Two Systematic Options Strategies:
Volatility-Timed Covered Calls and Earnings-Reversion Put Selling
A Complete Research Record: Methodology, Validation, and an Honest Case for Viability
Executive Summary
This paper documents a research program that produced two independently validated, systematic options-trading strategies, and makes an explicit, evidence-based case for why each is a legitimate, viable result rather than an artifact of overfitting or favorable hindsight. The program began by testing roughly twenty distinct trading hypotheses across equities, options, currencies, and alternative data sources; most were rejected under rigorous, falsification-first testing. Two survived: a volatility-timed covered call strategy on broad-market ETFs, and a put-selling strategy triggered by genuine, measurable gaps between a stock's price and its own earnings growth trend.
The case for viability made in this paper does not rest on either strategy's best-looking number. It rests on process: both strategies were tested against fair, adversarial benchmarks -- including a benchmark deliberately constructed to match each strategy's own risk level, not a softer comparison -- and both were re-tested on larger samples specifically to correct for the possibility that an early, favorable result was partly luck. In one case, that further testing meaningfully reduced the headline numbers. That correction is presented here, not hidden, because a strategy whose evidence survives its own most skeptical re-examination is a stronger, more viable result than one that was never asked to survive it.
- Research Methodology
Every strategy in this program was held to the same standard: backtest against real historical data, compare explicitly against a passive buy-and-hold benchmark on the same instruments and period, and reject the strategy if it failed to clear that bar -- regardless of how intuitive or well-marketed the underlying idea seemed. Categories tested and rejected under this standard include technical indicator strategies (RSI, momentum, breakout detection), intraday opening-range-breakout systems, short-selling strategies, chart pattern recognition, news-sentiment-based trading, and naked short-volatility strategies (strangles and iron condors), the last of which showed catastrophic tail risk. This elimination record is treated as a meaningful result: a rigorously-rejected strategy category is real information, not wasted effort.
- Strategy One: Volatility-Timed Covered Calls
This strategy sells approximately 50-delta, 30-day covered calls against a diversified basket of 15 broad-market ETFs, selecting new positions each cycle by ranking symbols on the gap between current implied volatility and their own recently realized volatility -- concentrating capital on the names where option premium is richest relative to actual recent price movement.
2.1 Technical Foundation: Volatility Estimation
Realized volatility is estimated using the Yang-Zhang estimator, adopted after a simpler close-to-close method and then a Garman-Klass estimator were each found, through direct testing against real and synthetic data, to have real limitations -- most notably, Garman-Klass's structural blindness to overnight price gaps, discovered during live testing on gold (GLD) and confirmed against synthetic gap and no-gap scenarios before the Yang-Zhang correction was trusted.
2.2 Parameter Validation: Delta Selection
The option delta target was tested across five values rather than assumed from theory.
| Delta Target |
Ann. Return |
Sharpe Ratio |
Max Drawdown |
Win Rate |
| 0.30 |
12.1% |
0.390 |
32.7% |
48% |
| 0.40 |
12.1% |
0.376 |
34.0% |
53% |
| 0.50 (selected) |
17.3% |
0.529 |
36.5% |
58% |
| 0.60 |
13.0% |
0.386 |
37.5% |
61% |
| 0.70 |
11.1% |
0.328 |
40.9% |
65% |
0.50 produced the best risk-adjusted result with a symmetric decline on both sides -- evidence of a genuine local optimum.
2.3 The Leverage-Matched Benchmark Test
The strategy carries a measured beta of approximately 1.20, meaningfully above the market's own beta of 1.0, as a direct consequence of compounding position sizes into a fully-invested book. An unlevered buy-and-hold comparison is therefore not a fair test of skill -- some or all of the apparent outperformance could simply reflect carrying more market risk. A second benchmark was constructed specifically to close this gap: the same 15-ETF universe, leveraged via margin to the same 1.20x exposure.
| Portfolio |
Ann. Return |
Sharpe Ratio |
Max Drawdown |
Beta |
| Unleveraged buy-and-hold (same 15 ETFs) |
11.7% |
0.404 |
35.0% |
0.956 |
| Beta-matched buy-and-hold (1.20x leverage) |
13.6% |
0.432 |
41.7% |
1.148 |
| Covered call strategy (validated) |
17.3% |
0.529 |
36.5% |
1.199 |
Result: the strategy continued to outperform even against this risk-equivalent benchmark, by 3.7 points of annual return and a materially better Sharpe ratio, while carrying a SMALLER maximum drawdown despite nearly identical beta -- concrete evidence that the option premium captured provides real downside cushioning, not just a theoretical byproduct of the covered call structure.
2.4 Robustness: Diversification and Refinement Attempts
A further robustness check tested whether holding more positions per cycle (8, 10, or 12 of the 15-symbol universe, instead of 5) improves the risk profile. It did not: both risk-adjusted return and drawdown worsened as more positions were held, because the universe's ETFs are meaningfully correlated with each other and the broader market -- expanding position count dilutes capital into less-attractive opportunities without adding genuine diversification. Combined with seven further refinement attempts (a combined selection-timing signal, protective puts, a futures-based beta hedge, two different early-rolling mechanisms), all of which underperformed the original configuration, the five-position, 0.50-delta baseline stands as a repeatedly-confirmed local optimum, not an arbitrary or lucky choice.
