Minute-resolution intraday system on a leveraged Nasdaq ETF
Backtest results · hypothetical, not live
Macro-event risk overlay
Performance
Detail
Results from a single backtest on QuantConnect. Costs and slippage modeled per the strategy's deployment specification.
About
A one-minute intraday system on the leveraged Nasdaq ETF. The core trade is an intraday breakout managed with a tight trailing stop, with hard liquidation at the session close. The system never carries a position overnight.
Layered on top of the core, a small set of deterministic event-day rules controls when the strategy is allowed to take new positions and at what size, reducing exposure on the sessions where the trade has historically been most punished.
Two versions are shown side by side. The original version has been running in live simulation since early 2025, returning +15% since February. The interventions version adds the event-day overlay around scheduled macro releases.
Execution costs
Intraday strategies live or die on execution. A model that holds for weeks pays its costs a handful of times a year. This one pays them on every entry and every exit, hundreds of times a year, so a cost assumption that is even slightly too kind can manufacture an edge that does not exist. Nothing shown above is a gross figure. Costs are charged inside the backtest, trade by trade, before any statistic is computed.
Each fill is charged an Interactive Brokers retail commission, per contract on futures and per share on ETFs, and charged per side, so a round turn pays twice. Retail pricing, not an institutional rate I do not actually get.
On top of commissions, every fill is moved against the strategy by a slippage adjustment before the trade is booked. Buys fill worse than the modeled price, and so do sells. The adjustment is sized to cover the bid-ask spread and queue position on the instruments actually traded.
The platform default for stop fills runs optimistic for breakout strategies, because stops trigger in exactly the fast conditions where fills are worst. I compare modeled fills against my own broker fills on the same signals and tighten the assumption until the backtest matches what I actually get.
The win rate, loss rate, average win, and average loss shown above are all post-cost measurements. Costs push each result toward the losing side: a trade that finished a tick or two ahead gross ends up recorded as a small loss, winners are booked slightly smaller than the price move, and losers slightly larger. So the win rate you see is lower than the raw signal produces, and the ratio between the average win and the average loss is narrower.
That matters most for a low win-rate breakout strategy, where the arithmetic depends on a small number of large wins covering many small losses. Shrink the wins and inflate the losses on every trade and the whole structure can invert. Reading these statistics as already-degraded numbers is the point: the margin between the average win and the average loss is what survives the cost model, not what the signal looks like before it.
This is also why some intraday strategies I have built are not on this site. They cleared every other test and then failed this one. The exact commission and slippage parameters, and the validation stages each strategy has to pass, are written up in full on the methodology page.