Skip to main content
The Backtest Engine lets you simulate your strategy against historical market data — running real AI decisions on past candles, so you can see how your strategy would have performed before risking real capital. Unlike traditional backtests, Shekel’s engine uses the same AI model and strategy prompt you’d run live. At each decision point, the model sees real historical data and makes a decision exactly as it would in production.
Run a backtest before going live. A strategy that sounds good in plain English may behave differently than expected when the AI actually works through real market conditions.

​
How to Run a Backtest

1

Open Backtest

Tap Backtest in the navigation. Available in the web app at app.shekel.xyz/backtest and the mobile app.
2

Choose Your Agent (Optional)

Connect your Shekel API key to load your saved strategy, whitelist, and risk settings automatically. Or configure a custom strategy from scratch.
3

Configure the Run

  • Tokens — which assets to simulate trading
  • Date Range — the historical period to test (minimum 3 months recommended)
  • Interval — how often the AI makes decisions (matches your live agent’s run schedule)
  • Model — which AI model makes the decisions
  • Capital — starting portfolio value for the simulation
  • Data Sources — which data sources to include (same options as live)
4

Review the Credit Estimate

Before running, you’ll see an estimated credit cost. The estimate is conservative — actual cost is settled from real token usage after the run completes, and any over-reservation is refunded automatically.
5

Run

Tap Run Backtest. The engine runs in the cloud — you can close the app and return when it’s complete. Active backtests appear on the dashboard.

​
What Happens During a Backtest

The engine steps through time at your chosen interval. At each step:
  1. Historical market data is assembled for that exact timestamp (prices, order book, technicals, sentiment, etc.)
  2. Your custom endpoint is queried for data at that timestamp (if configured)
  3. The AI model reads the full context and decides: open (LONG/SHORT), add to a position, reduce (partial close), set or re-tune a take-profit ladder, manage exits (trail the stop, re-set targets), flip, close, or wait
  4. If the decision is actionable, the simulated trade executes at the historical price
  5. Portfolio state is updated (position sizes, P&L, drawdown)
This continues until the backtest period ends. The engine models scale-ins, reduces, flips, take-profit ladders, and stop adjustments identically to live, so a strategy that builds, trims, and manages exits behaves the same in simulation as in production.

​
Tips for Good Backtests

Use at least 3 months of data. Shorter windows may fit noise rather than signal — a strategy that “works” on a 2-week period isn’t meaningful. Test multiple models on the same strategy. Different AI models produce meaningfully different results. Claude Opus and Grok 4.3 may reason differently about the same setup. Watch for overfitting. If a strategy only works on one specific time period, it’s probably not robust. Test across different market regimes (trending, ranging, high-volatility). Compare against your live results. Once you’re live, use the agent chat to compare your live performance against backtest expectations. Divergence is signal — ask your agent why. Match your backtest settings to your live settings. Use the same model, interval, whitelist, and data sources. Differences between backtest and live configurations are a common source of confusion.

​
Rigorous Mode (5× range report)

A normal backtest is one run — but because the AI runs at a temperature above 0, a single run is just one of many possible outcomes (it can look great or poor by luck). Toggle 🎲 Rigorous (5×) on the config to run the exact same test 5 times and see the range of what could happen instead of one number. You get one card (with a live running mean P&L while the 5 run) and, when all finish, a Range Report:
  • Average and median return, and the full spread (worst → best run)
  • How many of the 5 runs were profitable, and the worst-case drawdown
  • A plain-English robustness verdict — Robust / Moderate / Fragile / Coin-flip
  • A stress floor (your worst run) so you judge the downside, not just the upside
A tight band around a positive number = a reliably good strategy; a wide band straddling zero = you got lucky (or unlucky) on a single run. Rigorous costs ~5× a normal run, so it’s a premium check best used right before you fund an agent.
Set Temperature above 0 before running Rigorous — at 0 the 5 runs cluster together and there’s little spread to measure.
See the Shekel Score on the Results page — the single live-readiness grade that comes out of a Rigorous set.

​
Pause & Resume long runs

Every running test’s card has a ⏸ Pause button. Pause saves the run’s full state at its next decision step, bills only what it used so far, and refunds the rest (like Stop, but with a bookmark). The card turns amber (Paused @ X%) and ▶ Resume continues the same test from the exact step it paused at. Paused runs stay resumable for 7 days; 🗑 Kill discards a paused run immediately. Rigorous packs pause/resume all 5 runs at once.

​
Dynamic Whitelist

The Modify Agent Settings panel on the Backtest page has a Dynamic Whitelist toggle. When on, instead of a fixed token list the engine re-ranks the crypto universe each cycle and rotates to the strongest-trending, most-liquid tokens (your whitelist becomes the starting seed). Config: Rotate Every, Max Slots, Min Score (0–10), Pinned, Max Churn/Cycle. Requires LLM Full mode.
The liquidity data behind the rotation only goes back to mid-May 2026. A dynamic-whitelist backtest that starts before then can’t pick tokens for the early window. For honest tests, start mid-May 2026 or later. “Approximate OI” can fill the gap for rough exploration of older periods, but those early results are estimates, not faithful.
The Momentum tab in the left nav shows the same universe ranking live (0–10 momentum score, LONG/SHORT bias, one-line thesis per token) — measuring each token’s strength independent of Bitcoin. It’s read-only and the same board your agent’s auto-rotating whitelist draws from.

​
Backtest History

Every completed backtest is automatically saved to your account. Your last 5 backtests are available to your agent in chat — ask it to analyze patterns across runs, compare models, or explain why live results differ. Pin one run as your gold standard (📌) and the agent measures new variants against it; if an engine upgrade could change results, a banner tells you to re-run the gold standard before comparing. Your backtest history is also accessible from Results in the app for detailed replay and analysis.