bizzle/btc-accumulation-monitor
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix: purge ML validation label leakage

Browse Source
This commit is contained in:
Hermes Agent
2026-06-29 00:09:26 +00:00
parent 8fca6181d5
commit de2cd512cd
5 changed files with 405 additions and 137 deletions
Download Patch File Download Diff File Expand all files Collapse all files
config/llm_settings.json
+3 -3
View File
@@ -1,9 +1,9 @@
{
"provider": "openrouter",
"model": "minimax/minimax-m2.5",
"provider": "ollama",
"model": "gemma4:12b-mlx",
"providers": {
"ollama": {
"base_url": "http://100.100.242.21:11434"
"base_url": "http://100.79.255.5:11434"
},
"lmstudio": {
"base_url": "http://100.100.242.21:1234"
data/score_history.jsonl
+37
View File
@@ -110,3 +110,40 @@
{"timestamp": "2026-03-21T22:54:22.144542+00:00", "composite_score": 70.0, "scored_count": 9, "metrics": {"fear_greed": {"score": 10, "value": 12}, "puell_multiple": {"score": 8, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 8, "value": 0.5211180167687892}, "drawdown": {"score": null, "value": null}, "price_vs_200w_sma": {"score": 7, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 8, "value": 0.22243290955405431}, "lth_realized_price": {"score": 5, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-03-21T22:55:08.385540+00:00", "composite_score": 71.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 10, "value": 12}, "puell_multiple": {"score": 8, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 8, "value": 0.5211180167687892}, "drawdown": {"score": 8, "value": 44.26554568527919}, "price_vs_200w_sma": {"score": 7, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 8, "value": 0.22243290955405431}, "lth_realized_price": {"score": 5, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-03-21T22:55:33.933753+00:00", "composite_score": 71.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 10, "value": 12}, "puell_multiple": {"score": 8, "value": 0.6602699608966011}, "mvrv_zscore": {"score": 8, "value": 0.5211180167687892}, "drawdown": {"score": 8, "value": 44.26316624365482}, "price_vs_200w_sma": {"score": 7, "value": 58895.78086828114}, "reserve_risk": {"score": 10, "value": 0.0012985709697654493}, "rhodl_ratio": {"score": 4, "value": 1230.6243545314708}, "nupl": {"score": 8, "value": 0.22243290955405431}, "lth_realized_price": {"score": 5, "value": 43346.58756410873}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T18:10:22.517545+00:00", "composite_score": 63.3, "scored_count": 3, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": null, "value": null}, "mvrv_zscore": {"score": null, "value": null}, "drawdown": {"score": 8, "value": 52.07407994923858}, "price_vs_200w_sma": {"score": null, "value": null}, "reserve_risk": {"score": null, "value": null}, "rhodl_ratio": {"score": null, "value": null}, "nupl": {"score": null, "value": null}, "lth_realized_price": {"score": null, "value": null}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T18:25:30.189079+00:00", "composite_score": 63.3, "scored_count": 3, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": null, "value": null}, "mvrv_zscore": {"score": null, "value": null}, "drawdown": {"score": 8, "value": 52.098667512690355}, "price_vs_200w_sma": {"score": null, "value": null}, "reserve_risk": {"score": null, "value": null}, "rhodl_ratio": {"score": null, "value": null}, "nupl": {"score": null, "value": null}, "lth_realized_price": {"score": null, "value": null}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T18:26:00.556392+00:00", "composite_score": 63.3, "scored_count": 3, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": null, "value": null}, "mvrv_zscore": {"score": null, "value": null}, "drawdown": {"score": 8, "value": 52.098667512690355}, "price_vs_200w_sma": {"score": null, "value": null}, "reserve_risk": {"score": null, "value": null}, "rhodl_ratio": {"score": null, "value": null}, "nupl": {"score": null, "value": null}, "lth_realized_price": {"score": null, "value": null}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T18:40:30.805472+00:00", "composite_score": 63.3, "scored_count": 3, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": null, "value": null}, "mvrv_zscore": {"score": null, "value": null}, "drawdown": {"score": 8, "value": 52.04473350253808}, "price_vs_200w_sma": {"score": null, "value": null}, "reserve_risk": {"score": null, "value": null}, "rhodl_ratio": {"score": null, "value": null}, "nupl": {"score": null, "value": null}, "lth_realized_price": {"score": null, "value": null}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T18:55:31.544772+00:00", "composite_score": 63.3, "scored_count": 3, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": null, "value": null}, "mvrv_zscore": {"score": null, "value": null}, "drawdown": {"score": 8, "value": 