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The Simulator

The Simulator evaluates the meta-model's aggregate predictions as simulated long/short perpetual-futures portfolios over historical Hyperliquid market data. It allows you to test portfolio construction rules, optimizers, rebalancing frequencies, and market frictions against the community signal.

You can use the Simulator interactively in your browser without writing code, or programmatically through the Python client and MCP tools.

Portfolio construction knobs

The simulator supports several configuration parameters:

  • Cohort sizing (n_long, n_short): Number of top-ranked assets bought long and bottom-ranked assets sold short (1 to 100 names per leg).
  • Cadence and rolling tranches (rebalance_days): Rebalancing intervals of 1, 5, 10, or 30 days. Values ending in t (5t, 10t, or 30t) use tranched rolling vintages. Tranched execution splits the portfolio into equal daily sub-cohorts to eliminate single-day rebalance timing luck.
  • Optimizers (optimizer): equal (equal dollar allocation across assets), inv_vol (inverse 30-day volatility, Challenger tier), or hrp (Hierarchical Risk Parity, Challenger tier). Higher tiers unlock covariance optimizers and conviction-weighted sizing; the API's capabilities endpoint lists the optimizer keys and knobs your tier unlocks.
  • Frictions and carry costs (include_funding, fee_bps, impact_book): Incorporates hourly perpetual funding rates (on by default), exchange transaction fees (0 to 20 bps per side, 3.5 by default), and order book market impact models for an assumed book size.
  • Sizing (leverage, target_vol): One decision on the whole book, separate from its shape. leverage (0.25 to 3.0, default 1.0) is the gross book as a multiple of equity. A vol target (target_vol, annualized, 0 = off) adapts the multiple to hold realized volatility near the target, never above leverage, so the two compose as a ceiling, not a product. Leverage is not free in the backtest: funding, fees and impact scale with gross, and a book whose gross drifts past the venue's maintenance margin (6x equity, the modal Hyperliquid perp) is liquidated, booked as a total loss and reported as liquidated_on in the stats.
  • Risk and liquidity filters: Optimizer risk window (risk_lookback: 10 to 365 days of trailing returns, 0 = per-optimizer default), signal lag (signal_lag, 0 to 14 days), minimum open interest (min_oi), and minimum daily trading volume (min_volume). Continuous knobs snap to the step the capabilities listing reports.

Evaluation metrics and reports

Every backtest produces performance metrics split between full-period, in-sample (is_stats), and out-of-sample holdout (oos_stats) data:

  • Core statistics: Annualized Sharpe ratio, Sortino ratio, CAGR, maximum drawdown, annualized volatility, and average gross. A path that reached zero equity (ruined_on) or was liquidated (liquidated_on) reports total return and drawdown only; its annualized ratios are withheld.
  • Optional breakdown series (include): Daily NAV curve (curve), current target weights (holdings), monthly returns table (monthly), and per-asset P&L attribution (contributions).
  • Null benchmarks (benchmark_trials): Compares strategy performance against up to 100 random-ranking portfolios with identical construction rules to test whether returns exceed chance.

Parameter sweeps and multi-sleeve blends

  • Parameter sweeps (run_sweep): Evaluates a grid of configurations (e.g., testing multiple cohort sizes against different rebalance cadences). When analyzing sweep results, look for stable parameter plateaus across out-of-sample data rather than isolated point peaks.
  • Multi-sleeve blends (run_blend): Nets several weighted simulation configurations into one book, marked as one account (offsetting positions cancel before they are charged), and returns an inter-sleeve correlation matrix. Sizing belongs to the blend, not its sleeves: sleeves run at natural gross and the netted book is sized once, by the leverage and target_vol the sleeves share (the web chart keeps every charted configuration on the knob bar's pair; the API reads the first sleeve's).

Tier unlocks and parameter clamping

Simulator capabilities scale with your CC Points tier:

Tier Simulator features
Everyone Backtesting on 90-day delayed meta-model data
Challenger (100+ points) Real-time meta-model data*, Inverse-Vol & HRP optimizers, parameter sweeps
Contender (500+ points) Covariance optimizers, leverage and volatility targeting, market impact scaling, expanded sweep (up to 96 cells) and blend budgets (up to 5 sleeves)
Centurion (1,500+ points) Conviction-weighted sizing and classified alpha controls

*Real-time meta-model data at any tier requires a submission in the last 30 days. Without one, data falls back to a 90-day delay.

If a configuration specifies a parameter above your current tier, the server automatically clamps the value to your highest accessible tier rather than failing the request. Clamped parameters are listed in the locked field of the response.

Python quickstart

from crowdcent_challenge import ChallengeClient

client = ChallengeClient("hyperliquid-ranking")

# 1. Backtest a single configuration
result = client.run_simulation(
    config={
        "n_long": 10,
        "n_short": 10,
        "optimizer": "inv_vol",
        "rebalance_days": "10t",
        "include_funding": True,
    },
    include=["curve", "holdings"],
    benchmark_trials=25,
)

print(f"In-sample Sharpe: {result['is_stats']['sharpe']:.2f}")
print(f"Out-of-sample Sharpe: {result['oos_stats']['sharpe']:.2f}")
print(f"Web URL: {result['web_url']}")

# 2. Grid-search across multiple parameters
sweep = client.run_sweep(
    config={"n_short": 10, "optimizer": "inv_vol", "include_funding": True},
    sweep={"n_long": [5, 10, 20], "rebalance_days": ["5t", "10t", "30t"]},
)

for cell in sweep["results"]:
    print(cell["params"], "OOS Sharpe:", cell["oos_stats"]["sharpe"])

# 3. Blend weighted sleeves into an ensemble portfolio
blend = client.run_blend(
    sleeves=[
        {
            "config": {"n_long": 5, "n_short": 10, "rebalance_days": "30t"},
            "weight": 0.6,
            "label": "Slow Trend",
        },
        {
            "config": {"n_long": 10, "n_short": 10, "rebalance_days": "5t"},
            "weight": 0.4,
            "label": "Fast Rebalance",
        },
    ]
)

print("Composite Sharpe:", blend["stats"]["sharpe"])
print("Sleeve Correlations:", blend["correlation"])

Deploying to live trading

Once a portfolio strategy has been evaluated in the Simulator, you can deploy it as a mandate on Hyperliquid using Live Trading (Challenger tier and above).

Simulator Disclaimer

Simulations are provided for informational and educational purposes only. Not financial, investment, or trading advice. Simulated performance is not indicative of future results. Perpetual futures are leveraged instruments and you can lose your entire margin. See the full disclaimer.