DOLPHIN/prod/docs/ASSET_BUCKETS.md

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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:

python adaptive_exit/train.py --k 7 --force-rebuild

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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.