Research & Insights
The Scientific Research Approach to Stock Indicators
Most retail investors treat stock indicators as signals. A better frame is to treat them as hypotheses — subject to the same empirical standards we apply to any scientific claim.
Read article →Do Technical Indicators Actually Work? We Ran 856 Statistical Tests to Find Out
A rigorous 20-year study of 14 technical indicators across 5 ETFs using permutation bootstrap testing and Bonferroni correction reveals which signals are statistically real — and which are mythology.
Read article →The 9 EMA / 200 SMA Intraday Study: What the Data Actually Says Across 22 Assets
A systematic empirical test of the 9 EMA / 200 SMA framework across 22 assets on 1-hour bars, covering 704 hypothesis tests with Bonferroni correction — finding that the cross works on ETFs, fails on speculative single stocks, and that the pullback entry is confirmed negative on NVDA.
Read article →Same Signal, Opposite Result: How Stock Type Determines Whether Technical Indicators Work
We applied the three strongest technical indicators from our ETF study to NVDA, NFLX, WMT, COST, BAC, and JNJ — and found that the direction of the signal completely reverses depending on whether the stock trends or mean-reverts.
Read article →VWAP + RSI Four-Quadrant Study: Why the Regime Filter Doesn't Work the Way You Think
We tested the theory that stocks uptrend above VWAP until overbought, and downtrend below VWAP until oversold. 616 hypothesis tests across 11 assets over 20 years reveal a more nuanced — and more actionable — picture.
Read article →VIX Is the Real Signal: How Fear Amplifies Technical Indicators by 9x
Combining RSI, Keltner Channel, and OBV with a VIX regime filter reveals a four-tier trading framework — including Cohen's d above 1.0 on defensive stocks during market panic, and a critical warning about QQQ that inverts the signal entirely.
Read article →Exit When RSI Hits 50, Not on a Fixed Date: Completing the Mean-Reversion Trading System
We tested 11 exit strategies against the same entry events across 6 assets and 20 years. Dynamic RSI and Keltner Channel exits deliver 78% win rates and Sharpe ratios above 0.90 — versus 60% win rates and Sharpe of 0.23 from the fixed 10-day hold most traders default to.
Read article →Study 6: Does Retail Participation Change Which Indicators Work?
We tested whether Bollinger Bands outperform RSI/KC on retail-heavy stocks. The BB lower hypothesis didn't confirm — but three sharper findings emerged: crypto-adjacent stocks broke every signal, NVDA responded to BB not RSI, and institutional stocks showed the strongest overbought reversal edge.
Read article →Study 7: Per-Asset Profiling and the MFI Discovery — Six Tickers, One Unexpected Signal
An automated per-asset profiling study across SPY, QQQ, WMT, COST, JNJ, and NVDA that uncovered Money Flow Index (MFI < 20) as the most consistently powerful oversold signal across asset archetypes — outperforming RSI, Keltner Channel, and Bollinger Bands on 4 of 6 assets.
Read article →Study 8: Sector ETF and Mega-Cap Profiling — TSLA's Momentum Archetype and AAPL's Defensive Secret
Profiling nine assets across three mega-caps (AAPL, MSFT, TSLA) and six sector ETFs reveals a new fifth archetype — momentum_continuation — where TSLA's BB upper touch predicts continued upside rather than reversal, and confirms AAPL as a defensive fundamental_reverter with a 100% win-rate KC midline exit.
Read article →40% CAGR, Sharpe 1.94: A Three-Strategy Portfolio That Leaves SPY Behind
A rigorous 2018–2026 backtest combining value stock picking, SPX iron condor income, and a leveraged QQQ/BOXX regime-switching leg. Each strategy audited for look-ahead bias. Combined result: $100K → $1.6M at 40.4% CAGR with max drawdown under 21%.
Read article →The Impact of Options Expiration Day on S&P 500 Volatility
How does options expiration day influence market volatility, and what can traders learn from these patterns?
Read article →QQQ Oversold Reversion: What MFI, RSI, and VIX Tell You — and When They Lie
We ran 13 signal combinations on 20 years of QQQ daily data to find early indicators of oversold conditions and reversion points. The findings contradict the conventional approach: crossback signals fire too late, VIX 20–25 is a trap, and the best entry is before the obvious confirmation — not after it.
Read article →Study 10: The Only Calendar Spread Strategy That Actually Makes Money
We tested calendar spreads on every S&P 500 stock for 20 years. They lost money. Then we figured out why — and built a version that works. Sharpe 0.677, win rate 56%, deployed only 17% of the time.
Read article →MFI Oversold: An Exhaustive Study Across 8 Assets, 3 Timeframes, and 281 Combinations
We tested Money Flow Index oversold signals across 8 assets, 3 bar timeframes, 4 MFI periods, and 3 thresholds — 281 combinations total. IWM on daily bars is the standout: MFI14 < 20 produces 74% win rate over 5 days with p < 0.0001. Daily bars dominate. Hourly bars mostly fail. Weekly results split sharply by asset character.
Read article →Study 9: Which Options Strategies Actually Work — A 20-Year, 404-Ticker Regime Study
We backtested 12 options strategies across all S&P 500 companies from 2005 to 2025, tagging every trade with four regime dimensions: VIX bucket, market trend, rate environment, and NBER recession. Calendar spread is the only strategy that works in every regime. Here is exactly when to run each one.
Read article →Study 11: We Built a Value-Screened Options Strategy. The Screen Made It Worse.
We ran bull put spreads on stocks with positive margin of safety and growing earnings. Sharpe 1.980, 89% win rate. Then we removed the value screen. Sharpe went to 2.782. Here's exactly why.
Read article →Study 12: The One Filter That Actually Improves Bull Put Spreads
We tested every entry filter we could think of — value screens, moving averages, trend filters. One worked. IV rank ≥ 30 lifted Sharpe from 2.782 to 3.160, turned both bad years positive, and cut kurtosis by 18%. The others made things worse.
Read article →Study 13: Two Filters, One Near-Market-Neutral Strategy
We swept every IV rank threshold from 0 to 60 with and without earnings avoidance across 7 years and 56 liquid large-caps. The optimal combination — IVR≥30, no earnings window — achieves Sharpe 3.612, wins every year including 2018 and 2022, and has a SPY daily correlation of 0.001.
Read article →Training Series
View all →Calendar Spreads Part 3: Three Example Trades — Setup, Management, and Result
Three concrete calendar spread trade walkthroughs — a clean theta-harvest on SPY, an earnings-adjacent trade gone wrong, and a CRISIS-regime trade during the 2020 selloff — with full entry/exit and P&L
Read article →Calendar Spreads Part 4: Managing the Position — Rolling, Adjusting, and Responding to Moves
How to actively manage a calendar spread through delta drift, IV changes, and adverse moves — covering the roll mechanics, when to defend vs accept the loss, and the Greeks-based decision framework.
Read article →Calendar Spreads Part 5: When and How to Exit — Profit Targets, Stop Losses, and Converting to a Diagonal
The complete exit framework for calendar spreads — profit targets by regime, the 50% loss stop rule, how to close both legs efficiently, and when converting to a diagonal makes sense
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