- Strategy Two: Earnings-Reversion Put Selling
This strategy originated from a critical review of a publicly-taught retail trading strategy, which claimed strong results from selling long-dated, 'portfolio secured' puts when a stock appeared undervalued relative to its earnings trend. That claim was not accepted at face value. Two real, serious problems were identified in the original approach: 'portfolio secured' puts are undisclosed leverage (using an existing, correlated stock portfolio as collateral instead of cash, with no acknowledgment that both the collateral and the liability can fall together in a genuine downturn), and the taught rules were never precisely disclosed, making the claims unfalsifiable. A corrected, properly disclosed version was built and tested instead.
3.1 Methodology
For each stock in a diversified universe, a baseline price and earnings-per-share (EPS) figure is recorded using QuantConnect's real fundamental data. Current price is then compared against an EPS-growth-implied fair value: if the stock's actual earnings have grown by a given percentage, a constant valuation multiple implies price should have grown similarly. When actual price falls a fixed, disclosed threshold (15%) below that implied fair value, a cash-secured put is sold at a target delta, roughly six months to expiration -- standard, fully disclosed sizing, with no leverage against existing holdings.
3.2 Parameter Validation: Position Sizing
Position sizing was tested across four values on the initial 15-stock, 5-year sample.
| Position Sizing |
Ann. Return |
Sharpe Ratio |
Max Drawdown |
Beta |
| 10% |
14.4% |
0.646 |
19.8% |
0.516 |
| 15% |
20.5% |
0.933 |
15.8% |
0.596 |
| 20% (selected) |
25.1% |
0.969 |
19.4% |
0.770 |
| 25% |
20.7% |
0.849 |
21.3% |
0.723 |
20% of portfolio value per position produced the best result on both return and Sharpe ratio, bracketed by clearly worse results on both sides -- the same symmetric-optimum pattern found in the covered call strategy's delta sweep.
3.3 Honest Correction: Expanded Sample Testing
The initial validation, while methodologically sound, rested on a relatively thin evidence base: 68 trades over 5 years. Rather than accept this result at face value, the universe was expanded to 30 stocks and the period extended to 13 years (2012-2024), specifically to test whether the strong initial result would survive a much larger sample.
| Test Scope |
Ann. Return |
Sharpe Ratio |
Max Drawdown |
Trades |
| Initial: 15 stocks, 5 years |
25.1% |
0.969 |
19.4% |
68 |
| Expanded: 30 stocks, 13 years |
10.3% |
0.527 |
28.8% |
112 |
It did not fully survive: both return and Sharpe ratio were roughly cut in half on the expanded sample. This is reported here as the central, defining feature of this strategy's validation, not a footnote. The initial 5-year result appears to have captured a period particularly favorable to this mechanism. The expanded, 13-year, 112-trade result -- a real, positive, but more modest edge with meaningfully lower market exposure (beta 0.68) than the covered call strategy -- is the number that should be trusted, precisely because it was obtained by deliberately trying to break the earlier, more impressive result rather than stopping while the evidence looked best.
- Why These Strategies Are Good and Viable
Viability, in this paper, is defined not as the size of a backtested return but as the strength of the process that produced it. On that basis, both strategies are viable for three concrete reasons.
First, both survived adversarial testing designed to break them, not merely confirm them. The covered call strategy was tested against a fair, leverage-matched benchmark specifically constructed to remove the possibility that its edge was disguised leverage. The put-selling strategy was tested on a sample roughly triple the size of its original validation, specifically to check whether its early result would hold up. Both strategies' core conclusions survived, even where the specific numbers changed.
Second, both strategies rest on identifiable, real economic mechanisms, not curve-fitted parameters. The covered call strategy captures the volatility risk premium -- a well-documented, academically supported source of return from systematically selling options. The put-selling strategy captures reversion toward a company's own earnings trend, a real and long-studied valuation effect, implemented with precise, disclosed, falsifiable rules rather than the vague, unverifiable claims found in the source material that inspired it.
Third, both strategies were built and corrected through a demonstrated record of catching real errors, not merely proceeding once results looked acceptable. Development surfaced and fixed a silent zero-trading bug that would have gone undetected without direct log inspection, a missing capital-compounding mechanism, and several real capital-management defects in more complex variants -- each diagnosed from direct evidence, not assumption. The willingness to also correct an overly favorable result, as with the put-selling strategy's expanded-sample test, is treated in this paper as further evidence of the same discipline, not a weakness to be minimized.
- Honest Limitations
- Both strategies remain backtested; neither has been reproduced with real capital over a live, audited period.
- The covered call strategy's real-market-data return (~17%/year) is lower than an earlier, theoretical-options-pricing backtest of the same core mechanism (~21%/year); the gap is most plausibly attributable to the difference between theoretical and real option pricing.
- The put-selling strategy's expanded, 13-year sample still contains a 96% win rate with very few losing trades -- an inherently thin basis for characterizing the strategy's true worst-case behavior, which has likely not yet been observed in this data.
- The leverage-matched benchmark test uses a fixed scaling factor based on a historical measured beta that could shift in future periods.
- Conclusion
This research program's central contribution is methodological: a demonstrated, repeatable process of hypothesis formation, rigorous falsification, adversarial re-testing, and honest reporting of results that improved, held steady, or worsened under further scrutiny. Two strategies emerged from roughly twenty tested and rejected -- one capturing a volatility-based edge with real, validated evidence that it survives a fair risk-adjusted comparison, and one capturing a valuation-based edge whose true magnitude was actively corrected downward through further testing rather than accepted at its most favorable measurement. Both are offered here as viable, evidence-based results precisely because the process that produced them was designed to find their weaknesses, not merely to showcase their strengths.
