Backtesting - Decile Testing and Monotonocity - Part II

So now that we understand decile testing and monotonicity, we can run this on ALL features to see how they look.... 

And THIS is why the back-test was using "just" lag_ret_20d instead of the model predictions.

So the Macro features - just adding unnecessary noise, and no value!?

Is the News Sentiment adding any value at all? Maybe, they're ranked #4 and #8, but we would need to try to combine them to see if they add any value at all. Keep in mind also, that the residual (finbert_signed_resid) ferrets out "true" sentiment from momentum (as discussed in an earlier blog post).

And - if you combine them, in what ratio for them to make an optimal combination? Should we combine just two? Or more?

You can see where this can go. You almost need a permutational approach.  

Backtesting - Decile Testing and Monotonicity

 

The Main Backtest (what runs by default)

Uses ONLY lag_ret_20d and momentum_strength.

That's it. The cohort analysis, decile analysis, 2D grid, and portfolio simulations all just look at raw price features directly from the database. No sentiment, no macro, no model. It's purely:

• Filter: is momentum_strength < 0 and lag_ret_20d > 5%?
• Rank: sort by lag_ret_20d descending
• Pick top N

The sentiment and macro features aren't even loaded in the default run. Look at the SQL query in load_from_db — it only pulls lag_ret_5d, lag_ret_20d, volatility_5d, and the return columns.

How lag_ret_20d Became the Ranking Signal

It wasn't chosen upfront. It emerged from the decile analysis in the backtest output. When all stocks are sorted by lag_ret_20d from lowest to highest and look at actual forward returns across deciles, you get this clean pattern:

D1  (avg lag20d: -26%) → ret_5d: -0.544%
D2  (avg lag20d: -13%) → ret_5d: -1.201%
...
D9  (avg lag20d: +15%) → ret_5d: +0.915%
D10 (avg lag20d: +29%) → ret_5d: +1.358%

 

 

7/9 monotonic — returns increase almost perfectly as lag_ret_20d increases. That's the definition of a good ranking signal. The data told you: stocks that have been strong over 20 days continue to be strong over the next 5 days.

When you ran the same decile test on momentum_strength you only got 4/9 monotonic — weak. And when you ran it on avg_pred (the model) you got 5/9 — moderate.

So the answer to "why lag_ret_20d" is simply: it produced the most monotonic decile separation on the holdout test set, which means it's the most reliable ranker of the three things tested.

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What Hasn't Been Tested Yet

Here's the important caveat: you've only tested three ranking signals so far — momentum_strength, lag_ret_20d, and avg_pred. There could be better signals you haven't looked at yet, like volume, or lag_ret_20d combined with something else. But of the options tested, lag_ret_20d won clearly.

ok so the decile test is being run on all features, and lag_ret_20d is the one that had the best score? i don't know what monotonic means

5:16 PM

Not quite — the decile test was only run on a few features, not all of them. And let me explain monotonic in plain terms first because it's the key concept.

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Monotonic in Plain English

Imagine you rank all students in a class by how much they studied, from least to most. Then you look at their test scores. If the more-they-studied, the-better-they-scored — perfectly and consistently — that's monotonic. Every step up in studying corresponds to a step up in grade.

In the backtest, you're asking: if I sort stocks by signal X from lowest to highest, do returns also go from lowest to highest? If yes, the signal is monotonic and you can trust it to rank stocks reliably.

Here's what lag_ret_20d looked like:

D1  lowest lag_ret_20d  → ret_5d: -1.201%   ← worst returns
D2                      → ret_5d: -0.544%
D3                      → ret_5d: -0.293%
...
D9                      → ret_5d: +0.915%
D10 highest lag_ret_20d → ret_5d: +1.358%   ← best returns

Almost perfectly stair-stepping up. That's what 7/9 monotonic means — 7 out of 9 consecutive steps went in the right direction.

Compare to momentum_strength which only got 4/9 — it was all over the place, stepping up then down then up. You can't rely on it to rank stocks.

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Which Features Were Actually Decile-Tested

Only three were tested in the backtest:

• momentum_strength → 4/9 ❌ weak
• lag_ret_20d → 7/9 ✅ strong
• avg_pred (model output) → 5/9 ⚠️ moderate

That's it. The other features — sentiment, macro, volatility, trend_consistent — were never individually decile-tested. They only appear bundled together inside the model's avg_pred.

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Should You Be Testing the Others?

Yes, if you want to know whether you're leaving alpha on the table.

The right way to do this is to run the decile test on every feature individually and see which ones have the cleanest monotonic separation. You'd be looking for features that score 6/9 or better. Then you test combinations of the top performers.

