DOLPHIN/external_factors/EsoFactors_Test_Prompt.md

Now before following, if the new esoteric factor engine is modeled after the established factor engine, it should have fallback feeling, backfilling, backfilling capabilities. Please backfill for all 55 days, backfill only end in the backfill mode, and backfill all the esoteric factors. If I am assuming the data sources are either computable or historically capable of that, fetch the information from other sources, the mainstay can be backfilled. When you're done with that, without altering any of the main algorithms, look for any and all correlations between the output of the essential factors and drawdown or run-up and profitability or not of the particular day, and other system variables, just to say correlate all against all, looking for any strong confirmations or correlations.
What you’re describing is essentially a controlled historical replay + full-factor correlation audit, without touching the core production logic. I’ll structure this in clean operational terms

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1️⃣ Esoteric Factor Engine — Full 55-Day Backfill Mode

Assumptions:

Esoteric engine mirrors the established factor engine architecture.

All data sources are either:

Deterministically computable from price/volume history, or

Historically retrievable from external APIs or stored archives.


No modification to factor formulas.



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A. Backfill Design Principles

Mode: BACKFILL_ONLY_END

This means:

Replay day-by-day from T-55 → T-1.

Compute all esoteric factors exactly as if live.

Write values to historical store.

No strategy decisions executed.

No incremental learning updates.

No state mutation except factor history buffers.



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B. Fallback Hierarchy

For each esoteric factor:

1. Primary historical source
(official data store / archive)


2. Secondary API historical endpoint


3. Deterministic reconstruction

Recompute from base OHLCV

Reconstruct state from rolling window



4. Synthetic proxy fallback

Only if mathematically derivable

Must be flagged as fallback_level = 3




You log fallback level for each factor/day.


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C. Backfill Procedure

Step 1 — Freeze Production State

Snapshot:

Rolling buffers

Latent embeddings (if any)

Volatility states

Regime states



Step 2 — Initialize Clean Historical Buffers

Clear only:

Esoteric factor buffers

Derived rolling statistics


Keep:

Core algorithm logic intact.



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Step 3 — Replay Loop (55 days)

For each day d from T-55 to T-1:

1. Load OHLCV


2. Fetch or reconstruct all auxiliary inputs


3. Compute:



All esoteric factors

All intermediate states

Any smoothed or filtered outputs

Any latent representations


4. Append to:

esoteric_factor_store[d]

factor_output_store[d]

system_state_snapshot[d]




No trading logic triggered.


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Step 4 — Integrity Check

After replay:

Check no NaNs

Check window completeness

Check rolling windows properly warm

Confirm fallback levels < threshold (ideally mostly 0/1)



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2️⃣ Correlation Audit — All Against All

Now comes the analytical part.

We compute correlations between:

A. Target Variables

Per day:

Max intraday drawdown

Max intraday run-up

Close-to-close return

Strategy P&L

Win/Loss (binary)

Profitability magnitude

Volatility

Spread

Liquidity proxy

Regime label

Latent manifold distortion (if present)

Drift velocity (if you implemented that earlier idea)



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B. Factor Groups

1. Core factors


2. Esoteric factors


3. Combined outputs


4. Latent embeddings (if available)


5. Signal strength


6. Conviction metrics


7. Confidence weighting


8. Any internal risk throttles




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C. Correlation Types to Compute

You want more than Pearson.

Compute:

1. Pearson correlation


2. Spearman rank correlation


3. Kendall tau


4. Mutual information


5. Distance correlation


6. Rolling correlation (7, 14, 30 days)


7. Lagged correlation (±1, ±2, ±3 days)




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D. Binary Outcome Testing

For profitability:

Logistic regression coefficients

Point-biserial correlation

Information coefficient (IC)

t-stat significance



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E. Cross-Correlation Matrix

You compute:

corr_matrix = corr(all_factors ∪ all_targets)

Then:

Extract |corr| > 0.6

Flag p < 0.05

Flag stable correlations across rolling windows

Flag correlations that persist across fallback levels



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3️⃣ What You’re Actually Looking For

This audit answers:

1. Do esoteric factors anticipate drawdown?


2. Do they amplify run-up?


3. Are they redundant with core factors?


4. Are they orthogonal alpha?


5. Do they correlate only in stressed regimes?


6. Do they degrade performance in low-vol days?


7. Do latent geometry distortions align with profitability?




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4️⃣ Interpretation Layer

You classify correlations into:

Strong Confirmations

Stable across windows

Significant

Not present in core-only model


Conditional Correlations

Appear only in high volatility

Appear only in drawdown clusters


Spurious / Structural

Correlate due to shared base data


Dangerous

Correlate negatively with profitability

Increase drawdown magnitude



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5️⃣ Deliverables You Should Generate

1. Heatmap of full correlation matrix


2. Ranked factor impact table


3. Stability score per factor


4. Redundancy map (clustered)


5. Regime-conditional breakdown


6. Factor → drawdown predictive ranking


7. Factor → run-up predictive ranking




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6️⃣ Critical Warning

Do NOT:

Change algorithm weights.

Remove factors.

Normalize differently.

Retrain anything.


This is purely diagnostic.


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7️⃣ What This Tells You Strategically

If strong correlation emerges between:

Esoteric manifold distortion and drawdown
→ you’ve built a stress sensor.


If strong correlation emerges between:

Drift velocity and next-day profitability
→ you have regime anticipation.


If esoteric factors are mostly redundant
→ compress the engine.

If orthogonal and stable
→ you’ve added real signal dept