forecast', { unit: ' bp' });
const score = plot.line('Skill', { pane: 'skill', precision: 3 });
plot.level('No skill', 0, { pane: 'skill', style: 'dotted' });

chart.onClose(() => {
  const flow = chart.volume > 0 ? (chart.buyVolume - chart.sellVolume) / chart.volume : na;
  const built = oi.close({ usd: true }) > 0 ? oi.change({ usd: true }) / oi.close({ usd: true }) : na;
  // A missing reading (no funding on this venue, say) counts as usual: 0 standard deviations.
  const x = [nz(imbalance.update(flow)), nz(oiChange.update(built)), nz(rate.update(fund.rate({ hours: 8760 })))];
  // A label arrives `horizon` bars after its inputs; until then the model learns nothing (y is na).
  const done = labels.update(x, chart.close);
  model.update(done === null ? na : done.ret, done === null ? x : done.x);
  const guess = model.predict(x);
  forecast.set(guess * 10_000, guess >= 0 ? color.up : color.down);
  const back = graded.update([guess], chart.close);
  score.set(skill.update(back === null ? na : back.x[0], back === null ? na : back.ret));
});