52.066148477157356}, "price_vs_200w_sma": {"score": null, "value": null}, "reserve_risk": {"score": null, "value": null}, "rhodl_ratio": {"score": null, "value": null}, "nupl": {"score": null, "value": null}, "lth_realized_price": {"score": null, "value": null}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T18:57:42.639217+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.04790609137056}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T19:12:44.307793+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.02014593908629}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T19:27:45.019177+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.22874365482234}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T19:42:45.679921+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.35009517766498}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T19:57:46.474768+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.34850888324873}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T20:12:47.228456+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.2604695431472}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T20:27:47.904578+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.27633248730964}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T20:42:48.631663+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.26919416243655}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T20:57:49.365132+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.264435279187815}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T21:12:50.093194+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.202569796954315}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T21:27:50.802146+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.16132614213198}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T21:42:51.460898+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.24857233502538}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T21:57:52.223283+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.318369289340104}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T22:12:52.898551+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.37071700507614}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T22:27:54.711182+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.429409898477154}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-27T22:42:55.510257+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 15}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.31043781725888}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-28T20:31:14.232026+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.70066624365482}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-28T20:35:38.591732+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.73080583756345}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-28T20:42:21.081121+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.721288071065985}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-28T20:57:21.749415+00:00", "composite_score": 74.0, "scored_count": 10, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.75301395939086}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}}}
{"timestamp": "2026-06-28T21:09:38.418891+00:00", "composite_score": 68.1, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.73159898477158}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 4, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T21:11:19.117377+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.723667512690355}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T21:26:19.843356+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.723667512690355}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T21:39:03.697500+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.68797588832488}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T21:42:37.143098+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.71890862944163}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T21:57:37.803751+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.96002538071066}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T22:12:38.557670+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.72049492385786}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T22:27:39.293792+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.94733502538072}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T22:42:40.175733+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 53.05678934010152}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T22:57:40.874889+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 53.22255710659899}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T23:12:41.665346+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 53.02744289340101}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
{"timestamp": "2026-06-28T23:27:42.349311+00:00", "composite_score": 69.4, "scored_count": 16, "metrics": {"fear_greed": {"score": 8, "value": 18}, "puell_multiple": {"score": 5, "value": 0.7044739567707577}, "mvrv_zscore": {"score": 8, "value": 0.22409759021503936}, "drawdown": {"score": 8, "value": 52.95923223350254}, "price_vs_200w_sma": {"score": 10, "value": 62284.65298428873}, "reserve_risk": {"score": 10, "value": 0.0010258831016337609}, "rhodl_ratio": {"score": 7, "value": 882.5868942025234}, "nupl": {"score": 8, "value": 0.11309045542914359}, "lth_realized_price": {"score": 7, "value": 49767.33015910989}, "hash_ribbons": {"score": 3, "value": null}, "sopr": {"score": 8, "value": 0.990007085019496}, "sellside_risk": {"score": 10, "value": 0.000734882488382521}, "active_address_momentum": {"score": 4, "value": -0.09386733861382784}, "txcount_momentum": {"score": 6, "value": 0.04454611494295241}, "nvt_price": {"score": 5, "value": 54673.815447216126}, "vdd_multiple": {"score": 4, "value": -0.030495759573562122}}}
ml/optimizer.py
+216 -122
View File
@@ -43,6 +43,9 @@ START_DATE = "2018-02-01"
