Vector Databases

 I took an interesting course in Vector Databases on Udemy.

I learned some interesting things in this course, with regards to how information can be chunked, embedded into vectors, and saved in a vector database, with hands-on real coding examples using databases like ChromaDB (with a sqlite3 plugin).

A great example of how vectors are used:

• Feline
• Cat 

AI, through training, has learned that words appearing in similar contexts get similar vectors. 

Here's how it works:
 

1. Distributional hypothesis. The core idea is: a word is characterized by the company it keeps. "Cat" and "feline" both appear in sentences about pets, animals, veterinarians, whiskers, meowing. If you collect enough text, the statistical patterns of what surrounds each word are nearly identical.
 

2. The embedding captures context, not meaning. Training creates a high-dimensional space (e.g., 768–4096 dims) where each word is a point. The training objective is essentially: predict the surrounding words. Words that co-occur with the same neighbors get pushed into the same region of the space.
 

3. Relationship = distance in that space. 

After training:
Similarity = cosine similarity between vectors. "Feline" and "cat" point in nearly the same direction → high cosine → related.
 

Analogies emerge as vector arithmetic: king − man + woman ≈ queen. The direction that encodes "gender" is a consistent axis; directions encode relationships, and positions encode meaning.

Removing Two Stale Macro Features

 

Removing Two Stale Macro Features

The model was trained on 11 features, two of which were macroeconomic sentiment indicators sourced from FRED. On inspection, both turned out to be monthly series — meaning they only update once a month and carry a publication lag on top of that. Despite this, the model had assigned them significant feature importance, essentially learning to lean on data that wasn't meaningfully changing day to day and wasn't even fully available in real time when historical training data was constructed.

Removing them dropped the feature set from 11 to 9. With those features gone, the model redistributed weight toward momentum and the remaining daily macro indicators in a more sensible way. Validation rank correlations improved on two of the three prediction horizons after the change. The two daily macro features that remained — VIX and treasury spread — are genuinely responsive to market conditions and carry the macro signal adequately on their own.

Both changes were low risk given that model predictions are used for monitoring purposes in this system rather than directly driving trading decisions.

Fixing a Train/Serve Skew in Sentiment Residuals

 

Fixing a Train/Serve Skew in Sentiment Residuals

The signal generation process uses a technique called sentiment residualization — essentially, we remove the portion of the FinBERT sentiment score that can be explained by price momentum alone, leaving behind only the genuine sentiment surprise. A stock that has been running up for 20 days will naturally attract positive news coverage, so we want to isolate the sentiment signal that exists above and beyond what the price action would predict.

The problem was subtle. During training, the residual model was fitted on tens of thousands of rows spanning months of history. But at inference time, the same calculation was being refitted fresh each day on whatever small universe of stocks passed the daily filters — typically around 30 stocks. That's a very different statistical population, which meant the sentiment residuals being fed into the composite ranking signal weren't quite the same thing the model had learned from during training. Classic train/serve skew.

The fix was straightforward — serialize the residual model coefficients to disk at the end of each training run and load those fixed coefficients at inference time rather than refitting. Now the definition of sentiment surprise is consistent from training through to live signal generation.

Adding a Layer of LLM as a Final Trading Gate

When we did our back-testing logic we really started to get a full understanding of our features and what mattered and influenced returns.

 The *ONLY* feature that made ANY difference, was the finbert residual, which attempts to ferret out "true sentiment" from momentum. 

The uncomfortable but honest conclusion: the XGB model as currently built is mostly a complicated way of rediscovering momentum, with a thin layer of sentiment on top.

Nonetheless, there is a true signal there, and the signal IS news-related. Which means it is worth investigating. For some length of time anyway. 

After all, you don't want a "news sentiment" model, that can't use news sentiment!

The Iran situation has caused huge volatility in a negative way, and this affected my trading badly as we picked stocks that were headed south. Our balance right now, is 89K from the initial 100K, so over 2 months, we have burned 11K of capital. Good thing it is paper trading.

I added two LLM aspects to the model:

1. agent_review.py which is a standalone agent analysis tool. It reads the signal file from generate_news_signal.py and searches for recent (keyword: recent) news on each candidate, and asks an LLM to recommend which stocks to buy with reasoning.

2. The portfolio_manager.py also looks at the current vix value - for the day (not the vix_0d value that was tied to the article headline date which is x days in the past), and also consults an LLM to check and see if the climate looks right to buy the selected stocks.

Both of these are designed to avoid mistakes buying. And, using an LLM is easier when you give it small tasks. Less tokens consumed, the task is more focused. Trying to give LLMs huge chunks of data to process can cause timeouts (504 errors), and strange results.

I do see a of trades being vetoed in our falling markets right now, so this is working and perhaps was added a bit late in the game. But it can maybe protect our capital so that when the regime shifts, we can start to get back up to the original 100K and then turn some profit. 

 

 

 

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.

---

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.

---

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.

---

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.

---

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.

The News Sentiment Model is Not About News Sentiment

Quick post to discuss some things...

First, what I am discovering after training this model daily, and running predictions, is that the news sentiment is NOT what is influencing this model. 

The news sentiment is based on Transformer models for financial news (Finbert). The news IS fresh, as we get these articles and and then predict over a 1-3 day period, before they're expired out to a training database where predicted returns are compared with actual returns.

News is NOT what this model is learning and training on. Instead, momentum and macro environment features are the aspects that is influencing the returns. Part of this could be the news being stale - the market already knows the news before the model is running. Also, we are only scoring headlines - not actual articles - due to processing constraints. So there's that.

From the training output, here's the feature importance ranked across all three horizons:

Macro features (dominating ~50% of importance):

• vix_0d — market fear index
• treasury_spread_0d — yield curve spread
• business_confidence_0d — FRED macro indicator
• consumer_sentiment_0d — FRED macro indicator

Momentum features (~40%):

• momentum_strength — short-term trend deceleration
• risk_adjusted_momentum — momentum relative to volatility
• trend_consistent — direction consistency

Sentiment features (~10%):

• tone_signed_resid — residualized tone score
• finbert_signed_resid — residualized FinBERT score
• tone_signed — raw tone
• finbert_signed — raw FinBERT

So the model is essentially saying: buy beaten-down stocks when the macro environment is calm. The news sentiment is contributing about 10% of the predictive signal, which is barely above noise given the R² of 0.002 on the residual models.

It's not really a news sentiment model at this point. It's a macro-regime mean-reversion model that happens to have sentiment features along for the ride.

Which raises the real question — is that actually a bad thing? Mean reversion in calm macro regimes is a legitimate strategy. The problem isn't the model's logic, it's that you only have 3 months of data so it hasn't seen enough regime diversity to be robust. And right now you're in a fear regime — elevated VIX, geopolitical uncertainty — which is exactly when this model historically underperforms.