Better tx distribution - 52 weeks down-sampling
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dapplion
parent
2cc32d5764
commit
ac451b34af
172
src/App.tsx
172
src/App.tsx
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@ -21,7 +21,10 @@ import {
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getInitialParams,
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getPriceR,
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getRandomDeltas,
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getSlippage
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getSlippage,
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getTxDistribution,
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getDeltaR_priceGrowth,
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rv_U
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} from "./math";
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import { throttle } from "lodash";
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// General styles
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@ -42,6 +45,9 @@ const useStyles = makeStyles((theme: Theme) =>
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},
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paddingBottom: theme.spacing(9)
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},
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simulationContainer: {
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minHeight: "442px"
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},
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paper: {
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width: "100%",
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height: "100%",
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@ -72,6 +78,15 @@ const useStyles = makeStyles((theme: Theme) =>
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paddingRight: "5px",
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paddingLeft: "5px"
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},
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boxPlaceholder: {
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padding: theme.spacing(3, 3),
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display: "flex",
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height: "100%",
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alignItems: "center",
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justifyContent: "center",
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color: theme.palette.text.secondary,
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opacity: 0.4
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},
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header: {
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backgroundColor: "#0b1216",
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color: "#f8f8f8",
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@ -102,7 +117,7 @@ export default function App() {
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* to re-render too often
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*/
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const setCurveParamsThrottle = useMemo(
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() => throttle(setCurveParams, 1000),
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() => throttle(setCurveParams, 250),
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[]
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);
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@ -124,11 +139,15 @@ export default function App() {
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const [totalReserve, setTotalReserve] = useState(R0);
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const [withdrawCount, setWithdrawCount] = useState(0);
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const [avgSlippage, setAvgSlippage] = useState(0);
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const [avgTxSize] = useState(10000);
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const [avgTxSize, setAvgTxSize] = useState(0);
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// Simulation state variables
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const [simulationActive, setSimulationActive] = useState(false);
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const [simulationRunning, setSimulationRunning] = useState(false);
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useEffect(() => {
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setSimulationActive(false);
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}, [curveParams]);
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// #### TEST: Immediate simulation
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async function startSimulation() {
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@ -157,47 +176,52 @@ export default function App() {
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const p_t: number[] = [getPriceR({ R: R0, V0, k })];
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const wFee_t: number[] = [0];
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const slippage_t: number[] = [];
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const avgTxSize_t: number[] = [];
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// Random walk
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const numSteps = 100;
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const updateEveryNth = 1;
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// Compute the random deltas
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const deltaR_t = getRandomDeltas({ numSteps, avgTxSize });
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const numSteps = 52;
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// numSteps = 52 take 8ms to run
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setSimulationRunning(true);
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for (let i = 0; i < numSteps; i++) {
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const deltaR = deltaR_t[i];
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for (let t = 0; t < numSteps; t++) {
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const txsWeek = Math.ceil(t < 5 ? rv_U(0, 5) : rv_U(5, 2 * t));
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const priceGrowth = rv_U(0.99, 1.03);
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const R = getLast(R_t);
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const deltaR = getDeltaR_priceGrowth({ R, k, priceGrowth });
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const R_next = R + deltaR;
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const txs = getTxDistribution({ sum: deltaR, num: txsWeek });
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// Compute slippage
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const slippage = getAvg(
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txs.map(txR => getSlippage({ R, deltaR: txR, V0, k }))
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);
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const txsWithdraw = txs.filter(tx => tx < 0);
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const wFees = -wFee * txsWithdraw.reduce((t, c) => t + c, 0);
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const _avgTxSize =
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txs.reduce((t, c) => t + Math.abs(c), 0) / txs.length;
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R_t.push(R_next);
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p_t.push(getPriceR({ R: R_next, V0, k }));
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slippage_t.push(getSlippage({ R, deltaR, V0, k }));
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// Consider withdraw fees on sales only
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if (deltaR < 0) {
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wFee_t.push(getLast(wFee_t) - deltaR * wFee);
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setWithdrawCount(c => c + 1);
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} else {
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wFee_t.push(getLast(wFee_t));
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}
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slippage_t.push(slippage);
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avgTxSize_t.push(_avgTxSize);
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wFee_t.push(getLast(wFee_t) + wFees);
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setWithdrawCount(c => c + txsWithdraw.length);
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// Stop the simulation if it's no longer active
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if (!simulationActive || !canContinueSimulation) break;
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if (i % updateEveryNth === 0) {
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setPriceTimeseries(p_t);
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setWithdrawFeeTimeseries(wFee_t);
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setAvgSlippage(getAvg(slippage_t));
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setTotalReserve(R_next);
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// Make this run non-UI blocking
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await pause(5);
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}
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}
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setPriceTimeseries(p_t);
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setWithdrawFeeTimeseries(wFee_t);
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setAvgSlippage(getAvg(slippage_t));
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setAvgTxSize(getAvg(avgTxSize_t));
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setTotalReserve(getLast(R_t));
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// Make this run non-UI blocking
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await pause(5);
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setSimulationRunning(false);
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}
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@ -210,7 +234,9 @@ export default function App() {
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const resultFields = [
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{
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label: `Average slippage (avg tx size ${avgTxSize} DAI)`,
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label: `Average slippage (avg tx size ${Math.round(
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avgTxSize
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).toLocaleString()} DAI)`,
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value: +(100 * avgSlippage).toFixed(3) + "%"
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},
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{
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@ -219,6 +245,10 @@ export default function App() {
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(+getLast(withdrawFeeTimeseries).toPrecision(3)).toLocaleString() +
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" DAI"
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},
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{
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label: `Capital collected from initial hatch`,
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value: Math.round(d0 * theta).toLocaleString() + " DAI"
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},
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{
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label: `Total reserve`,
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value: (+totalReserve.toPrecision(3)).toLocaleString() + " DAI"
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@ -300,44 +330,58 @@ export default function App() {
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</Grid>
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</Grid>