TRAIN_CUTOFF_DAYS = 365
# Target: forward 365d return > 30% = "good time to buy"
GOOD_BUY_THRESHOLD = 30.0
# Validation embargo/purge horizon: labels use 365-day forward returns.
LABEL_HORIZON_DAYS = 365
VALIDATION_SPLITS = 5
# The 8 core metrics we score
METRIC_KEYS = [
@@ -95,6 +98,112 @@ def score_range(value, ranges):
return 0
SCORE_KEYS = [
"puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
"nupl", "fear_greed", "drawdown", "pct_above_200w_sma", "pct_above_lth_rp",
]
SCORE_FEATURES = [f"score_{k}" for k in SCORE_KEYS]
RAW_FEATURES = [
"raw_puell_multiple", "raw_mvrv_zscore", "raw_reserve_risk",
"raw_rhodl_ratio", "raw_nupl", "raw_fear_greed",
"raw_pct_above_200w_sma", "raw_pct_above_lth_rp", "raw_drawdown",
]
DELTA_FEATURES = [
"delta_30d_mvrv_zscore", "delta_30d_nupl",
"delta_30d_puell_multiple", "delta_30d_reserve_risk",
]
INTERACTION_FEATURES = ["mvrv_x_nupl", "puell_x_reserve"]
CYCLE_FEATURES = ["days_since_ath"]
FEATURE_COLS = SCORE_FEATURES + RAW_FEATURES + DELTA_FEATURES + INTERACTION_FEATURES + CYCLE_FEATURES
BRACKETS = [
(0, 20, "Extreme Caution"),
(21, 40, "Caution"),
(41, 55, "Neutral"),
(56, 70, "Moderate Opportunity"),
(71, 85, "Strong Accumulation"),
(86, 100, "Extreme Accumulation"),
]
def _row_date(row):
return datetime.strptime(row["date"], "%Y-%m-%d")
def purged_time_series_splits(rows, n_splits=VALIDATION_SPLITS,
label_horizon_days=LABEL_HORIZON_DAYS,
embargo_days=0):
"""Yield expanding-window splits with overlapping forward-label windows removed.
A row dated T with a 365-day forward-return label consumes information up to
T+365. For validation beginning at V, any training row whose label window
reaches V is removed. This keeps validation metrics out-of-sample for the
forward-return label, not just for features.
"""
base_splitter = TimeSeriesSplit(n_splits=n_splits)
row_dates = [_row_date(r) for r in rows]
horizon = timedelta(days=label_horizon_days)
embargo = timedelta(days=embargo_days)
for train_idx, val_idx in base_splitter.split(np.arange(len(rows))):
val_start = row_dates[val_idx[0]]
val_end = row_dates[val_idx[-1]]
purged_train = []
for idx in train_idx:
label_end = row_dates[idx] + horizon
before_validation_label_window = label_end <= val_start - embargo
after_validation_embargo = row_dates[idx] > val_end + embargo
if before_validation_label_window or after_validation_embargo:
purged_train.append(idx)
if purged_train:
yield np.array(purged_train, dtype=int), np.array(val_idx, dtype=int)
def _build_model():
return GradientBoostingClassifier(
n_estimators=300,
learning_rate=0.05,
max_depth=4,
subsample=0.8,
min_samples_leaf=20,
random_state=42,
)
def derive_metric_weights(feature_cols, importances):
"""Aggregate feature importances back to transparent score metric weights."""
