Wayne Whaley posted an interesting study last week and I wanted to dig into it a bit further. Wayne is one of my favorite quantitative researchers. His work is data driven, straightforward, and worth following. The link to his original post is below. What I wanted to add is some context around the numbers using a random entry baseline. Same approach I use in my own research here.

The setup is simple. A 2% or greater down day that was preceded by a 10% or better 13 week quarter. That combination fired at Friday's close on June 5th. Thirty four prior instances going back to 1950. Small sample size. Worth keeping that in mind throughout. But 75 years of data across a wide range of market environments gives it some credibility.

The first thing worth noting is the short term. After a 2% down day the VIX spikes and near term noise increases. You can see that in the 1 week returns column. Big swings in both directions. In the current case VIX is sitting above 21. Worth keeping in mind.

But even in that noisy first week the numbers hold up better than you might expect. The setup produces a 67.6% win rate with an average winner of 2.31% and an average loser of 1.74%. A random 1 week hold in SPY produces a 57.43% win rate with an average winner of 1.64% and an average loser of 1.75%. The setup leads on every metric. Profit factor 2.77 versus 1.26 for random entry. That 1% average net gain in a single week annualizes to nearly 68%. There is real power concentrated in a short window of time here.

At 4 weeks the setup produces a 70.6% win rate with an average winner of 4.77% versus an average loser of 3.23%. Random entry at the same holding period produces a 64.09% win rate with an average winner of 3.31% and an average loser of 3.65%. Profit factor 3.55 versus 1.62 for random entry. That 2.42% average net gain over 4 weeks annualizes to 36.5%.

At 13 weeks the separation becomes dramatic. 88.2% win rate. Average winner 8.78% versus an average loser of only 3.70%. Only 4 losing instances in 34. Random entry at 13 weeks produces a 69.47% win rate with an average winner of 6.25% and an average loser of 6.07%. Profit factor 17.80 versus 2.34 for random entry. That 7.31% average net gain over 13 weeks annualizes to 32.6%. The sample size caveat applies here more than anywhere given only 4 losing instances driving that profit factor.

At 1 year the setup produces an 85.3% win rate with an average winner of 21.41% and an average loser of only 6.24%. That average loser of 6.24% against winners averaging 21.41% is the number that stands out. When this setup loses it tends to lose modestly. When it wins it tends to win big. Random entry at 1 year produces a 79.26% win rate with an average winner of 16.47% and an average loser of 14.34%. Profit factor 19.91 versus 4.39 for random entry. At 1 year the annualized returns converge. Setup 17.3% versus random 10.1%. The shorter holding periods are where the edge over random entry is most pronounced.

A few things worth keeping in mind before drawing any conclusions. Thirty four instances going back to 1950 is a small sample. The profit factors at 13 and 26 weeks are extraordinary but they are being driven by very few losing instances. That cuts both ways. The consistency across 75 years of market history is genuinely impressive. But past setups do not guarantee future outcomes and this is not a trading recommendation.

What I find useful about Wayne’s work is exactly this kind of simple clearly defined setup with a long historical record. No curve fitting. No complex rules. A specific condition that has shown up 34 times in 75 years and produced a consistent forward return profile worth understanding. Credit to Wayne for the original research. His post is worth reading in full.

Wayne Whaley on X

Dave Johnson

Quantitative Developer

TradingTimeMachine.com

Via https://backtest.substack.com/p/s-and-p-500-after-a-2-down-day-historical

Wanted to take a few minutes to walk through some of the tools on the site that do not always get enough attention. A few of them have been particularly relevant lately and worth knowing about if you are following the markets day to day.

The trading forecast page at TradingTimeMachine.com is updated every evening. The gauge scans decades of SPY history, finds days that look like today, and measures whether expected returns are running above or below the historical average for those conditions. Three zones. Green means conditions have historically produced above average returns. Yellow means close to average, no strong edge either way. Red means conditions have historically leaned bearish. Timeframes run from 1 day out to 10 days. When multiple timeframes are in agreement the signal carries more weight. When they are mixed that is worth paying attention to as well.