The candidates sitting untested right now:

Probably worth testing:

• lag_ret_5d — you use it to construct momentum_strength but never tested it alone
• volatility_5d — low volatility stocks might have cleaner signals
• finbert_signed — raw sentiment score before residualization
• finbert_signed_resid — sentiment surprise (sentiment unexplained by momentum)

Probably less useful alone but worth checking:

• tone_signed and tone_signed_resid
• vix_0d — same value for all stocks on a given day so won't help rank stocks against each other, but might tell you which days to trade
• risk_adjusted_momentum — momentum normalized by volatility, might be cleaner than raw momentum_strength

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What Was Added

A new --feature-tournament flag that runs a systematic decile test on every feature and prints a ranked leaderboard. When you run it:

bash

python backtest_signal_quality.py --feature-tournament

It tests all of these in one shot:

Price/momentum: lag_ret_20d, lag_ret_5d, momentum_strength, risk_adjusted_momentum, trend_consistent, volatility_5d

Sentiment (raw): finbert_signed, tone_signed

Sentiment (residual): finbert_signed_resid, tone_signed_resid

Macro: vix_0d, treasury_spread_0d, business_confidence_0d, consumer_sentiment_0d

For each feature it reports monotonicity score, Spearman correlation, and D10-D1 spread (best decile return minus worst decile return). Then it prints a clean leaderboard ranked by monotonicity, and runs head-to-head portfolio simulations for the top 3 features.

You can also combine it with --model-lift:

bash

python backtest_signal_quality.py --feature-tournament --model-lift

The output will tell you definitively whether lag_ret_20d is actually the best signal available, or whether something else deserves to be the ranking signal in production.

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The Leaderboard Summary

Rank	Feature	Mono	Spearman	Portfolio Sharpe
#1	volatility_5d	6/9	-0.014	2.70
#2	lag_ret_20d	5/9	+0.120	7.05
#3	lag_ret_5d	5/9	+0.055	5.38
#4	finbert_signed_resid	5/9	+0.006 (not significant)	—
Model (avg_pred)	—	5/9	+0.124	2.88

The Volatility Problem

The tournament crowned volatility_5d as the best feature by monotonicity (6/9). But look at what actually happens when you trade it — Sharpe 2.70, worst day -6.77%, a +13.96% outlier day that saved the whole period. That's a lottery ticket strategy, not a signal. It's picking the most volatile stocks and occasionally getting lucky with a huge mover.

This exposes a flaw in using monotonicity as the sole ranking criterion. Monotonicity measures consistency of direction, not quality of risk-adjusted returns. Volatility ranked stocks happen to step up consistently across deciles, but the actual portfolio is chaotic and dangerous.

lag_ret_20d has lower monotonicity (5/9) but Sharpe 7.05 vs 2.70 — more than 2.5x better risk-adjusted performance. It's the right choice for a strategy you'd actually trade.

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What the Tournament Confirms

Sentiment is weak. Raw finbert_signed and tone_signed score 2/9 — essentially noise as standalone signals. The residual versions do better (5/9 for finbert_signed_resid) but the Spearman correlation is tiny (+0.006) and statistically insignificant. Sentiment is not currently a useful standalone ranking signal.

Macro features are useless for cross-sectional ranking. VIX and treasury spread score 4/9 and business confidence/consumer sentiment score 0/9. This makes sense — they're the same number for every stock on a given day, so they can't tell you which stocks to pick. They might be useful as day-level filters ("don't trade on high VIX days") but that's a different analysis.

lag_ret_5d is a viable alternative to lag_ret_20d. Same monotonicity (5/9), similar cumulative return (+17.01% vs +17.01% — identical in this test period), but lower Sharpe (5.38 vs 7.05) and a much scarier worst day (-5.87% vs -2.40%). The 20-day window is smoother and more reliable.

The model (avg_pred) at 5/9 monotonicity is now in proper context. It ties with lag_ret_20d and lag_ret_5d on monotonicity, but produces Sharpe 2.88 vs 7.05. The model is combining features in a way that degrades the clean signal from lag_ret_20d rather than enhancing it.

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Bottom Line

lag_ret_20d remains the right ranking signal — not because it won a clean tournament, but because it has the best combination of monotonicity, Spearman correlation (+0.12 and statistically significant), and actual portfolio performance. The tournament confirms there is no obvious better single feature hiding in the data that you were previously ignoring.

The one thing worth investigating further: could lag_ret_20d + finbert_signed_resid combined beat lag_ret_20d alone? Both score 5/9 and their Spearman correlations suggest they might be capturing different things. That would be a combined ranking signal test — a logical next step.

Fixing the Momentum Filter

 

The Problem

I noticed the model was not making ANY trades for over a week. Every day, the model generated 30+ buy signals, but the portfolio manager - which acts as a gatekeeper - blocked ALL of them due to negative momentum.

Root Cause Analysis

1. Stale momentum data:

• Using articles from the last 10 days (now 3 days)
• Momentum was calculated from prices 5-20 days old
• By the time you made trading decisions, that momentum was ancient history

2. The momentum paradox:

• momentum_strength = lag_ret_5d - lag_ret_20d
• This measures deceleration, not absolute direction
• Example: Stock up 20% over 20 days, then pulls back 4% in last 5 days = -24% momentum_strength
• The filter was blocking strong stocks taking healthy pullbacks

The Backtest Evidence

Ran analysis on 31,600 test samples and found:

OLD filter (momentum_strength >= 0.1):

• Blocked 89% of stocks
• Blocked stocks: +0.64% avg return, 59.1% win rate ✅ BETTER
• Allowed stocks: +0.23% avg return, 55.7% win rate ❌ WORSE

Best performing cohort (which were blocking):

• "Strong deceleration" (<-0.15 momentum)
• Returns: +1.05% (1d), +2.58% (3d), +2.04% (5d)
• Win rates: 61.6%, 67.4%, 63.4%

The filter was blocking the best opportunities.