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{simulationActive && (
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<Grid container spacing={3}>
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<Grid item xs={12} sm={12} md={6} lg={8}>
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<Paper className={classes.paper}>
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<Box className={classes.boxHeader}>
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<Typography variant="h6">Simulation</Typography>
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<HelpText
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text={<span>Some context about this simulation</span>}
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/>
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</Box>
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<Grid container spacing={3} className={classes.simulationContainer}>
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{simulationActive ? (
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<>
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<Grid item xs={12} sm={12} md={6} lg={8}>
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<Paper className={classes.paper}>
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<Box className={classes.boxHeader}>
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<Typography variant="h6">Simulation</Typography>
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<HelpText
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text={<span>Some context about this simulation</span>}
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/>
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</Box>
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<Box className={classes.boxChart}>
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<PriceSimulationChart
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priceTimeseries={priceTimeseries}
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withdrawFeeTimeseries={withdrawFeeTimeseries}
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p0={p0}
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p1={p1}
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/>
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<Box className={classes.boxChart}>
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<PriceSimulationChart
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priceTimeseries={priceTimeseries}
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withdrawFeeTimeseries={withdrawFeeTimeseries}
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p0={p0}
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p1={p1}
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/>
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</Box>
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</Paper>
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</Grid>
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<Grid item xs={12} sm={12} md={6} lg={4}>
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<Paper className={classes.paper}>
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<Box className={classes.boxHeader}>
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<Typography variant="h6">Results</Typography>
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<HelpText
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text={
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<span>Explanation of what do this results mean</span>
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}
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/>
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</Box>
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<Box className={classes.box}>
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<ResultParams resultFields={resultFields} />
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</Box>
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</Paper>
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</Grid>
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</>
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) : (
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<Grid item xs={12}>
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<Paper className={classes.paper}>
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<Box className={classes.boxPlaceholder}>
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<Typography variant="h6">
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Run a simulation to see results
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</Typography>
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</Box>
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</Paper>
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</Grid>
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<Grid item xs={12} sm={12} md={6} lg={4}>
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<Paper className={classes.paper}>
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<Box className={classes.boxHeader}>
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<Typography variant="h6">Results</Typography>
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<HelpText
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text={<span>Explanation of what do this results mean</span>}
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/>
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</Box>
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<Box className={classes.box}>
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<ResultParams resultFields={resultFields} />
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</Box>
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</Paper>
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</Grid>
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</Grid>
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)}
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)}
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</Grid>
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</Container>
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</>
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);
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@ -55,7 +55,6 @@ function PriceSimulationChart({
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function ReferenceLabel(props: any) {
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const { textAnchor, viewBox, text } = props;
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console.log(props);
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return (
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<text
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x={viewBox.x + 10}
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61
src/math.ts
61
src/math.ts
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@ -53,6 +53,8 @@ export function getSlippage({
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return Math.abs(realizedPrice - spotPrice) / spotPrice;
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}
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// Price walk utils
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/**
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* Generate a random delta given three components:
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* 1. Climbing sin
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@ -79,3 +81,62 @@ export function getRandomDeltas({
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const deltaR_avg = getAvg(deltaR_t);
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return deltaR_t.map((deltaR: number) => (avgTxSize * deltaR) / deltaR_avg);
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}
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/**
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* Get deltaR for a given price growth factor
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*/
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export function getDeltaR_priceGrowth({
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R,
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k,
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priceGrowth
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}: {
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R: number;
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k: number;
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priceGrowth: number;
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}) {
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return -R + (priceGrowth * R ** (1 - 1 / k)) ** (k / (-1 + k));
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}
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/**
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* Computes a tx distribution using a normal distribution,
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* Given a sum of tx value and a number of transactions
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*
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* Demo: https://codepen.io/anon/pen/mNqJjv?editors=0010#0
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* Very quick: < 10ms for 10000 txs
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*/
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export function getTxDistribution({ sum, num }: { sum: number; num: number }) {
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const mean = sum / num;
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const off = mean * 4;
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const x: number[] = [];
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for (let i = 0; i < num; i++) {
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x[i] = randn_bm(mean - off, mean + off);
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}
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return x;
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}
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// Minor utils
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/**
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* Random variable uniformly distributed
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*/
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export function rv_U(min: number, max: number) {
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return Math.random() * (max - min) + min;
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}
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/**
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* Standard Normal variate using Box-Muller transform.
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* by https://stackoverflow.com/questions/25582882/javascript-math-random-normal-distribution-gaussian-bell-curve/36481059#36481059
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*/
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function randn_bm(min: number, max: number) {
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var u = 0,
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v = 0;
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while (u === 0) u = Math.random(); //Converting [0,1) to (0,1)
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while (v === 0) v = Math.random();
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let num = Math.sqrt(-2.0 * Math.log(u)) * Math.cos(2.0 * Math.PI * v);
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num = num / 10.0 + 0.5; // Translate to 0 -> 1
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if (num > 1 || num < 0) num = randn_bm(min, max); // resample between 0 and 1 if out of range
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num *= max - min; // Stretch to fill range
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num += min; // offset to min
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return num;
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}
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