Yellow is actually the most common state. The gauge spends a lot of time there. Green and red are the rarer conditions and when they show up across multiple timeframes that is when the gauge becomes most actionable. During the recent run it has been sitting mostly in yellow. Normal returns. No strong edge. A lot of other sites were screaming overbought through this whole move. This one stayed neutral because the data did not support a strong call in either direction. Sometimes that is exactly the right read.

Below the pattern forecast is the volatility structure gauge. This one reads the VIX term structure. Specifically the relationship between VIX9D, VIX, and VIX3M. When near term fear is lower than longer dated fear the curve is in contango. Normal healthy market structure. When near term fear spikes above longer dated fear the curve inverts. That is a stress signal. Most tools stop there and just read the shape of the curve. This one goes further. It tested every combination of term structure shape, VIX level, and spread direction against actual forward SPY returns across more than 121,000 ten minute bars going back to 2013. The result is a regime classification tied to real historical outcomes not just a textbook rule. Strong bull at elevated VIX historically produces the best forward return setups. Full inversion at elevated VIX in the 25 to 30 range is historically the most negative regime in the dataset. And one interesting nuance. At extreme panic levels above VIX 30 a fully inverted structure actually flips to a contrarian bullish signal. Fear at its peak has historically been a mean reversion catalyst. The gauge accounts for that automatically.

The forward return window this tool is looking at is 1 to 2.5 trading days. Short term lean not a long term call. Worth noting it is still experimental and can be a little clunky with updates. It will be refined over time. Data runs on a 15 minute delay so best used during regular trading hours when VIX9D and VIX3M are getting updated quotes.

If you follow the markets during the day bookmark the Traders Tools page. Two audio sources running together. TickStrike for price tick audio on SP500, EUR/USD, Oil, and Bitcoin. FinancialJuice voice squawk for live news. Live economic calendar and a text news feed rounding it out. No ads. No noise. Just what you need.

One thing worth doing when you first set it up. Make sure both audio sources are set to persistent in your browser. Otherwise the sound fades when you switch tabs and you miss alerts.

All of these tools are free and live at tradingtimemachine.com. No login required for the forecast page or the trader tools dashboard. A lot of people find the Substack first and never make it to the site. Worth the visit.

Have a Great Night!

Dave Johnson

Quant Developer

TradingTimeMachine.com

via Trading Time Machine https://ift.tt/FYjciq0

Wanted to take a few minutes to walk through some of the tools on the site that do not always get enough attention. A few of them have been particularly relevant lately and worth knowing about if you are following the markets day to day.

The trading forecast page at TradingTimeMachine.com is updated every evening. The gauge scans decades of SPY history, finds days that look like today, and measures whether expected returns are running above or below the historical average for those conditions. Three zones. Green means conditions have historically produced above average returns. Yellow means close to average, no strong edge either way. Red means conditions have historically leaned bearish. Timeframes run from 1 day out to 10 days. When multiple timeframes are in agreement the signal carries more weight. When they are mixed that is worth paying attention to as well.

Yellow is actually the most common state. The gauge spends a lot of time there. Green and red are the rarer conditions and when they show up across multiple timeframes that is when the gauge becomes most actionable. During the recent run it has been sitting mostly in yellow. Normal returns. No strong edge. A lot of other sites were screaming overbought through this whole move. This one stayed neutral because the data did not support a strong call in either direction. Sometimes that is exactly the right read.

Below the pattern forecast is the volatility structure gauge. This one reads the VIX term structure. Specifically the relationship between VIX9D, VIX, and VIX3M. When near term fear is lower than longer dated fear the curve is in contango. Normal healthy market structure. When near term fear spikes above longer dated fear the curve inverts. That is a stress signal. Most tools stop there and just read the shape of the curve. This one goes further. It tested every combination of term structure shape, VIX level, and spread direction against actual forward SPY returns across more than 121,000 ten minute bars going back to 2013. The result is a regime classification tied to real historical outcomes not just a textbook rule. Strong bull at elevated VIX historically produces the best forward return setups. Full inversion at elevated VIX in the 25 to 30 range is historically the most negative regime in the dataset. And one interesting nuance. At extreme panic levels above VIX 30 a fully inverted structure actually flips to a contrarian bullish signal. Fear at its peak has historically been a mean reversion catalyst. The gauge accounts for that automatically.

