# ASSET BUCKETS — Smart Adaptive Exit Engine

**Generated from:** 1m klines `/mnt/dolphin_training/data/vbt_cache_klines/`  
**Coverage:** 2021-06-15 → 2026-03-05 · 1710 daily files · 48 assets  
**Clustering:** KMeans k=7 (silhouette optimised, n_init=20)  
**Features:** `vol_daily_pct` · `corr_btc` · `log_price` · `btc_relevance (corr×log_price)` · `vov`

> **OBF NOT used for bucketing.** OBF (spread, depth, imbalance) covers only ~21 days and
> would overfit to a tiny recent window. OBF is reserved for the overlay phase only.

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## Bucket B2 — Macro Anchors (n=2)
**BTC, ETH** · vol 239–321% (annualised from 1m) · corr_btc 0.86–1.00 · price >$2k

Price-discovery leaders. Lowest relative noise floor, highest mutual correlation.
Exit behaviour: tightest stop tolerance, most reliable continuation signals.

---

## Bucket B4 — Blue-Chip Alts (n=5)
**LTC, BNB, NEO, ETC, LINK** · vol 277–378% · corr_btc 0.66–0.74 · price $10–$417

Established mid-cap assets with price >$10. High BTC tracking (>0.65), moderate vol.
Exit behaviour: similar to anchors; slightly wider MAE tolerance.

---

## Bucket B0 — Mid-Vol Established Alts (n=14)
**ONG, WAN, ONT, MTL, BAND, TFUEL, ICX, QTUM, RVN, XTZ, VET, COS, HOT, STX**  
vol 306–444% · corr_btc 0.54–0.73

2017-era and early DeFi alts with moderate BTC tracking.
Sub-dollar to ~$3 price range. Broad mid-tier; higher spread sensitivity than blue-chips.
Exit behaviour: standard continuation model; moderate giveback tolerance.

---

## Bucket B5 — Low-BTC-Relevance Alts (n=10)
**TRX, IOST, CVC, BAT, ATOM, ANKR, IOTA, CHZ, ALGO, DUSK**  
vol 249–567% · corr_btc 0.29–0.55

Ecosystem-driven tokens — Tron, Cosmos, 0x, Basic Attention, Algorand, etc.
Each moves primarily on its own narrative/ecosystem news rather than BTC beta.
Note: TRX appears low-vol here but has very low BTC correlation (0.39) and
sub-cent price representation — correctly separated from blue-chips.
Exit behaviour: wider bands; less reliance on BTC-directional signals.

---

## Bucket B3 — High-Vol Alts (n=8)
**WIN, ADA, ENJ, ZIL, DOGE, DENT, THETA, ONE**  
vol 436–569% · corr_btc 0.58–0.71

Higher absolute vol with moderate BTC tracking. Include meme (DOGE, DENT, WIN)
and layer-1 (ADA, ZIL, ONE) assets.
Exit behaviour: wider MAE bands; aggressive giveback exit on momentum loss.

---

## Bucket B1 — Extreme / Low-Corr (n=7)
**DASH, XRP, XLM, CELR, ZEC, HBAR, FUN**  
vol 653–957% · corr_btc 0.18–0.35

Privacy coins (DASH, ZEC), payment narrative (XRP, XLM), low-liquidity outliers (HBAR, FUN, CELR).
Extremely high vol, very low BTC correlation — move on own regulatory/narrative events.
Exit behaviour: very wide MAE tolerance; fast giveback exits; no extrapolation from BTC moves.

---

## Bucket B6 — Extreme / Moderate-Corr Outliers (n=2)
**ZRX, FET** · vol 762–864% · corr_btc 0.59–0.61

DeFi (0x) and AI (Fetch.ai) narrative tokens with extreme vol but moderate BTC tracking.
Cluster n=2 is too small for reliable per-bucket inference; falls back to global model.
Exit behaviour: global model fallback only.

---

## Summary Table

| Bucket | Label | n | Rel-vol tier | mean corr_btc | Typical names |
|--------|-------|---|-------------|---------------|---------------|
| B2 | Macro Anchors | 2 | lowest | 0.93 | BTC, ETH |
| B4 | Blue-Chip Alts | 5 | low | 0.70 | LTC, BNB, ETC, LINK, NEO |
| B0 | Mid-Vol Established | 14 | mid | 0.64 | ONT, VET, XTZ, QTUM… |
| B5 | Low-BTC-Relevance | 10 | mid-high | 0.46 | TRX, ATOM, ADA, ALGO… |
| B3 | High-Vol Alts | 8 | high | 0.65 | ADA, DOGE, THETA, ONE… |
| B1 | Extreme Low-Corr | 7 | extreme | 0.27 | XRP, XLM, DASH, ZEC… |
| B6 | Extreme Mod-Corr | 2 | extreme | 0.60 | ZRX, FET — global fallback |

Total: **48 assets** · **7 buckets**

---

## Known Edge Cases

- **TRX (B5):** vol=249%, far below B5 average (~450%). Correctly placed due to low corr_btc=0.39 and
  sub-cent price (log_price=0.09 ≈ btc_relevance=0.035). TRX is Tron ecosystem driven, not BTC-beta.
- **DUSK (B5):** vol=567%, corr=0.29 — borderline B1 (low-corr), but vol places it in B5.
  Consequence: exit model uses B5 (low-relevance alts) rather than extreme low-corr bucket.
- **B6 (ZRX, FET):** n=2 — per-bucket model will have minimal training data.
  Continuation model falls back to global for these two assets.

---

## Runtime Assignment

Bucket assignments persisted at: `adaptive_exit/models/bucket_assignments.pkl`  
`get_bucket(symbol, bucket_data)` returns bucket ID; unknown symbols fall back to B0.

Rebuild buckets:
```bash
python adaptive_exit/train.py --k 7 --force-rebuild
```

---

## Phase 2 Overlay (future)

After per-bucket models are validated in shadow mode, overlay 5/10s eigenscan + OBF features
(spread_bps, depth_1pct_usd, fill_probability, imbalance) as **additional inference-time inputs**
to the continuation model — NOT as bucketing criteria. OBF enriches live prediction; it does not
change asset classification.