The Solution

OLD: Only buy if momentum_strength > 0

NEW: Buy if EITHER:

1. lag_ret_20d > 10% (strong 20-day uptrend), OR
2. 0 < momentum_strength < 15% (mild positive momentum)

Why this works (at least according to the backtest):

• Captures pullbacks in strong uptrends (mean reversion plays)
• Captures steady risers (not overextended)
• Blocks actual falling knives (negative long-term trend)
• Blocks momentum chasers at the top (>15% recent momentum)

Results

• 28/30 signals now pass the filter (vs 0/30 before)
• Deployed 9 trades today with the new logic
• All are strong stocks pulling back (exactly what backtest said to buy)

Now we wait to see if these actually perform as the backtest predicted.

AI / ML - Here is Why You Backtest

My model was working nicely. 

It scored stocks on a number of fronts (pillars).

It used Profitability. It used Solvency. It used Liquidity. It used Efficiency.

These are the "four horseman" of stock evaluation.

I added some of my own twists to the "grading formula", in the form of macro variables (consumer sentiment, business confidence, et al).  I also had some trend analysis, rewarding trends up and penalizing trends down. I rewarded (and penalized) profitability, cash flow, etc. I had scaling done correctly, too in order to ensure a "fair playing field", and also some sector normalization as well.

When I ran the model, using XGBoost to predict 1-year forward return, the stocks at the top of the report looked great when I spot-checked them against various sites that also grade out stocks. I felt good. The r-squared I was getting from XGBoost and a SHAP-pruned feature run was at academic levels (as high as .46 at one point).

As part of some final QA, I ran the resultant code through AI engines which praised the thoroughness, and slapped me on the back reassuring me that my model was on a par with, if not superior to, many academic models.

Then - someone asked me if this model has this been back-tested. 
And the answer was no.  I had not back-tested it up to that point. I didn't think I was ready for back-testing. 

Maybe back-testing is an iterative "continual improvement" process that should be done much earlier in the process, to ensure you don't go down the wrong road.  But I didn't do that.

So, I ran a back-test. And to my horror, the model was completely "upside down" in terms of stocks that would predict forward return.  The AI engines suggested I simply "flip the sign" on my score and invert them. But that didn't feel right. It felt like I was trying to force a score.  

So - the first thing we did, was evaluate the scoring. We looked at correlation between individual scoring pillars and forward return. Negative.

We then looked at correlation in more detail.

First, we calculated Pearson (row-level) and Spearman (rank-level). correlations.

They were negative.

Then, we calculated Average Fwd Return by Score Decile. Sure enough, there was a trend, but completely backwards from what one would expect. 

Quality stocks with scores of 9,8,7,6,5 had negative values that improved as the decile dropped, while the shaky stocks (0,1,2,3,4) had graduated positive values.

The interesting analysis, was a dump of the correlations of each individual pillar to fwd return. The strongest were Profitability and Valuation, followed by MacroBehavior (macroeconomic features) but these were not strong. And the correlations were slightly negative, a couple slightly above zero positive.

But - one was VERY interesting. A log1p correlation between the "final composite score" to forward return that was noticeable if not sizable - but negative.

We experimented with commenting out the penalties, so we could focus on "true metrics" (a flag was engineered in to turn these off which made it easy to test). Re-ran the model, STILL the correlations with forward return were negative.

Then - we decided to remove individual pillars. Didn't change a thing. STILL the correlations with forward return were negative.

Finally, after the AI ensured me - after reviewing the code - that there were no scoring errors, the only thing left to try, aside of shelving the model for lack of success in predicting forward return, was to in fact put a negative sign on the score to invert it and "flip the score".

I did this. And, while the companies that bubbled to the top were shaky on their fundamentals, I did see cases where Analyst Ratings on these stocks were above (and in some cases way above) the current stock price.  

So here is the evidence that we have a model that IS predicting forward return, in a real way.

So - in conclusion. Quality does NOT necessarily equate to forward return.

What does??? Well, nothing in those pillars individually. But - when you combined all of these metrics/features into a big pot, and send them to a sophisticated regression modeler, it does find a magical combination that can predict a relationship with forward return that is linear, and depending on whether you flip that line one way or another, you can theoretically gain - or lose - a return on your money.

Now, if we had put money into those "great stocks" at the top of that prior list, and then had to watch as we lost money, it would have been puzzling and frustrating. But - do we have the courage to put money into these less-than-stellar fundamental stocks to see if this model is right, and that we WILL get a positive forward return? 

I guess it takes some experimentation. Either a simulator, OR, put $X into the top ten and another $X into the bottom ten and see how the perform. Which is what I might be doing shortly.