Test

Before risking real capital, use Test to backtest your algorithm against historical data, review the trade-by-trade results, replay entries and exits, and run rolling cross-validation.

For a hands-on first test, see the Test Walkthrough →

On this page: Running a Backtest · Local Drafts · Fees & Slippage · Results · Replay & Compare · Optimize · Under the Hood

Note

Use at least 6 months of data. Shorter periods can produce misleading results — a strategy might look great over 2 weeks purely by luck.

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Running a Backtest

Open Test, select an algorithm, choose your setup, and click Run Backtest. Test uses the same strategy logic you built in Build, so it is the place to check the algorithm before you run it live.

Inputs

Parameter	Description
Algorithm	Saved algorithm and version to test
Start / End	Historical window for the backtest
Symbol	Market to test, such as BTC or ETH
Exchange	Exchange venue, usually Hyperliquid
Initial Capital	Simulated starting capital for this test
Leverage	Leverage assumption for the run
Trades Limit	Maximum number of trades to simulate
Session Calendar Policy	Whether the algorithm pauses around configured windows
Assume Position Now	Starts the test as if the strategy is already in trade
Close Position on Exit	Closes any open position at the end of the test window

Note

Quick Test from Build saves and compiles your algorithm, then opens Test with that algorithm already selected. You still control the date range, capital, and run settings before starting the backtest.

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Local Drafts

The Test workspace lets you make temporary edits without changing the saved algorithm.

Use the side rail to move between:

• Setup — market, dates, capital, leverage, trades limit, and session policy
• Entry — temporary entry condition edits
• Exit — temporary exit condition edits
• Reset — reset and follow-up behavior

Draft edits are useful for quick experiments. When you find an improvement you want to keep, go back to Build and save a new algorithm version.

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Fees & Slippage

Cost	Default	Description
Fees	Exchange-specific (bps)	Deducted from trade value on entry and exit
Slippage	Exchange-specific (bps)	Price impact — longs slip up on entry, down on exit; shorts the reverse

Both are applied to every trade. Position size is calculated after deducting entry fees and slippage.

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Results

Key Metrics (Check These First)

These five metrics give you the fastest read on whether your strategy is working:

Metric	What It Tells You	Good Range
winRate	Percentage of winning trades	> 40% (depends on R:R)
profitFactor	Gross profit / gross loss	> 1.5 (> 2.0 is strong)
sharpeRatio	Risk-adjusted return (annualized return / volatility)	> 1.0 (> 2.0 is strong)
maxDrawdownPct	Largest peak-to-trough equity decline	< 20% (lower is better)
totalPnlUSD	Net profit/loss in USD	Positive, obviously

Note

Ask yourself: Sharpe > 1.0? Max drawdown < 20%? Profit factor > 1.5? If all three check out, your strategy has a solid foundation. If not, revisit your indicators and thresholds before optimizing.

Full Metrics Reference

Returns

Metric	Description
totalTrades	Total completed trades
winningTrades / losingTrades	Profitable vs unprofitable trades
totalPnlUSD	Net profit/loss in USD
grossProfitUSD / grossLossUSD	Sum of winning / losing trade P&L
avgPnlUSD	Average P&L per trade
avgWinUSD / avgLossUSD	Average winning / losing trade
largestWinUSD / largestLossUSD	Best and worst single trade

Risk-Adjusted

Metric	Description
sharpeRatio	Annualized return / volatility. > 1.0 is good, > 2.0 is strong
sortinoRatio	Like Sharpe but only penalizes downside volatility — better for asymmetric strategies
calmarRatio	Annualized return / max drawdown — how well you’re compensated for worst-case pain
maxDrawdownPct	Largest peak-to-trough equity decline (%)
maxDrawdownUSD	Largest peak-to-trough equity decline (USD)

Trade Breakdown

Metric	Description
longTrades / shortTrades	Count by direction
longWinRate / shortWinRate	Win rate by direction
longPnlUSD / shortPnlUSD	P&L by direction
avgTradeDurationBars / avgTradeDurationSeconds	Average trade length
avgWinDurationBars / avgLossDurationBars	Duration of winners vs losers
totalFeesUSD / totalSlippageUSD	Total execution costs

Indicator Analysis

Each indicator gets a usefulness score (0-100) that tells you whether it’s actually contributing to your strategy’s decisions — or just adding noise.

Field	What It Tells You
flipCount	How many times the signal changed
flipRate	Flips per bar
avgDurationTrueBars / avgDurationFalseBars	Average length of true/false periods
pctTimeTrue	Percentage of time the signal was true
triggeringFlipCount	Flips that actually triggered a condition change
blockingCount	Times this indicator prevented a condition from triggering
usefulnessScore	Composite score (0-100)

Entry scoring weights: entropy (20%) + efficiency (35%) + criticality (30%) + bottleneck (15%)

Exit scoring weights: entropy (25%) + selectivity (40%) + signal density (35%)

Note

A low usefulness score means the indicator rarely changes the outcome. Consider removing it to simplify your strategy — simpler strategies tend to generalize better.

