Best mean reversion settings for Hyperliquid

You can adjust Rebalance buffer, Washout strictness, Signal timeframe. The config explorer on this page lets you compare every setting we tested — including the ones that underperformed — with the exact backtested result for each.

mean-reversionmedium risk

Mean Reversion Strategy on Hyperliquid

Name: Mean Reversion Strategy on Hyperliquid — backtest results (Hyperliquid, 2024-08-15→2026-09-29)
Creator: Keel

Does mean reversion work on crypto perps? Backtested on real Hyperliquid perps from 2024-08-15 to 2026-09-29, the default settings earned a Sharpe of 0.23 — +4.5% total return with a 37.0% worst drawdown across 3,838 trades in 1,882 positions, net of fees, slippage, and funding.

By Keel Research · Data as of 2026-09-29 · how this backtest was produced

Sharpe

0.23

default settings

Total return

+4.5%

Worst drawdown

−37.0%

Trades

3,838

2024-08-15 → 2026-09-29 · net of fees, slippage, and funding · price-only Sharpe 0.30 · these are the default settings, not a hand-picked best case.

Run this strategy →Fork & customize

At $1,000, this strategy’s worst historical dip was about $370. Free account, your own Hyperliquid keys — Keel only trades.

How it works

Crypto perpetuals overshoot on forced liquidations and panic, then snap back toward their recent average. This strategy buys that snap-back. Each hour it scans the top-20 Hyperliquid perpetuals for washed-out large-caps: price has pierced below its lower Bollinger band (20, 2.0) and a 2-period RSI is deeply oversold — but only if the coin is still above its long-term trend line, so it buys dips inside uptrends rather than catching falling knives. Positions are equal-weight, 1x gross, long-only, and exit as the oversold condition clears.

When it works best

Choppy, range-bound markets with frequent small reversals, and on the one-hour timeframe, where these brief oversold snaps happen often enough to trade. The trend filter keeps it on the long side of coins that are still structurally rising. The current window has not been that market: over 2024-08-15 to 2026-09-29 the default one-hour settings returned +4.5% with a Sharpe of 0.23 and a maximum drawdown of −37.0%, net of fees, slippage, and funding, across 3,838 trades — weak, with the bounces it buys barely covering its costs.

When it struggles

Sustained one-way selloffs and breakouts, where an oversold reading just gets more oversold and the bounce never comes. The strict entry conditions also fire rarely, so there are long stretches with no trades — on slower timeframes they barely co-occur at all, which is why it runs on one-hour bars. It needs a broad enough universe, too: concentrating on too few coins removes the diversification the approach depends on.

How it’s built

The exact strategy behind this backtest — no black box. Switch to code to see or copy the full definition.

Configuration

Globalsconfig

target_timeframe1h

UniverseassetsTop by Volume · HL Perps · 20 assets20 resolved

Executiontrading

rebalanceBufferedbuffer_threshold0.1buffer_moderelative+1

Factories

trend_vs_200factory

Pipeline7 steps

PriceDataLoaderData Loader

ohlcvstore

Parallel2 branches

entries

Parallel2 branches

const

ConstantForecastForecast Mapper

value1

setup

Parallel3 branches

oversold

BollingerBandsIndicator

period20num_std2

BelowThresholdFilterSignal Transform

threshold0.05inclusivetrue

rsi_washed

RSIIndicator

period2

BelowThresholdFilterSignal Transform

threshold10inclusivetrue

uptrend

trend_vs_200call

AboveThresholdFilterSignal Transform

threshold0

MaskAndSignal Composer

ApplyMaskSignal Composer

score_signalconstfilter_signalsetup

mr_entrystore

exits

MeanReversionDistanceIndicator

sma_period20atr_period14

ThresholdCrossSignal Transform

upper0modelong_only

mr_exitstore

TradeManagerPosition Manager

entriesmr_entrypricesohlcv

Parallel1 branches

back_to_mean

mr_exitload

AboveThresholdFilterSignal Transform

threshold1inclusivetrue

ExitPosition Manager

ExposurePosition Manager

EqualWeightSizerPosition Sizer

target_leverage1max_weight0.2

Globals(target_timeframe="1h")

