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.
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.
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.
Waits for price to push above the recent high, then rides the session's first real move.
Change any value, then load the template into the editor below.
Where it breaks: Choppy, range-bound sessions. Almost every breakout fades straight back, and at intraday frequency the round-trip costs turn a small loss into a steady bleed.
Buys short-term exhaustion — oversold on a fast RSI — and exits as soon as price normalizes.
Change any value, then load the template into the editor below.
Where it breaks: Trending sessions. Oversold keeps getting more oversold, and mean-reversion takes its worst losses on exactly the days that look most obvious.
A fast/slow average cross compressed onto intraday bars — follows the direction of the last few minutes, flat the moment it turns.
Change any value, then load the template into the editor below.
Where it breaks: Flat, low-volume stretches. The averages cross back and forth with no follow-through, and every whipsaw pays a full round trip in fees.
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 backtesting applies the same idea to cryptocurrency pairs: replay your rules against historical price bars and measure how a strategy would have behaved. Crypto markets are volatile and trade continuously, which makes realistic cost assumptions especially important.
24/7 market
Crypto trades around the clock, so gaps and weekend moves are part of the data.
Volatility
Large price swings change both opportunity and risk, so drawdown deserves close attention.
Pick a pair and a rule
Choose BTC-USDT and test a momentum rule: buy when price closes above its 20-day average, sell when it closes below.
Apply realistic costs
Crypto spreads and fees vary widely, so the backtest applies per-trade costs that reflect a typical venue rather than the most favorable one.
Run and read the drawdown
The report shows return, max drawdown, and profit factor — and crypto's volatility usually makes the drawdown the first thing to check.
Validate before trusting
A strong crypto backtest is a starting point. Move to the training ground to check the rule on unseen data before risking a coin.
Pick a market
Choose the stock, ETF or crypto pair you want to study.
Build the rules
Combine entry, exit and position rules as visual blocks — no code required.
Run the test
The engine replays the rules bar by bar over real price history.
Read the result
Review the return, drawdown and equity curve, plus a plain-language assessment of strengths and weaknesses.
Total return
Overall profit/loss across the simulated period.
Max drawdown
Largest peak-to-trough decline; crypto strategies often show deep drawdowns.
Profit factor
Gross profit divided by gross loss — above 1 means profitable, but must be combined with sample size.
✗ Understating fees and spread because crypto feels 'free'
✓Crypto costs swing with volatility and venue. Optimistic cost inputs are the fastest way to a backtest that looks great and lives poorly.
✗ Assuming the 24/7 crypto market behaves like stocks
✓No session boundaries means gaps happen around news at any hour, and weekend moves are part of the data — the backtest must reflect that.
✗ Chasing a rule that only fits one past bull run
✓A strategy tuned to a single regime will fail the next one. Test across trend and chop before believing it.
Use it when
Skip it when
Stress the costs
Run the backtest with pessimistic fees and spreads too. If the edge survives the worst realistic costs, it is more robust than one that only works with rosy inputs.
Expect deep drawdowns
Crypto strategies routinely show drawdowns that are hard to hold. Check that the historical worst case is something you could actually sit through.
Check both bull and chop windows
A trend rule needs a trend to make money. Look specifically at how it behaves in the sideways stretches, not just the rallies.
It is useful for research but limited by data quality and changing market structure. Treat results as one input to judgment, not as certainty.
Continuous trading, higher volatility and wider spreads change both the data and the importance of cost and slippage assumptions.
Because spreads and fees vary widely by venue and market conditions, and during volatile stretches they can be several times larger than in calm periods. Unrealistic cost inputs flatter crypto results more than almost any other market.
The backtest replays cached historical price bars for supported pairs. Data comes from public market sources, and the usual caveat applies: past price action cannot predict where a pair heads next.
Often yes, and that is the point of checking. Crypto's volatility produces large swings in both directions, so a strategy that looks profitable can still carry drawdowns that are difficult to hold through — the backtest makes that explicit.
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 backtest results are simulated from historical data and are provided for educational purposes only. They do not constitute investment advice 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.