How our backtests actually work.

01 Intro

One engine, deliberately conservative

Every strategy you run on TextToQuant typed in plain English or assembled in the visual builder compiles to the same backtest engine. Where the engine has to make a judgment call at bar resolution, it makes the conservative one: ambiguous situations resolve against the trade, not in its favor. The goal is that a good result survives scrutiny, not that results look good.

02 Execution model

Signals, entries, and exits

• Signals are evaluated on completed bars (bar close). The engine never acts on a bar that is still forming.
• Entries are edge triggered: a condition must become true having been false on the previous bar to fire. A level condition like “RSI above 50” fires once when it crosses, preventing continuous re-entry on every bar the condition merely stays true.
• Entry fills occur at the signal bar’s close, with configurable slippage applied against the trade direction.
• Intrabar exit ordering is conservative: stop losses are checked before profit targets on the same bar. A bar that touches both your stop and your target books the loss.
• Fills are gap aware: if a bar opens beyond a stop or target level, the fill uses the open price worse for a gapped stop, better for a gapped target never the level the market skipped past.

03 Costs

Fees and slippage

• Exchange style percentage fees are charged on every fill entries, exits, and partial fills alike.
• Slippage is configurable and always applied against the trade direction: entries fill slightly worse than the signal price, never better.

04 Data handling

No look ahead, correct warm up

• Multi timeframe and cross asset conditions read only the previous completed higher timeframe bar a 4 hour strategy consulting the daily trend sees yesterday’s finished daily bar, never today’s still forming one. No look ahead.
• Monthly bars use true calendar closes, not fixed width approximations.
• Indicators are seeded with warm up data before your requested start date, so a 200 period moving average is already correct on bar 1 of your window instead of spending the first months of your test converging.

05 Risk & sizing

Position sizing and liquidation

• Sizing modes: risk based (risk a fixed % of equity per trade against your stop distance), % of equity, and fixed sizing all with an affordability cap, so a position can never be larger than the account can actually pay for.
• Leveraged futures include a liquidation model, and spot shorts carry a margin style liquidation backstop.
• Equity can never go below zero. A blow up terminates the run rather than letting the simulation trade with money that no longer exists.

06 Metrics

How the numbers are computed

• Returns are computed on the full mark to market equity curveone point per bar, including open positions not just on closed trade snapshots.
• Sharpe and Sortino use per timeframe annualization with asset appropriate trading calendars, so a 1 hour crypto strategy and a daily stock strategy are annualized on their own terms.
• Drawdown tracks the running peak of the equity curve.

07 Overfitting protection

Stress testing the result

A single backtest number is easy to overfit. The platform ships several independent checks designed to catch it:

• Seeded Monte Carlo resampling with permutation testing reshuffled trade orders and permuted returns show how much of the result is path luck.
• Out of sample splits performance on data the strategy was not tuned on.
• Walk forward optimization parameters refit on rolling in sample windows, judged only on the forward window that follows.
• Parameter sensitivity sweeps whether the result survives nearby parameter values or lives on an isolated spike.
• Multi asset robustness checks the same logic run on other markets.

08 Limitations

What a backtest cannot tell you

TextToQuant is an educational research tool, not financial advice. See our Terms of Service for the full disclosures.