Universe(mode="top_volume", top_n=20, market="perp", resolved=["BNB", "BTC", "DOGE", "ETH", "FARTCOIN", "GRAM", "HYPE", "LINK", "LIT", "NEAR", "PUMP", "SOL", "SUI", "VVV", "WLD", "XPL", "XRP", "ZEC", "kBONK", "kPEPE"], resolved_at="2026-07-06T20:53:28.722475+00:00")

Execution(
    rebalance="buffered",
    buffer_threshold=0.1,
    buffer_mode="relative",
    rebalance_method="to_edge",
)

trend_vs_200 = Pipeline([
    {
        "px": [EWMA(window=2)],
        "trend": [EWMA(window=200)],
    },
    Crossover(fast_key="px", slow_key="trend"),
], name="trend_vs_200")

Pipeline([
    PriceDataLoader(),
    Store("ohlcv"),
    {
        "entries": [
            Pipeline([
                {
                    "const": [ConstantForecast(value=1.0)],
                    "setup": [
                        Pipeline([
                            {
                                "oversold": [BollingerBands(period=20, num_std=2.0), BelowThresholdFilter(threshold=0.05, inclusive=True)],
                                "rsi_washed": [RSI(period=2), BelowThresholdFilter(threshold=10.0, inclusive=True)],
                                "uptrend": [trend_vs_200, AboveThresholdFilter(threshold=0.0)],
                            },
                            MaskAnd(),
                        ], name="setup"),
                    ],
                },
                ApplyMask(score_signal="const", filter_signal="setup"),
                Store("mr_entry"),
            ], name="entries"),
        ],
        "exits": [MeanReversionDistance(sma_period=20, atr_period=14), ThresholdCross(upper=0.0, mode="long_only"), Store("mr_exit")],
    },
    TradeManager(entries="mr_entry", prices="ohlcv"),
    {
        "back_to_mean": [Load("mr_exit"), AboveThresholdFilter(threshold=1.0, inclusive=True), Exit()],
    },
    Exposure(),
    EqualWeightSizer(target_leverage=1.0, max_weight=0.2),
], name="mean_reversion_hyperliquid")

Globals(target_timeframe="1h")Universe(mode="top_volume", top_n=20, market="perp", resolved=["BNB", "BTC", "DOGE", "ETH", "FARTCOIN", "GRAM", "HYPE", "LINK", "LIT", "NEAR", "PUMP", "SOL", "SUI", "VVV", "WLD", "XPL", "XRP", "ZEC", "kBONK", "kPEPE"], resolved_at="2026-07-06T20:53:28.722475+00:00")Execution(    rebalance="buffered",    buffer_threshold=0.1,    buffer_mode="relative",    rebalance_method="to_edge",)trend_vs_200 = Pipeline([    {        "px": [EWMA(window=2)],        "trend": [EWMA(window=200)],    },    Crossover(fast_key="px", slow_key="trend"),], name="trend_vs_200")Pipeline([    PriceDataLoader(),    Store("ohlcv"),    {        "entries": [            Pipeline([                {                    "const": [ConstantForecast(value=1.0)],                    "setup": [                        Pipeline([                            {                                "oversold": [BollingerBands(period=20, num_std=2.0), BelowThresholdFilter(threshold=0.05, inclusive=True)],                                "rsi_washed": [RSI(period=2), BelowThresholdFilter(threshold=10.0, inclusive=True)],                                "uptrend": [trend_vs_200, AboveThresholdFilter(threshold=0.0)],                            },                            MaskAnd(),                        ], name="setup"),                    ],                },                ApplyMask(score_signal="const", filter_signal="setup"),                Store("mr_entry"),            ], name="entries"),        ],        "exits": [MeanReversionDistance(sma_period=20, atr_period=14), ThresholdCross(upper=0.0, mode="long_only"), Store("mr_exit")],    },    TradeManager(entries="mr_entry", prices="ohlcv"),    {        "back_to_mean": [Load("mr_exit"), AboveThresholdFilter(threshold=1.0, inclusive=True), Exit()],    },    Exposure(),    EqualWeightSizer(target_leverage=1.0, max_weight=0.2),], name="mean_reversion_hyperliquid")

Explore the settings

precomputed · updates instantly

Adjust a setting to see the exact backtested result — including the ones that lost money.

Complete 18-cell public cross-product on explicit resolved universes.

Washout strictness

Signal timeframe

Rebalance buffer

Sharpe

0.23

Return

4.5%

Worst DD

−37.0%

Trades

3,838

Heads up: weaker risk-adjusted return.