The forward return window this tool is looking at is 1 to 2.5 trading days. Short term lean not a long term call. Worth noting it is still experimental and can be a little clunky with updates. It will be refined over time. Data runs on a 15 minute delay so best used during regular trading hours when VIX9D and VIX3M are getting updated quotes.

If you follow the markets during the day bookmark the Traders Tools page. Two audio sources running together. TickStrike for price tick audio on SP500, EUR/USD, Oil, and Bitcoin. FinancialJuice voice squawk for live news. Live economic calendar and a text news feed rounding it out. No ads. No noise. Just what you need.

One thing worth doing when you first set it up. Make sure both audio sources are set to persistent in your browser. Otherwise the sound fades when you switch tabs and you miss alerts.

All of these tools are free and live at tradingtimemachine.com. No login required for the forecast page or the trader tools dashboard. A lot of people find the Substack first and never make it to the site. Worth the visit.

Have a Great Night!

Dave Johnson

Quant Developer

TradingTimeMachine.com

Via https://backtest.substack.com/p/a-quick-look-at-some-of-the-free

A recent piece by Nigel at theStrat Lab caught my attention. He took Jeff Sun's 50 SMA ATR extension heuristic and backed it with real distribution data across nearly 2,700 tickers and 1.9 million candles. The bell curve framework he built around ATR extension levels is genuinely useful. It tells you where price extension lives statistically and when a stock is entering rare territory. What it does not tell you is what happens to forward returns after a stock crosses above those extension levels. That is the question this study addresses.

I tested every Nasdaq 100 constituent over 25 years using non-survivorship bias corrected data. That distinction matters. Most backtests use only current index members and quietly inherit an upward bias from testing only on survivors. Every stock that was ever in the Nasdaq 100 is in this data regardless of what happened to it afterward. Testing was done in Wealth-Lab using WealthData, a clean daily bar data source checked for bad ticks and data oddities. The entry signal is straightforward. The first close above a moving average plus a specific ATR multiplier. The ATR is calculated using the standard 14 period setting throughout. One position per symbol. No market filter. I then measured what happened over holding periods from 5 days out to 150 days.

Three variables shape the results. The moving average period, tested from 10 to 200 days. The ATR multiplier, meaning how far above the moving average the stock needs to be before the signal fires, tested from 1.0x to 8.0x in half point increments. And the holding period after entry, tested from 5 to 150 days in 5 day steps. That is 2,250 parameter combinations across 25 years of data. The goal was not to find the best single combination. It was to understand how each variable affects forward returns and where the signal generates genuine alpha over simply buying any Nasdaq 100 stock at random.

Before looking at what the ATR extension signal produces it is worth establishing a baseline. What does a random entry in a Nasdaq 100 stock held for a fixed period actually return over 25 years? The answer is better than most people expect. The Nasdaq 100 has a strong upward bias over this period. A random 5 day entry produces a profit factor of 1.10 with a 51.8% win rate. At 30 days that rises to 1.34. At 60 days 1.50. At 120 days 1.74. The market itself is doing a lot of work.

That baseline matters because it is the honest benchmark for everything that follows. The ATR extension signal is only interesting if it beats random entry. And the first finding is not what most people would expect. At 5 days the ATR extension signal underperforms random entry across nearly every combination tested. The short term caution Nigel’s article implies is in the data. These stocks consolidate, pause, and sometimes pull back in the immediate days after crossing above a significant extension level. Buying them right at the breakout is not the edge.

But hold those same stocks longer and something changes.

From around 30 to 60 days onward the ATR extension signal starts separating from random entry. The gap widens consistently as the holding period extends. By 120 days the separation is meaningful across most combinations and dramatic at higher ATR multipliers. By 150 days the signal is still improving. The study stops there not because the edge disappears but because the focus here is the short to intermediate term holding period that the original article never explored.

This is not a mean reversion story. The stocks that break above statistically significant ATR extension levels are not snapping back to their moving averages. They are strong stocks expressing momentum that persists over months. The rubber band keeps stretching. The data is consistent on that point across 25 years, across multiple moving average periods, and across a wide range of ATR multipliers.