metric_names = list(SCORE_KEYS)
feature_to_metric = {}
for m in metric_names:
feature_to_metric[f"score_{m}"] = m
feature_to_metric[f"raw_{m}"] = m
feature_to_metric["delta_30d_mvrv_zscore"] = "mvrv_zscore"
feature_to_metric["delta_30d_nupl"] = "nupl"
feature_to_metric["delta_30d_puell_multiple"] = "puell_multiple"
feature_to_metric["delta_30d_reserve_risk"] = "reserve_risk"
metric_importances = {m: 0.0 for m in metric_names}
for name, imp in zip(feature_cols, importances):
if name in feature_to_metric:
metric_importances[feature_to_metric[name]] += float(imp)
elif name == "mvrv_x_nupl":
metric_importances["mvrv_zscore"] += float(imp) / 2
metric_importances["nupl"] += float(imp) / 2
elif name == "puell_x_reserve":
metric_importances["puell_multiple"] += float(imp) / 2
metric_importances["reserve_risk"] += float(imp) / 2
elif name == "days_since_ath":
metric_importances["drawdown"] += float(imp)
total_imp = sum(metric_importances.values())
if total_imp > 0:
weights = {k: round(v / total_imp, 4) for k, v in metric_importances.items()}
else:
weights = {k: round(1 / len(metric_importances), 4) for k in metric_importances}
return dict(sorted(weights.items(), key=lambda x: x[1], reverse=True))
def build_dataset(index, thresholds):
"""Build aligned training dataset: metric scores + forward returns."""
# Get all dates from 2018-02-01 onward
@@ -257,39 +366,33 @@ def train_model(rows):
log.info("Target distribution: %d positive (%.1f%%), %d negative",
positive, positive / len(labeled) * 100, len(labeled) - positive)
# Feature columns: scores + raw values + deltas + interactions + cycle position
score_features = [
"score_puell_multiple", "score_mvrv_zscore", "score_reserve_risk",
"score_rhodl_ratio", "score_nupl", "score_fear_greed",
"score_drawdown", "score_pct_above_200w_sma", "score_pct_above_lth_rp",
]
raw_features = [
"raw_puell_multiple", "raw_mvrv_zscore", "raw_reserve_risk",
"raw_rhodl_ratio", "raw_nupl", "raw_fear_greed",
"raw_pct_above_200w_sma", "raw_pct_above_lth_rp", "raw_drawdown",
]
delta_features = [
"delta_30d_mvrv_zscore", "delta_30d_nupl",
"delta_30d_puell_multiple", "delta_30d_reserve_risk",
]
interaction_features = ["mvrv_x_nupl", "puell_x_reserve"]
cycle_features = ["days_since_ath"]
feature_cols = score_features + raw_features + delta_features + interaction_features + cycle_features
feature_cols = FEATURE_COLS
X = np.array([[r[f] for f in feature_cols] for r in labeled])
y = np.array([r["target"] for r in labeled])
log.info("Feature matrix: %d samples x %d features", X.shape[0], X.shape[1])
# Time-series cross-validation (expanding window, 5 splits)
tscv = TimeSeriesSplit(n_splits=5)
