Our Track Record: How We Backtest Every Strategy

Anyone can claim their strategy works. The difference is proving it with data. That's why we backtest every strategy recommendation using real option prices and publish the results for everyone to see.

Here's how the backtesting works, what the metrics mean, and how to evaluate the results honestly.

Why Backtesting Matters

Most options signal services operate on trust. They tell you their picks are profitable, maybe show a few cherry-picked winners, and ask you to subscribe. If they track results at all, they often use theoretical prices that don't reflect what you'd actually get filled at.

We take a different approach. Every strategy recommendation generated by the scoring system is tracked from signal to outcome using actual option prices from the market. When we say a setup had a 65% win rate, that's based on what you could have reasonably achieved by following the signal.

This matters because theoretical backtests lie. A backtest that assumes you buy at the mid-price, every time, with no slippage, no bid-ask spread cost, and no fill issues will look far better than reality. Our methodology accounts for realistic execution.

The Methodology

Real Option Prices, Not Theoretical

The backtesting engine uses actual historical option prices, not Black-Scholes theoretical values. When a trade idea recommends buying the AAPL May 195 call, the backtest uses the actual ask price at the time the signal fired, not what a pricing model says the option should cost.

This distinction is critical. Theoretical prices don't account for supply and demand imbalances, volatility skew, or liquidity conditions. Real prices do. If a signal looks great on paper but the actual option was priced $0.50 wider than the model suggests, that eats into the real return.

Entry and Exit Rules

Every backtested trade follows the same rules that would apply if you followed the signal:

Entry: The signal fires before market open. The backtest assumes entry at the open or within the first hour of trading, using prices available at that time.

Exit scenarios: Each trade is tracked through multiple exit points:

• The peak favorable move during the trade's life
• The price at the recommended exit point (profit target)
• The price at the managed exit (stop loss or time-based exit)
• The price at expiration

This multi-exit tracking is important because it shows you different outcomes depending on how actively you manage the trade. A trade that peaked at +40% but expired at -10% is a win if you took profits, and a loss if you held to expiration.

No Survivorship Bias

Every signal that fires gets tracked, not just the ones that work out. Losers are included in all statistics. If the system generated 100 signals in a month and 35 lost money, those 35 are fully reflected in the win rate and profit data.

This sounds obvious, but many services quietly drop their losing recommendations from the record. We don't.

Understanding the Metrics

The track record page shows several metrics. Here's what each one means and why it matters.

Peak Win Rate

The percentage of trades that reached at least a defined profit threshold at some point during their life. This answers: "If I was watching this trade and had a take-profit order in place, how often would it have been triggered?"

Peak win rate is the most optimistic metric because it assumes perfect timing on the exit. Real traders won't capture every peak. But it shows the potential of the signal.

Expiration Win Rate

The percentage of trades that were profitable at expiration. This is the most conservative metric because it assumes no management at all. You opened the trade and walked away until expiry.

For most strategies, expiration win rate is lower than peak win rate. This makes sense: options are decaying assets, and many trades that are profitable mid-life give back gains as expiration approaches.

The gap between peak win rate and expiration win rate tells you how much active management matters for a given strategy. A large gap means the strategy requires attention. A small gap means it's more set-and-forget.

Managed Exit Win Rate

The percentage of trades that were profitable using a specific exit rule: take profit at a target, stop loss at a threshold, or time-based exit at a predetermined point. This is the most realistic metric because it assumes you follow a plan.

Managed exit win rate typically falls between peak and expiration rates. It's the number most relevant to how you'd actually trade the signals.

Sharpe Ratio

The risk-adjusted return of the strategy. A Sharpe ratio above 1.0 means the returns compensate well for the risk taken. Above 1.5 is strong. Above 2.0 is exceptional.

Sharpe ratio matters more than raw win rate because it accounts for the size of wins versus the size of losses. A strategy with a 50% win rate and a 3:1 reward-to-risk ratio has a Sharpe ratio far better than a strategy with an 80% win rate and a 0.5:1 reward-to-risk ratio.

Profit Factor

Total profits divided by total losses. A profit factor above 1.0 means the strategy is net profitable. Above 1.5 is solid. Above 2.0 is strong.

Profit factor complements win rate by incorporating trade sizing. A strategy that wins 60% of the time but wins big and loses small will have a high profit factor.

Maximum Drawdown

The largest peak-to-trough decline in the strategy's equity curve. This tells you the worst period you'd have experienced if you followed every signal. Maximum drawdown is the reality check: can you psychologically and financially survive the worst stretch?

How to Read the Track Record Page

When you visit the track record page, here's a practical approach:

1. Start with managed exit win rate. This is the most realistic metric. Is it above 50%? Is the strategy directionally profitable?

2. Check the profit factor. Above 1.5 means wins meaningfully exceed losses. Below 1.0 means the strategy is net losing.

3. Look at the Sharpe ratio. This tells you whether the returns justify the risk. Compare it to the S&P 500's historical Sharpe of roughly 0.5-0.8.

4. Review maximum drawdown. Can you handle the worst period? If the max drawdown is 30% and that would cause you to abandon the strategy, it's not right for you regardless of the other metrics.

5. Compare across strategies. The track record breaks down by strategy type (buyer, seller, wheel). Your preferred strategy might perform differently from the aggregate.

6. Check the sample size. Statistics on 20 trades are unreliable. Statistics on 500 trades are meaningful. Look for strategies with enough data to be statistically significant.

Limitations: What the Track Record Can't Tell You

Being transparent about backtesting means being honest about its limits.

Past performance doesn't predict future results. This isn't a legal disclaimer for fun. Market regimes change. A strategy that worked in a low-volatility bull market might struggle in a high-volatility correction. The backtesting data shows what happened, not what will happen.

Execution differs from theory. Even with real option prices, the backtest assumes you could get filled at the reported price. In practice, slippage, liquidity, and timing all affect your actual fills. The more liquid the underlying, the closer your experience will match the backtest.

Position sizing isn't captured. The track record shows per-trade results but doesn't model portfolio-level effects. Two simultaneous losers have different impacts depending on whether each was 2% of your portfolio or 10%.

Your psychology isn't backtested. The backtest assumes you follow every signal without hesitation. In reality, you might skip signals after a losing streak, size up after winners, or exit early out of fear. All of these human factors change the real-world outcome.

How Results Inform the Scoring System

The backtesting isn't just for display. It feeds back into the scoring methodology to improve signal quality over time.

When the data shows that a certain type of signal consistently underperforms, the pillar weights are adjusted. When a specific scenario type (like earnings plays) shows lower win rates in certain market conditions, the system becomes more selective about when it generates those signals.

This feedback loop means the scoring system isn't static. It evolves based on what works and what doesn't, measured against real outcomes.

The screener and trade ideas you see on the discovery page reflect the latest version of this continuously improving model.

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Start Using This

Transparent backtesting with real option prices proves what works. Check our track record, understand the methodology, and decide for yourself whether the data supports our approach.

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• View the full track record
• Understand our scoring methodology
• Read the methodology documentation
• Explore the screener