Turn your ideas into a set of clear rules, then replay them against historical price data with realistic costs. See how your rules would have performed — including the losing streaks every rulebook has — before risking real capital.
Disclaimer: Trading financial instruments involves risk. Simulated and historical results do not guarantee future performance, and this page is for education only — not financial advice.
To keep results accurate, confirm the basic parameters below before building rules — changing them resets the whole workspace.
Each template is a complete plan — symbol, capital, position sizing and rules (entry + exit) all filled in. Load one and you can run it immediately.
Nothing is locked: every number stays editable. Each card also tells you where the idea breaks down — which matters more than how it looks on history.
Page 1 of 4 · 46 templates
A carefully stacked 3-condition entry that looks brilliant on history. Import it, then take it to the Training Ground and watch it fall apart.
Change any value, then load the template into the editor below.
Where it breaks: Out-of-sample data. Every extra condition was tuned to one specific stretch of the past — that is overfitting, and the blind test is designed to expose it.
Bets on short-term trend continuation: enters only when an MA cross aligns with a Donchian breakout, banking quick profits in trending legs while rangebound chop bleeds capital through repeated stop-outs.
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Where it breaks: Proprietary indicator; martingale position sizing; time-of-day session
Bets on reversals at the opening range edges: fade the failed push, collect spreads in choppy opens, and get stopped out repeatedly when the open trends.
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Where it breaks: Cannot anchor the intraday opening range, only a rolling-window approximation is possible; candlestick geometry conditions cannot be expressed; order expiry and the daily trade-count cap belong to order management, which the engine does not support
Bets on triple confirmation (MA, RSI, MACD) before entering and rides trends; signals lag, so repeated crossovers bleed capital in range markets.
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Where it breaks: Not reproduced: relative strength across multiple symbols, weighted composite scoring.
Bets on momentum continuation: long on a moving-average golden cross and out on the death cross, with RSI crossing above price as confirmation; sideways markets churn out repeated losses.
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Where it breaks: Pattern recognition; multi-timeframe
Bets on which entry signal works best: enter when any indicator crosses above, exit below the moving average; trends pay, while chop breeds false signals.
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Where it breaks: Random-entry control; cost model; experiment framework
Bets on oversold reversion: buys only when RSI is oversold and price breaks the lower Bollinger Band, while ADX above 25 filters out trending markets; in strong trends it gets stuck in losing positions.
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Where it breaks: Multi-instrument hedge; multi-timeframe; exotic indicators (implied correlation / OU model)
Bets on reversal after a false breakout: only enters once price sweeps a Donchian boundary and snaps back; a genuine breakout that keeps running leaves it trapped.
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Where it breaks: Pattern recognition; session time; order flow
Bets on trend continuation after CCI crosses its threshold: rides directional moves, but repeated crossings near the threshold cause whipsaw losses.
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Where it breaks: Dynamic parameters from a discretionary lookback window; swing point recognition
Bets on Bollinger band reversion: buy the lower band and exit at the moving average in ranging markets; a sustained downtrend triggers repeated stop-outs.
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Where it breaks: Missing indicator: VWAP; unsupported order type: limit
Bets on trend continuation after breakout: enters only on a break above the Donchian upper band; frequent false breakouts in rangebound markets cause repeated stop-outs.
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Where it breaks: session-anchored indicator; multi-stage state machine
Bets on volume-price alignment: enters when OBV crosses above its MA and price breaks a 20-bar high; divergence or frequent false breakouts cause repeated stop-outs.
Change any value, then load the template into the editor below.
Where it breaks: custom indicator; order flow
Choose the symbol you want to study, your starting capital, and how much to risk on each trade. Fees and slippage are real numbers — what you see is what the market would have charged.
Two sentences are enough to start: when to get in, and when to get out. Stack conditions like building blocks — no code, and every rule reads back to you in plain language.
Run the plan over real price history and see what actually happened — the equity curve, the drawdown, and every entry and exit marked on the chart. Judge it by the numbers, not by how convincing the idea sounds.