# Purged time-series cross-validation. Standard TimeSeriesSplit is not
# enough here because each label consumes the next 365 days of returns.
cv_scores = []
cv_f1 = []
cv_precision = []
cv_recall = []
fold_results = []
for fold, (train_idx, val_idx) in enumerate(tscv.split(X)):
splits = list(purged_time_series_splits(
labeled,
n_splits=VALIDATION_SPLITS,
label_horizon_days=LABEL_HORIZON_DAYS,
embargo_days=0,
))
if not splits:
log.error("No viable purged validation splits. Need more history for %dd label horizon.",
LABEL_HORIZON_DAYS)
return None
for fold, (train_idx, val_idx) in enumerate(splits):
X_train, X_val = X[train_idx], X[val_idx]
y_train, y_val = y[train_idx], y[val_idx]
@@ -297,14 +400,7 @@ def train_model(rows):
X_train_s = scaler.fit_transform(X_train)
X_val_s = scaler.transform(X_val)
model = GradientBoostingClassifier(
n_estimators=300,
learning_rate=0.05,
max_depth=4,
subsample=0.8,
min_samples_leaf=20,
random_state=42,
)
model = _build_model()
model.fit(X_train_s, y_train)
y_pred = model.predict(X_val_s)
@@ -320,27 +416,40 @@ def train_model(rows):
cv_precision.append(prec)
cv_recall.append(rec)
train_dates = f"{labeled[train_idx[0]]['date']} to {labeled[train_idx[-1]]['date']}"
val_dates = f"{labeled[val_idx[0]]['date']} to {labeled[val_idx[-1]]['date']}"
fold_weights = derive_metric_weights(feature_cols, model.feature_importances_)
fold_results.append({
"fold": fold + 1,
"train_idx": train_idx.tolist(),
"val_idx": val_idx.tolist(),
"weights": fold_weights,
"metrics": {
"auc": round(float(auc), 4),
"f1": round(float(f1), 4),
"precision": round(float(prec), 4),
"recall": round(float(rec), 4),
},
"date_ranges": {
"train": f"{labeled[train_idx[0]]['date']} to {labeled[train_idx[-1]]['date']}",
"validation": f"{labeled[val_idx[0]]['date']} to {labeled[val_idx[-1]]['date']}",
},
"n_train": len(train_idx),
"n_validation": len(val_idx),
})
train_dates = fold_results[-1]["date_ranges"]["train"]
val_dates = fold_results[-1]["date_ranges"]["validation"]
log.info("Fold %d: Train %s | Val %s | AUC=%.3f F1=%.3f P=%.3f R=%.3f",
fold + 1, train_dates, val_dates, auc, f1, prec, rec)
log.info("CV Mean AUC: %.3f (+/- %.3f)", np.mean(cv_scores), np.std(cv_scores))
log.info("CV Mean F1: %.3f (+/- %.3f)", np.mean(cv_f1), np.std(cv_f1))
log.info("Purged CV Mean AUC: %.3f (+/- %.3f)", np.mean(cv_scores), np.std(cv_scores))
log.info("Purged CV Mean F1: %.3f (+/- %.3f)", np.mean(cv_f1), np.std(cv_f1))
# Train final model on all labeled data
log.info("Training final model on all %d labeled samples...", len(labeled))
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
final_model = GradientBoostingClassifier(
n_estimators=300,
learning_rate=0.05,
max_depth=4,
subsample=0.8,
min_samples_leaf=20,
random_state=42,
)
final_model = _build_model()
final_model.fit(X_scaled, y)
# Feature importances
@@ -357,48 +466,7 @@ def train_model(rows):
bar = "#" * int(imp * 200)
log.info(" %-30s %.4f %s", name, imp, bar)
# Extract optimal weights by aggregating importance per metric
# Map each feature back to its parent metric
metric_names = [
"puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
"nupl", "fear_greed", "drawdown", "pct_above_200w_sma", "pct_above_lth_rp",
]
feature_to_metric = {}
for m in metric_names:
feature_to_metric[f"score_{m}"] = m
feature_to_metric[f"raw_{m}"] = m
# Delta features map to their base metric
feature_to_metric["delta_30d_mvrv_zscore"] = "mvrv_zscore"
feature_to_metric["delta_30d_nupl"] = "nupl"