The chart above tells the story clearly. At low ATR extension levels the signal barely separates from random entry. The market’s own upward bias is doing most of the work. As the ATR multiplier increases the separation grows and it grows consistently over time. The stocks that break above statistically extreme extension levels are not average stocks having an average day. They are demonstrating unusual momentum strength. And that strength tends to persist. Not immediately. But over the following weeks and months the data shows these stocks outperforming a random entry in the same universe by a widening margin.

The 50 SMA at 7.5x ATR, the specific zone Nigel's article highlights as statistically rare, reaches a profit factor of 3.46 at 120 days versus 1.74 for random entry. The 40 SMA at 7.0x ATR reaches 4.07 versus the same 1.74 baseline. These are not marginal differences. They represent a genuine and consistent edge for a trend following approach built around momentum continuation.

Not all moving averages perform equally. The intermediate term moving averages in the 20 to 50 day range produce the strongest results at longer holding periods. The 40 day moving average stands out specifically at higher ATR multipliers. At 7.0x ATR and a 120 day hold the MA 40 reaches a profit factor of 4.07 with a 64.4% win rate and an average winner of 26.31% versus an average loser of 11.69%. That is a 2.25 to 1 winner to loser ratio with a majority win rate. The 50 SMA that Nigel’s article focuses on delivers a solid 2.70 at the same holding period and ATR level. The MA 40 simply performs better at extreme extension levels with meaningful trade counts.

One important caveat. At very high ATR multipliers the shorter MA periods like 10, 20, and 30 show even higher profit factors but with very thin trade counts. The MA 40 at 250 trades is where elevated quality meets the most meaningful sample size in the shorter MA range. From MA 50 onward the signal settles into a consistent and reliable range across thousands of trades. The signal works broadly. You are not dependent on finding one specific moving average.

The ATR multiplier tells an equally clean story. Higher multipliers produce stronger results but with fewer triggers. At 1.0x ATR extension the signal fires thousands of times per year across the Nasdaq 100 universe. The results are real but modest. The signal barely separates from random entry at most holding periods. As the multiplier increases the frequency drops and the quality rises. At 5.5x the trade count is in the hundreds to low thousands depending on the MA period. Results at 120 days are meaningfully above random entry. At 7.0x to 7.5x, squarely in the zone Nigel identified as statistically significant, trade counts drop to the low hundreds but the results at 120 days are dramatically above the baseline.

That tradeoff is worth understanding clearly. A trader using a 7.5x ATR multiplier on the 50 SMA is waiting for a genuinely rare setup. Across the entire Nasdaq 100 universe over 25 years that combination triggered around 400 times at the 5 day entry point. That is roughly 16 per year across hundreds of stocks. When it triggers the data says it is worth paying attention to. But it is not a high frequency approach. It is a quality filter for genuinely exceptional momentum conditions.

NVDA using a 7.5x ATR extension from the 50 period moving average and holding a static 120 days

The practical question is how to use this. The short answer is that this research is a starting point not a complete trading system. A complete system requires position sizing, stop methodology, portfolio heat management, and exit optimization beyond a fixed holding period. None of that is addressed here. What is addressed is a specific question about what happens after a stock crosses above a significant ATR extension level. The data is consistent. Short term these stocks consolidate. Medium to longer term they tend to keep going. The stronger the extension the stronger that tendency.

Rather than present a static table of every combination I built an interactive tool where readers can explore every MA period, ATR multiplier, and holding period themselves and see the profit factor, win rate, average winner and average loser for that specific combination. It also shows the trade count with a frequency rating so the statistical weight of each result is immediately visible.

The tool is here: https://www.tradingtimemachine.com/atr-extension-study.html

Nigel’s original article asked whether the ATR extension heuristic was backed by data. It is. This study asks what happens next. The answer depends on how long you hold and how extreme the extension is. Short term the caution is warranted. These stocks pause and consolidate after the breakout. But the traders who dismiss them entirely based on short term behavior are missing the longer term picture. Strong stocks stay strong. The data across 25 years and 2,250 parameter combinations is consistent on that point. This is trend following expressed through a momentum quality filter. The extension is not a warning to stay away. It is a signal that something unusual is happening in that stock. Whether you act on it and how you manage it is your decision. The data just tells you what has historically happened next.

