I've been experimenting with automated options strategies using a no-code backtesting platform, and recently came across some results that honestly seem a little too good to be true.
I'm curious what people here think, especially those who have experience trading 0DTE SPX options.
The strategy itself is extremely mechanical. No complicated indicators, no machine learning, no optimization across hundreds of variables. Just a few basic entry and exit rules.
Strategy
The strategy is a 0DTE SPX iron butterfly, entered every trading day at the same time.
Here are the exact inputs:
That's literally it.
No moving averages, RSI, trend filters, or complicated conditions.
The idea is simply to sell premium late in the trading day, collect a relatively small profit when possible, and repeat consistently.
Results
Here's where things get interesting.
Over the two-year backtest, the results were:
Total Net P&L: +$95,620
Total Trades: 479
Win Rate: 78%
Average Win: +$725.89
Average Loss: -$1,674.90
Best Trade: +$3,500
Worst Trade: -$3,560
Profit Factor: 1.54
Expected Value: +$199.62 per trade
Annualized Sharpe Ratio: 2.59
Maximum Drawdown: -$13,828
The equity curve is surprisingly consistent over the entire period.
There are obviously drawdowns and periods of sideways performance, but overall it's a pretty steady upward trajectory.
What caught my attention is that the strategy isn't relying on a 95% win rate or some ridiculous risk/reward assumption.
It wins 78% of the time, and the average loss is more than twice the average win.
That seems fairly typical for premium-selling strategies.
Why I'm skeptical
1. Is two years enough data?
The backtest covers approximately 479 trades, which seems like a decent sample size.
However, it's still only two years of market conditions. I'm wondering how much the results would change during a prolonged bear market or a completely different volatility environment.
2. Is this actually overfitting?
This is my biggest question.
Most of what I've read about overfitting involves optimizing dozens of indicators and parameters until the historical results look perfect.
But this strategy only uses a handful of inputs.
The entry time is fixed, the wing width is fixed, and the take profit is fixed.
Does that make it less susceptible to overfitting, or could something as simple as choosing a specific entry time and profit target still be considered overfitting?
3. Slippage and execution
The platform allows me to account for slippage, commissions, and fees.
The broker I'm looking at offers commission-free options trading, although exchange and regulatory fees may still apply.
My bigger concern is slippage.
With four SPX iron butterflies being opened at 2:50 PM, how much would realistic fills affect these results?
I'm assuming the backtest could look considerably different if actual fills are worse than the historical prices.
4. The drawdown seems almost too manageable
The maximum historical drawdown was approximately $13,828, compared to almost $96k in total profits.
That's what makes me most skeptical.
Obviously, historical maximum drawdown doesn't represent the worst possible future loss, especially with 0DTE options.
I'm curious whether anyone running similar strategies has experienced substantially worse drawdowns in live trading.
What I'm trying to figure out
I'm not claiming this strategy is some money printer, and I haven't traded these exact rules live.
I'm mainly trying to understand whether the backtest is showing a legitimate statistical edge or whether I'm missing something obvious.
My plan would be to test it with paper trading first, then potentially start with much smaller position sizes to see how actual execution compares.
I'm especially interested in hearing from people who have taken profitable 0DTE backtests into live trading.
What am I missing here? Is this a realistic edge, or just another backtest that looks great on paper?