feature_to_metric["delta_30d_puell_multiple"] = "puell_multiple"
feature_to_metric["delta_30d_reserve_risk"] = "reserve_risk"
# Interaction terms split evenly between constituent metrics
# mvrv_x_nupl -> mvrv_zscore + nupl
# puell_x_reserve -> puell_multiple + reserve_risk
metric_importances = {m: 0.0 for m in metric_names}
for name, imp in feat_imp:
if name in feature_to_metric:
metric_importances[feature_to_metric[name]] += imp
elif name == "mvrv_x_nupl":
metric_importances["mvrv_zscore"] += imp / 2
metric_importances["nupl"] += imp / 2
elif name == "puell_x_reserve":
metric_importances["puell_multiple"] += imp / 2
metric_importances["reserve_risk"] += imp / 2
# days_since_ath maps to drawdown conceptually
elif name == "days_since_ath":
metric_importances["drawdown"] += imp
# Normalize weights to sum to 1
total_imp = sum(metric_importances.values())
if total_imp > 0:
weights = {k: round(v / total_imp, 4) for k, v in metric_importances.items()}
else:
weights = {k: round(1 / len(metric_importances), 4) for k in metric_importances}
# Sort by weight descending
weights = dict(sorted(weights.items(), key=lambda x: x[1], reverse=True))
weights = derive_metric_weights(feature_cols, importances)
log.info("\nOptimal Metric Weights:")
log.info("-" * 50)
@@ -413,6 +481,7 @@ def train_model(rows):
log.info("COMPARISON BACKTEST: ML-Weighted vs Equal-Weight")
log.info("=" * 60)
comparison = run_comparison(rows, weights)
out_of_sample_comparison = run_out_of_sample_comparison(labeled, fold_results)
# Build output
result = {
@@ -424,6 +493,9 @@ def train_model(rows):
"mean_f1": round(float(np.mean(cv_f1)), 4),
"mean_precision": round(float(np.mean(cv_precision)), 4),
"mean_recall": round(float(np.mean(cv_recall)), 4),
"validation_method": "purged_expanding_window",
"label_horizon_days": LABEL_HORIZON_DAYS,
"folds": fold_results,
},
"training_info": {
"n_samples": len(labeled),
@@ -435,66 +507,47 @@ def train_model(rows):
"model": "GradientBoostingClassifier",
},
"comparison": comparison,
"out_of_sample_comparison": out_of_sample_comparison,
"trained_at": datetime.now(tz=__import__('datetime').timezone.utc).isoformat(),
}
return result
def run_comparison(rows, ml_weights):
"""Compare ML-weighted scoring vs equal-weight scoring across score brackets."""
# Metrics used in scoring (maps to score_* columns)
score_keys = [
"puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
"nupl", "fear_greed", "drawdown", "pct_above_200w_sma", "pct_above_lth_rp",
]
n_metrics = len(score_keys)
equal_weight = 1.0 / n_metrics
def _composite_score(row, mode, ml_weights=None):
scores = [row[f"score_{k}"] for k in SCORE_KEYS]
if mode == "equal_weight" or not ml_weights:
return sum(scores) / len(SCORE_KEYS) * 10
equal_weight = 1.0 / len(SCORE_KEYS)
weighted_sum = sum(row[f"score_{k}"] * ml_weights.get(k, equal_weight) for k in SCORE_KEYS)
return weighted_sum * 10
brackets = [
(0, 20, "Extreme Caution"),
(21, 40, "Caution"),
(41, 55, "Neutral"),
(56, 70, "Moderate Opportunity"),
(71, 85, "Strong Accumulation"),
(86, 100, "Extreme Accumulation"),
]
# Only use rows with forward returns
scored_rows = [r for r in rows if "fwd_365d" in r]
results = {"equal_weight": [], "ml_weighted": []}
for mode in ["equal_weight", "ml_weighted"]:
for r in scored_rows:
scores = [r[f"score_{k}"] for k in score_keys]
if mode == "equal_weight":
composite = sum(scores) / n_metrics * 10
else:
weighted_sum = sum(r[f"score_{k}"] * ml_weights.get(k, equal_weight) for k in score_keys)
composite = weighted_sum * 10