If you want to see what systematic research like this looks like built into a complete tradeable system that work is at Trading Time Machine

Dave Johnson

Quant Developer at TradingTimeMachine.com

Via https://backtest.substack.com/p/does-atr-extension-predict-a-reversal

Via https://backtest.substack.com/p/a-bear-enjoying-the-morning-sun

via Trading Time Machine https://ift.tt/BwSlHQ3

When I was 13 or 14, around 1979, I walked to the local library one rainy afternoon because baseball practice got cancelled. I never got the message, so I showed up with my glove, saw the empty field, and just kept walking.

By then I had already burned through most of the history and science shelves, so I wandered into the tiny investing section. Dusty binders, old hardcovers, the corner people rarely ever touched.

That is where I found it.

A Strategy of Daily Stock Market Timing for Maximum Profit by Joseph E. Granville.

The title alone sounded like a secret manual. For a kid who was just starting to wonder how money actually moved, it hooked me.

I opened it expecting dry charts and finance jargon. Instead I got 55 daily indicators, a practical method for figuring out the next day’s market, a clear explanation of the 200-day moving average, and Granville’s point about how the public is usually dead wrong at major turning points.

I probably only understood 20 percent of it, maybe less, on the first read. But I kept going back. I must have read that book close to 20 times over the next few months. I drew charts in notebooks, followed the Dow in the newspaper, and studied the most active list every single day like it held the answers.

Granville was a real character. He later became known for opening seminars by sliding down a wire in a tuxedo. But underneath the theatrics, he was a serious technician. He created On-Balance Volume and helped turn the 200-day moving average into something every trader knew. And he wrote one of the first market timing books a curious kid could actually sit down and learn from.

I wonder where that original copy is now? I wonder if anyone else was as drawn to it as I was.

It absolutely lit the fuse on my obsession with market data, patterns, indicators, and turning all of it into systems. Hopefully you’ll enjoy it as much as I did.

The book is now in the public domain, so I uploaded a clean digital copy to my website. It is the same one I found by accident that rainy afternoon after practice got cancelled.

You can grab your copy here

Have a Great Night!

Dave Johnson

TradingTimeMachine.com

via Trading Time Machine https://ift.tt/sOnPl3D

When I was 13 or 14, around 1979, I walked to the local library one rainy afternoon because baseball practice got cancelled. I never got the message, so I showed up with my glove, saw the empty field, and just kept walking.

By then I had already burned through most of the history and science shelves, so I wandered into the tiny investing section. Dusty binders, old hardcovers, the corner people rarely ever touched.

That is where I found it.

A Strategy of Daily Stock Market Timing for Maximum Profit by Joseph E. Granville.

The title alone sounded like a secret manual. For a kid who was just starting to wonder how money actually moved, it hooked me.

I opened it expecting dry charts and finance jargon. Instead I got 55 daily indicators, a practical method for figuring out the next day’s market, a clear explanation of the 200-day moving average, and Granville’s point about how the public is usually dead wrong at major turning points.

I probably only understood 20 percent of it, maybe less, on the first read. But I kept going back. I must have read that book close to 20 times over the next few months. I drew charts in notebooks, followed the Dow in the newspaper, and studied the most active list every single day like it held the answers.

Granville was a real character. He later became known for opening seminars by sliding down a wire in a tuxedo. But underneath the theatrics, he was a serious technician. He created On-Balance Volume and helped turn the 200-day moving average into something every trader knew. And he wrote one of the first market timing books a curious kid could actually sit down and learn from.

I wonder where that original copy is now? I wonder if anyone else was as drawn to it as I was.

It absolutely lit the fuse on my obsession with market data, patterns, indicators, and turning all of it into systems. Hopefully you’ll enjoy it as much as I did.

The book is now in the public domain, so I uploaded a clean digital copy to my website. It is the same one I found by accident that rainy afternoon after practice got cancelled.

You can grab your copy here

Have a Great Night!

Dave Johnson

TradingTimeMachine.com

Via https://backtest.substack.com/p/the-library-book-that-started-my

Comparing intelligence to electricity or water suggests a few things:

Always available - not something you “own,” but something you tap into whenever needed.

Metered consumption - you pay for usage, like compute, tokens, or API calls, rather than buying software once.

Utility scale dependence - it becomes embedded in daily life and business operations to the point where people stop thinking about using AI and just expect it to be there.