Crypto contract verification applies that discipline to perpetual futures: the rule is written down first, then replayed across many non-overlapping windows of the same history and read as a distribution instead of a single run. Perpetual markets trade around the clock, can be shorted with leverage, and charge a funding rate — so costs and liquidation carry as much weight as return.
Perpetual contracts
Positions can be long or short, with leverage and a funding rate exchanged between the two sides. Liquidation is a real outcome, not a theoretical worst case.
A market that never closes
Crypto trades continuously, so overnight, weekend and holiday moves are all part of the data — there is no closing bell to hide behind.
Write the rule down first
Decide entry, exit and position size before opening the workspace, and pick a liquid contract so simulated fills stay plausible.
Set costs that reflect a real venue
Commission, slippage and the funding rate are applied per fill and per funding interval, so a rule that only works with zero costs does not appear profitable.
Replay across many windows
The same history is cut into non-overlapping windows and replayed repeatedly with different boundaries, so no single split decides the conclusion.
Read it against holding
Every result is shown next to simply holding the contract over the same windows — worth knowing before spending more time on the rule.
Choose the market
Pick the perpetual contract and the timeframe you want to study, from one-minute bars upwards.
Write the rules down
Entry, exit and position rules — including whether leverage is used — are set before the run starts.
Run the verification
The engine replays the rule across every window of the stored history, with costs and funding applied.
Read the distribution
Look at how many windows were profitable, the median and worst window, the deepest drawdown, and the comparison with holding.
Median window return
The middle result across all windows — a far more honest headline than the best one.
Deepest drawdown
The largest peak-to-trough fall in any window, which is what you would have had to sit through.
Profitable windows
How many windows ended in profit. A rule that wins most windows can still lose money if the losses are larger.
✗ Quoting the best window as the result
✓The best window is an extreme selected from many. Report the median, the worst and the drawdown together.
✗ Scaling a short window up into an annual figure
✓Short windows are not a year. Compounding a few weeks of return up to twelve months multiplies the figure and invites the wrong conclusion.
✗ Ignoring funding and liquidation
✓On perpetuals these are recurring costs and genuine outcomes. A leveraged ruleset that never models them is not a simulation of anything real.
Use it when
Skip it when
Check the spread between windows
A small gap between the best and worst window means the rule travels well; a large gap means the answer depends on where you start.
Look at losing windows in a row
The hardest thing to hold is a run of losing windows. Check how the rule behaved in the sideways stretches, not only in the trends.
Keep the run reproducible
Each run records the seed that cut the windows, so the same windows can be rebuilt and the conclusion re-checked.
A backtest replays a rule over one chosen stretch of history. Verification cuts that same history into many non-overlapping windows, repeats it with different boundaries, and shows how much the conclusion moves.
Yes. Funding is charged over time and liquidation is modelled, so a leveraged rule that would have been liquidated in a window is not shown as surviving it.
Contract, timeframe, cost assumptions, leverage, window boundaries and parameter choices all move the outcome. The window seed is recorded so a run can be rebuilt.
Published results are open to read. Running your own strategy in the workspace and keeping its history requires signing in.
Minute-level intervals, from one minute upwards, over the stored history of the contract you pick.
Start free, scale as you grow. No hidden fees, no surprises.
The full platform with usage limits — no card, no countdown.
Best for
Testing whether an idea survives contact with real data.
No monthly allowance — signup credits only
Deeper history, and your first intraday intervals.
Best for
Swing and intraday strategies on 15- and 30-minute bars, over a decade of context.
Includes 360 credits/month · about 72 runs with review
Every interval we support, and the longest intraday windows.
Best for
High-frequency rules that only make sense on 1- to 5-minute bars.
Includes 1000 credits/month · about 200 runs with review
Crypto contract verification results are simulated from historical data and are provided for educational and research purposes only. They do not constitute investment advice, a recommendation or a guarantee of future performance.
Disclaimer: The tools and content on this site are provided for educational purposes only and do not constitute investment advice. Trading and investing involve a substantial risk of loss, and past performance does not guarantee future results.