Near-Miss Analysis

Shows how close conditions came to triggering without actually firing. This is valuable when your strategy isn’t trading enough — near-misses tell you which thresholds are too tight and by how much.

State Distribution

Field	Description
pctTimeFlat	Time with no position
pctTimeLong / pctTimeShort	Time in long/short positions
avgTimeFlatBars	Average bars between trades

Exit Reason Breakdown

Reason	Description
signal	Exit condition triggered
stopLoss	Fixed SL hit
stopLossTrailing	Trailing SL hit
takeProfit	TP level hit
endOfBacktest	Position open at backtest end

Note

If most exits are stop losses, your entry timing may be off — consider adjusting entry thresholds or widening stops. If most exits are signals, your exit logic is driving results, which is usually healthier.

Equity Curve

Event-driven snapshots of portfolio value — one data point per trade entry, exit, SL, or TP:

Field	Description
timestamp	UTC timestamp
equity	Total portfolio value
drawdownPct	Current drawdown from peak

Use plot_backtest to generate an equity curve chart (PNG) with drawdown overlay.

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Replay & Compare

Use replay controls to step through the test and see when signals, entries, exits, stops, and take profits happened on the chart.

You can also keep completed runs around for comparison. This makes it easier to answer practical questions like:

• Did the new entry rule improve win rate?
• Did the stop loss reduce drawdown?
• Did the algorithm improve because it traded better, or only because it traded less?

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Optimize: Rolling Cross-Validation

Switch the Test workspace from Backtest to Optimize when you want to check whether a strategy generalizes across time. Optimize runs rolling cross-validation: each fold trains on one window and tests on the next unseen window.

Caution

Optimize uses the saved strategy. Local draft edits, per-run capital overrides, and session policy changes from Backtest are not applied to rolling cross-validation. Save the strategy in Build first if you want those changes included.

How It Works

|── Train ──|── Test ──|
     |── Train ──|── Test ──|
          |── Train ──|── Test ──|
               |── Train ──|── Test ──|

The date range is divided into overlapping folds. Each fold trains on one window and evaluates on the next unseen period. Windows step forward by step_days.

Settings

Setting	Description
Algorithm	Saved algorithm and version to validate
Symbol	Market to validate
Start / End	Full historical range
Train Days	Length of each training window
Test Days	Length of each out-of-sample test window
Step Days	How far the window slides forward between folds

Example

12-month backtest with 90-day train, 30-day test, 30-day step:

• Fold 1: Train Jan-Mar, Test Apr
• Fold 2: Train Feb-Apr, Test May
• Fold 3: Train Mar-May, Test Jun
• ~9 out-of-sample test periods

Results

What You See	What It Means	What to Do
Consistent Sharpe/win rate across folds	Strategy generalizes well	Consider a small live run
Much better train metrics than test	Likely overfit	Reduce parameter space, simplify, or add regularization
Later folds performing worse	Losing edge (regime change)	Re-optimize on recent data, or reconsider the strategy entirely

The Folds tab is the most important view. If the train windows look strong but the test windows are weak, the strategy may be tuned too tightly to history.

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Under the Hood: Simulation Pipeline

You don’t need to understand the pipeline to use backtesting, but knowing what happens helps you interpret edge cases and trust the results.

1. Data Loading

Validates inputs, loads historical candles, filters to date range, and computes warmup offset (extra bars needed for indicator calculation).

2. Sub-Bar Loading

Loads intra-bar price data for accurate stop loss and take profit detection. Falls back to OHLC interpolation if high-resolution data isn’t available.

3. MipMap Building

Aggregates candles into multiple timeframes (1m, 5m, 15m, 1h, 4h, 1d) for efficient multi-resolution indicator computation.

4. Indicator Calculation

Computes all indicator values across the full date range using the warmup period.

5. Resampling

Converts continuous indicator values into boolean signals based on signal type and threshold. Detects signal crossings — the exact bars where an indicator flips.

6. Event-Driven Simulation

Runs the state machine (CASH → POSITION → TIMEOUT → CASH) over resampled signals. On each bar:

• Checks entry/exit conditions (AND/OR logic across indicator groups)
• Scans for stop loss and take profit hits using sub-bar data
• Executes trades with fee and slippage deductions
• Tracks equity, drawdown, and trade records

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What’s Next?

Want to…	Go to
Take your strategy live	Run Features →
Tweak your algorithm settings	Build Features →
Walk through your first test	Test Walkthrough →