I still meet people today who question whether AI is going to have a significant impact.

They have no idea why is coming.

Via https://backtest.substack.com/p/sam-altman-we-see-a-future-where

Two articles in the finding has been consistent. We are testing one specific question across different market alignment conditions. When the 7 period RSI lands in a specific 5 point zone does it matter whether it arrived there on a declining RSI or a rising RSI.

The rising RSI entry is the conventional approach. Wait for the RSI to turn back up before buying. That confirmation signal is what most traders are taught to look for. We call it the cross over entry. The declining RSI entry is the unconventional one. The RSI is still falling when you enter. We call it the cross under entry. Both entries find the RSI landing in the same tight zone. The only difference is the direction it was traveling when it got there.

Across two alignment conditions the data has consistently challenged the conventional approach. The cross under entry has outperformed the cross over entry in the majority of zones tested. This article pushes that question into the most hostile environment in the series so far. The 20 day moving average is below the 50. The 50 is below the 200. SPY is below its 200 day moving average. Everything is pointed down.

Most traders would not consider buying weakness here. The data suggests they might want to reconsider. At least in specific zones.

Article 1 - fully bullish alignment

Article 2 - early deterioration

The Test Setup

The methodology is identical to the previous articles. Nasdaq 100 stocks over 25 years. Testing done in Wealth-Lab using WealthData, a clean daily bar data source checked for bad ticks and data oddities. Entry at the open the morning after the signal fires. Fixed 5 day hold.

The same stocks. NVDA, AMZN, TSLA and the rest of the Nasdaq 100. The same 7 period RSI tested in clean 5 point landing zones. For each zone a cross under entry triggers when the RSI crosses down through the upper boundary and closes above the lower boundary. A cross over entry triggers when the RSI crosses up through the lower boundary and closes below the upper boundary. Both entries land in the same tight 5 point zone. The only difference is the direction the RSI was traveling when it got there. Entry is at the open the morning after the signal fires. The position is held for exactly 5 trading days then closed. No stops, no profit targets, no adjustments. A fixed 5 day hold regardless of what price does in between.

The alignment tested here is the most bearish in the series. The 20 day moving average is below the 50. The 50 is below the 200. SPY is below its 200 day moving average. All three moving averages stacked in the opposite order from article 1. The broader market and the individual stock are both in a fully bearish configuration.

One note on sample sizes. The extreme low RSI zones have limited trade counts on both sides. The extreme high RSI zones on the cross over side are also thin. Where sample sizes are limited the findings should be treated with caution. The full data is shown.

What the Data Shows

The core finding of this series continues to hold up. In 13 of 17 RSI zones, a declining RSI entering the zone (the cross under entry) outperformed a rising RSI entering the same zone. Seeing that pattern persist across three very different market environments is significant on its own.

What makes this alignment especially interesting is where the strongest results are appearing. The lower RSI zones are producing the highest quality trades we have seen in the entire series.

When the 7-period RSI falls into the 15 to 20 range on a declining basis, the cross under setup produces an average net profit of 2.06% with a profit factor of 1.65 across 567 trades. In the 20 to 25 range, the setup returns 1.48% with a 1.51 profit factor across 1,376 trades. In the 25 to 30 range, it delivers 1.18% with a 1.42 profit factor across 2,498 trades.

Those are the strongest profit factors we have seen in the lower RSI zones so far. Stronger than the fully bullish alignment. Stronger than the early deterioration alignment. In the most hostile market environment tested, the declining RSI setup in deeply oversold conditions is producing the best trade quality in the data.

There are fewer opportunities because of the alignment conditions, but the setups that do appear are consistently standing out as the highest quality trades.

The average winners and losers are also much larger here than in the fully bullish environment, roughly double in size in some zones. That is simply the nature of bearish markets. Price swings expand in both directions. But larger volatility alone does not weaken the edge. Profit factor already accounts for win rate, average winner, and average loser in a single metric. Even with the wider swings, the lower RSI zones in this bearish alignment are still producing stronger profit factors than the fully bullish environment.

From a practical standpoint, the larger swings are handled through position sizing. The underlying edge itself remains intact. If your average winner and loser are twice as large than our other setups the simple solution is to “normalize” this volatility with smaller position sizes.