r[f"composite_{mode}"] = composite
for low, high, label in brackets:
days_in = [r for r in scored_rows if low <= r[f"composite_{mode}"] <= high]
def _summarize_brackets(scored_rows, score_key):
results = []
for low, high, label in BRACKETS:
days_in = [r for r in scored_rows if low <= r[score_key] <= high]
if not days_in:
results[mode].append({
results.append({
"range": f"{low}-{high}", "label": label,
"days": 0, "avg_365d": None,
})
continue
returns_365 = [r["fwd_365d"] for r in days_in]
returns_sorted = sorted(returns_365)
win_rate = len([r for r in returns_365 if r > 0]) / len(returns_365) * 100
results[mode].append({
results.append({
"range": f"{low}-{high}",
"label": label,
"days": len(days_in),
"avg_365d": round(sum(returns_365) / len(returns_365), 2),
"median_365d": round(sorted(returns_365)[len(returns_365) // 2], 2),
"median_365d": round(returns_sorted[len(returns_sorted) // 2], 2),
"win_rate_365d": round(win_rate, 1),
})
return results
# Print comparison
def _log_comparison_table(results):
log.info("\n%-18s | %-8s %-8s %-8s | %-8s %-8s %-8s",
"Bracket", "EQ Avg", "EQ Med", "EQ Win%", "ML Avg", "ML Med", "ML Win%")
log.info("-" * 80)
@@ -508,6 +561,47 @@ def run_comparison(rows, ml_weights):
log.info("%-18s | %-8s %-8s %-8s | %-8s %-8s %-8s",
eq["label"], eq_avg, eq_med, eq_win, ml_avg, ml_med, ml_win)
def run_comparison(rows, ml_weights):
"""Compare final ML-weighted scoring vs equal-weight scoring across all labeled rows.
This is retained for backwards compatibility with existing output. It is an
in-sample/full-history comparison; prefer out_of_sample_comparison for model
selection decisions.
"""
scored_rows = [dict(r) for r in rows if "fwd_365d" in r]
for r in scored_rows:
r["composite_equal_weight"] = _composite_score(r, "equal_weight")
r["composite_ml_weighted"] = _composite_score(r, "ml_weighted", ml_weights)
results = {
"equal_weight": _summarize_brackets(scored_rows, "composite_equal_weight"),
"ml_weighted": _summarize_brackets(scored_rows, "composite_ml_weighted"),
}
_log_comparison_table(results)
return results
def run_out_of_sample_comparison(rows, fold_results):
"""Compare fold-specific ML weights on validation rows only."""
validation_rows = []
for fold in fold_results:
weights = fold.get("weights", {})
for idx in fold.get("val_idx", []):
if idx >= len(rows) or "fwd_365d" not in rows[idx]:
continue
r = dict(rows[idx])
r["fold"] = fold.get("fold")
r["composite_equal_weight"] = _composite_score(r, "equal_weight")
r["composite_ml_weighted"] = _composite_score(r, "ml_weighted", weights)
validation_rows.append(r)
results = {
"folds": len(fold_results),
"validation_days": len(validation_rows),
"equal_weight": _summarize_brackets(validation_rows, "composite_equal_weight"),
"ml_weighted": _summarize_brackets(validation_rows, "composite_ml_weighted"),
}
return results
tests/test_ml_optimizer_validation.py
+74
View File
@@ -0,0 +1,74 @@
from datetime import datetime, timedelta
from ml import optimizer
def _row(date, returns=10.0, score_200w=10, score_drawdown=0):
row = {
"date": date,
"price": 100.0,
"fwd_365d": returns,
"score_puell_multiple": 0,
"score_mvrv_zscore": 0,
"score_reserve_risk": 0,
"score_rhodl_ratio": 0,
"score_nupl": 0,
"score_fear_greed": 0,
"score_drawdown": score_drawdown,
"score_pct_above_200w_sma": score_200w,
"score_pct_above_lth_rp": 0,
}
return row
def test_purged_time_series_splits_remove_overlapping_forward_label_windows():
start = datetime(2020, 1, 1)
rows = [_row((start + timedelta(days=i)).strftime("%Y-%m-%d")) for i in range(900)]