Run these settings →Fork & customize

At $1,000, these settings' worst historical dip was about $370. You land in the editor with this exact setup. Free account, your own keys.

Compare all 18 settings

Settings	Sharpe	Return	Worst DD	Trades
Washout strict · Signal 1h · Rebalance 0.05	0.18	0.6%	−37.7%	4,381
Washout strict · Signal 1h · Rebalance 0.1	0.20	2.4%	−35.5%	3,445
Washout strict · Signal 1h · Rebalance 0.2	0.24	5.7%	−31.1%	2,793
Washout strict · Signal 4h · Rebalance 0.05	0.12	-3.2%	−38.8%	1,170
Washout strict · Signal 4h · Rebalance 0.1	0.11	-3.4%	−37.4%	982
Washout strict · Signal 4h · Rebalance 0.2	0.11	-2.5%	−34.0%	799
Washout reference · Signal 1h · Rebalance 0.05	0.20	2.1%	−39.0%	4,870
Washout reference · Signal 1h · Rebalance 0.1default	0.23	4.5%	−37.0%	3,838
Washout reference · Signal 1h · Rebalance 0.2	0.27	7.7%	−32.8%	3,074
Washout reference · Signal 4h · Rebalance 0.05	0.13	-3.0%	−38.4%	1,323
Washout reference · Signal 4h · Rebalance 0.1	0.11	-3.4%	−37.3%	1,132
Washout reference · Signal 4h · Rebalance 0.2	0.12	-2.4%	−34.8%	889
Washout loose · Signal 1h · Rebalance 0.05	-0.38	-38.6%	−64.0%	7,662
Washout loose · Signal 1h · Rebalance 0.1	-0.36	-36.0%	−62.1%	6,050
Washout loose · Signal 1h · Rebalance 0.2	-0.32	-30.8%	−57.6%	4,659
Washout loose · Signal 4h · Rebalance 0.05	0.27	6.7%	−38.5%	1,827
Washout loose · Signal 4h · Rebalance 0.1	0.26	6.2%	−37.7%	1,583
Washout loose · Signal 4h · Rebalance 0.2	0.27	7.6%	−35.7%	1,199

The data

Monthly returns

Month	Return
2024-08	0.0%
2024-09	+3.0%
2024-10	+4.7%
2024-11	+30.3%
2024-12	+4.2%
2025-01	+12.0%
2025-02	-8.9%
2025-03	-7.6%
2025-04	+17.7%
2025-05	-1.0%
2025-06	-5.6%
2025-07	-4.8%
2025-08	-0.0%
2025-09	-14.8%
2025-10	+15.8%
2025-11	+1.8%
2025-12	-4.1%
2026-01	+0.7%
2026-02	-0.1%
2026-03	-7.1%
2026-04	+3.8%
2026-05	-2.5%
2026-06	-13.8%
2026-07	-2.9%
2026-08	+7.4%
2026-09	-7.1%

Which assets it traded

Avg allocation	Days held	% of time held	Asset
0.169	2172	11.68	BNB
0.1596	2086	11.22	BTC
0.1586	1977	10.63	SOL
0.1529	1955	10.51	LINK
0.1575	1823	9.8	ETH
0.1557	1796	9.66	NEAR
0.1554	1776	9.55	SUI
0.1537	1766	9.49	WLD
0.1498	1750	9.41	DOGE
0.1726	1724	9.27	HYPE
0.1546	1661	8.93	XRP
0.1765	1478	7.95	VVV
0.1582	1296	6.97	FARTCOIN
0.1762	1288	6.92	ZEC
0.1493	1145	6.16	kBONK
0.1469	985	5.3	kPEPE
0.1654	731	3.93	XPL
0.1734	589	3.17	LIT
0.1498	188	1.01	PUMP
0.1562	134	0.72	GRAM

What this is: a historical backtest on real Hyperliquid market data, net of fees, slippage, and funding. Its worst historical drawdown was 37.0% — expect drawdowns of that order or worse. Past performance does not predict future results, and this is not investment advice. Size your account so a full drawdown is survivable.

Mean Reversion Strategy on Hyperliquid

How it works

How it works

When it works best

When it struggles

How it’s built

Configuration

Factories

Explore the settings

The data

Monthly returns

Which assets it traded

Common questions

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