What Stands Out Across Three Alignments

Three different alignment conditions have now been tested, all asking the same basic question: does a declining RSI entering a zone outperform a rising RSI entering the same zone? Across all three alignments, the answer has consistently been yes in the majority of cases. That alone is an important result.

But the most surprising finding in the series appears in this latest data set.

In the 20 to 25 and 25 to 30 RSI zones, the cross under entry actually produces a higher profit factor in the fully bearish alignment than it does in the fully bullish one. In the 20 to 25 zone, the fully bearish alignment posts a 1.51 profit factor versus 1.28 in the fully bullish alignment. In other words, the most hostile market environment tested is producing the strongest edge in those specific oversold zones.

The early deterioration alignment tells a different story. That environment, where the 20-day moving average has crossed below the 50-day while SPY itself remains relatively healthy, is where the cross under edge weakens the most in the upper RSI zones. Above the 50 to 55 RSI range, the traditional confirmation entry, the cross over, consistently outperforms the cross under. Interestingly, that pattern does not appear in either the fully bullish or fully bearish alignments.

One thing that remains remarkably consistent across all three environments is the behavior of the lower RSI zones. In deeply oversold conditions, the conventional confirmation entry has never been the superior trade. In both the fully bullish and early deterioration alignments, the cross over entry actually produces negative profit factors in the 15 to 20 and 20 to 25 RSI ranges.

There is also an important nuance in the fully bearish alignment itself. In the fully bullish environment, the gap between cross under and cross over performance in the lower zones was dramatic. Cross over often produced negative profit factors while cross under remained consistently profitable. In the fully bearish alignment, that gap narrows considerably.

In the 15 to 20 RSI zone, cross over actually edges out cross under slightly, with a 1.68 profit factor versus 1.65. In the 20 to 25 and 25 to 30 zones, cross under still performs better, but the margin is much smaller than what was seen in the bullish alignments. In other words, both approaches show edge in the lower RSI zones during fully bearish conditions. The data suggests some normalization between the two entry types, just not enough to fully erase the broader cross under advantage.

Another clear shift in the bearish alignment is the expansion in both average winner and loser size. That is expected in a high-volatility environment. What matters is that the edge, measured through profit factor, actually improves in the lower RSI zones despite the larger swings. The volatility changes the sizing requirements, not the validity of the edge itself.

Article 1 - fully bullish alignment

Article 2 - early deterioration

What This Tells Us

Three articles in. Three alignment conditions tested. The declining RSI entry has outperformed the conventional confirmation entry in the majority of zones across all three. That finding has held in the most bullish environment we tested and in the most bearish. The edge is not a product of favorable market conditions.

The lower RSI zones in the fully bearish alignment are producing the strongest profit factors in the series so far. That is not what most traders would expect. A stock with everything pointed down, generating a declining RSI into the 15 to 30 range, is showing up in the data as one of the better long side setups we have found across 25 years of Nasdaq 100 data.

One important note about what this data represents. Every qualifying trigger across the entire Nasdaq 100 universe is included. This is not filtered through portfolio slots, position limits, or capital allocation rules. Those are separate and important considerations for system design. What this data shows is the raw entry timing question across the complete population of signals. That is what gives the trade counts their statistical meaning.

This is raw research. Not a trading system. A complete system requires position sizing, stop methodology, portfolio heat management, and exit optimization. None of that is addressed here. What is addressed is a specific and testable question about entry timing across three different alignment conditions.

Some people will ask what they are supposed to do with this. The answer is straightforward. In the lower RSI zones a declining RSI is consistently better than a rising RSI across every alignment tested. Knowing where that edge lives and where it fades is worth something regardless of what you do with it next.

If you want to see what this kind of research looks like built into complete tradeable systems, that work is on the systems page at TradingTimeMachine.com

Next up: the same test with the stock fully bearish but SPY still above its 200 day moving average. A stock fully bearish against a healthy market. Does the edge survive that condition? The data will tell us. There are so many conditions that can be tested, is there one you the reader would like me test? Let me know!

Dave Johnson

Quant Developer at

TradingTimeMachine.com

via Trading Time Machine https://ift.tt/iQaNRDO