125 lines
4.9 KiB
Markdown
125 lines
4.9 KiB
Markdown
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# ASSET BUCKETS — Smart Adaptive Exit Engine
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**Generated from:** 1m klines `/mnt/dolphin_training/data/vbt_cache_klines/`
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**Coverage:** 2021-06-15 → 2026-03-05 · 1710 daily files · 48 assets
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**Clustering:** KMeans k=7 (silhouette optimised, n_init=20)
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**Features:** `vol_daily_pct` · `corr_btc` · `log_price` · `btc_relevance (corr×log_price)` · `vov`
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> **OBF NOT used for bucketing.** OBF (spread, depth, imbalance) covers only ~21 days and
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> would overfit to a tiny recent window. OBF is reserved for the overlay phase only.
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---
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## Bucket B2 — Macro Anchors (n=2)
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**BTC, ETH** · vol 239–321% (annualised from 1m) · corr_btc 0.86–1.00 · price >$2k
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Price-discovery leaders. Lowest relative noise floor, highest mutual correlation.
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Exit behaviour: tightest stop tolerance, most reliable continuation signals.
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---
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## Bucket B4 — Blue-Chip Alts (n=5)
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**LTC, BNB, NEO, ETC, LINK** · vol 277–378% · corr_btc 0.66–0.74 · price $10–$417
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Established mid-cap assets with price >$10. High BTC tracking (>0.65), moderate vol.
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Exit behaviour: similar to anchors; slightly wider MAE tolerance.
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---
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## Bucket B0 — Mid-Vol Established Alts (n=14)
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**ONG, WAN, ONT, MTL, BAND, TFUEL, ICX, QTUM, RVN, XTZ, VET, COS, HOT, STX**
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vol 306–444% · corr_btc 0.54–0.73
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2017-era and early DeFi alts with moderate BTC tracking.
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Sub-dollar to ~$3 price range. Broad mid-tier; higher spread sensitivity than blue-chips.
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Exit behaviour: standard continuation model; moderate giveback tolerance.
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---
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## Bucket B5 — Low-BTC-Relevance Alts (n=10)
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**TRX, IOST, CVC, BAT, ATOM, ANKR, IOTA, CHZ, ALGO, DUSK**
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vol 249–567% · corr_btc 0.29–0.55
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Ecosystem-driven tokens — Tron, Cosmos, 0x, Basic Attention, Algorand, etc.
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Each moves primarily on its own narrative/ecosystem news rather than BTC beta.
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Note: TRX appears low-vol here but has very low BTC correlation (0.39) and
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sub-cent price representation — correctly separated from blue-chips.
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Exit behaviour: wider bands; less reliance on BTC-directional signals.
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---
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## Bucket B3 — High-Vol Alts (n=8)
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**WIN, ADA, ENJ, ZIL, DOGE, DENT, THETA, ONE**
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vol 436–569% · corr_btc 0.58–0.71
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Higher absolute vol with moderate BTC tracking. Include meme (DOGE, DENT, WIN)
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and layer-1 (ADA, ZIL, ONE) assets.
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Exit behaviour: wider MAE bands; aggressive giveback exit on momentum loss.
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---
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## Bucket B1 — Extreme / Low-Corr (n=7)
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**DASH, XRP, XLM, CELR, ZEC, HBAR, FUN**
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vol 653–957% · corr_btc 0.18–0.35
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Privacy coins (DASH, ZEC), payment narrative (XRP, XLM), low-liquidity outliers (HBAR, FUN, CELR).
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Extremely high vol, very low BTC correlation — move on own regulatory/narrative events.
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Exit behaviour: very wide MAE tolerance; fast giveback exits; no extrapolation from BTC moves.
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---
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## Bucket B6 — Extreme / Moderate-Corr Outliers (n=2)
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**ZRX, FET** · vol 762–864% · corr_btc 0.59–0.61
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DeFi (0x) and AI (Fetch.ai) narrative tokens with extreme vol but moderate BTC tracking.
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Cluster n=2 is too small for reliable per-bucket inference; falls back to global model.
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Exit behaviour: global model fallback only.
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---
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## Summary Table
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| Bucket | Label | n | Rel-vol tier | mean corr_btc | Typical names |
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|--------|-------|---|-------------|---------------|---------------|
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| B2 | Macro Anchors | 2 | lowest | 0.93 | BTC, ETH |
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| B4 | Blue-Chip Alts | 5 | low | 0.70 | LTC, BNB, ETC, LINK, NEO |
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| B0 | Mid-Vol Established | 14 | mid | 0.64 | ONT, VET, XTZ, QTUM… |
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| B5 | Low-BTC-Relevance | 10 | mid-high | 0.46 | TRX, ATOM, ADA, ALGO… |
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| B3 | High-Vol Alts | 8 | high | 0.65 | ADA, DOGE, THETA, ONE… |
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| B1 | Extreme Low-Corr | 7 | extreme | 0.27 | XRP, XLM, DASH, ZEC… |
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| B6 | Extreme Mod-Corr | 2 | extreme | 0.60 | ZRX, FET — global fallback |
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Total: **48 assets** · **7 buckets**
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---
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## Known Edge Cases
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- **TRX (B5):** vol=249%, far below B5 average (~450%). Correctly placed due to low corr_btc=0.39 and
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sub-cent price (log_price=0.09 ≈ btc_relevance=0.035). TRX is Tron ecosystem driven, not BTC-beta.
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- **DUSK (B5):** vol=567%, corr=0.29 — borderline B1 (low-corr), but vol places it in B5.
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Consequence: exit model uses B5 (low-relevance alts) rather than extreme low-corr bucket.
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- **B6 (ZRX, FET):** n=2 — per-bucket model will have minimal training data.
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Continuation model falls back to global for these two assets.
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---
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## Runtime Assignment
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Bucket assignments persisted at: `adaptive_exit/models/bucket_assignments.pkl`
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`get_bucket(symbol, bucket_data)` returns bucket ID; unknown symbols fall back to B0.
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Rebuild buckets:
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```bash
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python adaptive_exit/train.py --k 7 --force-rebuild
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```
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---
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## Phase 2 Overlay (future)
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After per-bucket models are validated in shadow mode, overlay 5/10s eigenscan + OBF features
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(spread_bps, depth_1pct_usd, fill_probability, imbalance) as **additional inference-time inputs**
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to the continuation model — NOT as bucketing criteria. OBF enriches live prediction; it does not
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change asset classification.
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