splits = list(
optimizer.purged_time_series_splits(
rows,
n_splits=3,
label_horizon_days=365,
embargo_days=0,
)
)
assert splits, "expected at least one viable split"
for train_idx, val_idx in splits:
val_start = datetime.strptime(rows[val_idx[0]]["date"], "%Y-%m-%d")
latest_allowed_train_date = val_start - timedelta(days=365)
assert len(train_idx) > 0, "purging should keep non-overlapping expanding-window training rows"
for idx in train_idx:
train_date = datetime.strptime(rows[idx]["date"], "%Y-%m-%d")
assert train_date <= latest_allowed_train_date
def test_run_out_of_sample_comparison_scores_only_validation_rows_with_fold_weights():
rows = [
_row("2020-01-01", returns=-10, score_200w=0, score_drawdown=10),
_row("2020-01-02", returns=-5, score_200w=0, score_drawdown=10),
_row("2020-01-03", returns=100, score_200w=10, score_drawdown=10),
_row("2020-01-04", returns=120, score_200w=10, score_drawdown=10),
]
fold_results = [
{
"fold": 1,
"val_idx": [2, 3],
"weights": {"pct_above_200w_sma": 1.0, "drawdown": 0.0},
}
]
comparison = optimizer.run_out_of_sample_comparison(rows, fold_results)
assert comparison["validation_days"] == 2
assert comparison["folds"] == 1
assert sum(bucket["days"] for bucket in comparison["ml_weighted"]) == 2
assert sum(bucket["days"] for bucket in comparison["equal_weight"]) == 2
extreme_ml = next(bucket for bucket in comparison["ml_weighted"] if bucket["label"] == "Extreme Accumulation")
assert extreme_ml["days"] == 2
assert extreme_ml["avg_365d"] == 110.0
caution_equal = next(bucket for bucket in comparison["equal_weight"] if bucket["label"] == "Caution")
assert caution_equal["days"] == 2
tests/test_scoring_engine_ml.py
+63
View File
@@ -0,0 +1,63 @@
import math
from scoring import engine
def _complete_metrics():
return {
"fear_greed": {"value": 10, "classification": "Extreme Fear"},
"puell_multiple": {"value": 0.3},
"mvrv_zscore": {"value": -0.1},
"drawdown": {"value": 60.0, "ath": 250.0},
"price": {"price": 100.0},
"200w_sma": {"value": 120.0},
"reserve_risk": {"value": 0.001},
"rhodl_ratio": {"value": 50.0},
"nupl": {"value": -0.1},
"lth_realized_price": {"value": 120.0},
"hash_ribbons": {"buy_signal": False},
}
def test_score_all_ml_normalizes_displayed_weights_and_contributions(monkeypatch):
monkeypatch.setattr(
engine,
"load_ml_weights",
lambda: {
"fear_greed": 0.40,
"puell_multiple": 0.20,
"mvrv_zscore": 0.15,
"drawdown": 0.10,
"pct_above_200w_sma": 0.05,
"reserve_risk": 0.04,
"rhodl_ratio": 0.03,
"nupl": 0.02,
"pct_above_lth_rp": 0.01,
},
)
scored = engine.score_all_ml(_complete_metrics())
assert scored["ml_mode"] is True
valid_metrics = [m for m in scored["metrics"] if m["score"] is not None]
assert scored["ml_weight_total"] == 1.01 # trained weights + small hash-ribbons fallback
assert math.isclose(sum(m["ml_weight"] for m in valid_metrics), 1.0, abs_tol=0.001)
assert math.isclose(
sum(m["ml_contribution"] for m in valid_metrics),
scored["composite_score"],
abs_tol=0.05,
)
hash_ribbons = next(m for m in valid_metrics if m["key"] == "hash_ribbons")
assert hash_ribbons["ml_raw_weight"] == 0.01
assert hash_ribbons["ml_weight"] == round(0.01 / 1.01, 4)
def test_score_all_ml_preserves_classic_fallback_when_weights_missing(monkeypatch):
monkeypatch.setattr(engine, "load_ml_weights", lambda: {})
scored = engine.score_all_ml(_complete_metrics())
assert scored["ml_mode"] is False
assert scored["ml_error"] == "ML weights not found — run ml/optimizer.py"
assert "classic_score" not in scored