fix: purge ML validation label leakage
This commit is contained in:
Hermes Agent
@@ -1,9 +1,9 @@
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{
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{
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"provider": "openrouter",
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"provider": "ollama",
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"model": "minimax/minimax-m2.5",
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"model": "gemma4:12b-mlx",
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"providers": {
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"providers": {
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"ollama": {
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"ollama": {
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"base_url": "http://100.100.242.21:11434"
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"base_url": "http://100.79.255.5:11434"
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},
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},
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"lmstudio": {
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"lmstudio": {
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"base_url": "http://100.100.242.21:1234"
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"base_url": "http://100.100.242.21:1234"
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@@ -110,3 +110,40 @@
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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{"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}}}
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||||||
|
{"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}}}
|
||||||
|
|||||||
+216
-122
@@ -43,6 +43,9 @@ START_DATE = "2018-02-01"
|
|||||||
TRAIN_CUTOFF_DAYS = 365
|
TRAIN_CUTOFF_DAYS = 365
|
||||||
# Target: forward 365d return > 30% = "good time to buy"
|
# Target: forward 365d return > 30% = "good time to buy"
|
||||||
GOOD_BUY_THRESHOLD = 30.0
|
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
|
# The 8 core metrics we score
|
||||||
METRIC_KEYS = [
|
METRIC_KEYS = [
|
||||||
@@ -95,6 +98,112 @@ def score_range(value, ranges):
|
|||||||
return 0
|
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):
|
def build_dataset(index, thresholds):
|
||||||
"""Build aligned training dataset: metric scores + forward returns."""
|
"""Build aligned training dataset: metric scores + forward returns."""
|
||||||
# Get all dates from 2018-02-01 onward
|
# 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",
|
log.info("Target distribution: %d positive (%.1f%%), %d negative",
|
||||||
positive, positive / len(labeled) * 100, len(labeled) - positive)
|
positive, positive / len(labeled) * 100, len(labeled) - positive)
|
||||||
|
|
||||||
# Feature columns: scores + raw values + deltas + interactions + cycle position
|
feature_cols = FEATURE_COLS
|
||||||
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
|
|
||||||
|
|
||||||
X = np.array([[r[f] for f in feature_cols] for r in labeled])
|
X = np.array([[r[f] for f in feature_cols] for r in labeled])
|
||||||
y = np.array([r["target"] 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])
|
log.info("Feature matrix: %d samples x %d features", X.shape[0], X.shape[1])
|
||||||
|
|
||||||
# Time-series cross-validation (expanding window, 5 splits)
|
# Purged time-series cross-validation. Standard TimeSeriesSplit is not
|
||||||
tscv = TimeSeriesSplit(n_splits=5)
|
# enough here because each label consumes the next 365 days of returns.
|
||||||
cv_scores = []
|
cv_scores = []
|
||||||
cv_f1 = []
|
cv_f1 = []
|
||||||
cv_precision = []
|
cv_precision = []
|
||||||
cv_recall = []
|
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]
|
X_train, X_val = X[train_idx], X[val_idx]
|
||||||
y_train, y_val = y[train_idx], y[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_train_s = scaler.fit_transform(X_train)
|
||||||
X_val_s = scaler.transform(X_val)
|
X_val_s = scaler.transform(X_val)
|
||||||
|
|
||||||
model = GradientBoostingClassifier(
|
model = _build_model()
|
||||||
n_estimators=300,
|
|
||||||
learning_rate=0.05,
|
|
||||||
max_depth=4,
|
|
||||||
subsample=0.8,
|
|
||||||
min_samples_leaf=20,
|
|
||||||
random_state=42,
|
|
||||||
)
|
|
||||||
model.fit(X_train_s, y_train)
|
model.fit(X_train_s, y_train)
|
||||||
|
|
||||||
y_pred = model.predict(X_val_s)
|
y_pred = model.predict(X_val_s)
|
||||||
@@ -320,27 +416,40 @@ def train_model(rows):
|
|||||||
cv_precision.append(prec)
|
cv_precision.append(prec)
|
||||||
cv_recall.append(rec)
|
cv_recall.append(rec)
|
||||||
|
|
||||||
train_dates = f"{labeled[train_idx[0]]['date']} to {labeled[train_idx[-1]]['date']}"
|
fold_weights = derive_metric_weights(feature_cols, model.feature_importances_)
|
||||||
val_dates = f"{labeled[val_idx[0]]['date']} to {labeled[val_idx[-1]]['date']}"
|
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",
|
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)
|
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("Purged 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 F1: %.3f (+/- %.3f)", np.mean(cv_f1), np.std(cv_f1))
|
||||||
|
|
||||||
# Train final model on all labeled data
|
# Train final model on all labeled data
|
||||||
log.info("Training final model on all %d labeled samples...", len(labeled))
|
log.info("Training final model on all %d labeled samples...", len(labeled))
|
||||||
scaler = StandardScaler()
|
scaler = StandardScaler()
|
||||||
X_scaled = scaler.fit_transform(X)
|
X_scaled = scaler.fit_transform(X)
|
||||||
|
|
||||||
final_model = GradientBoostingClassifier(
|
final_model = _build_model()
|
||||||
n_estimators=300,
|
|
||||||
learning_rate=0.05,
|
|
||||||
max_depth=4,
|
|
||||||
subsample=0.8,
|
|
||||||
min_samples_leaf=20,
|
|
||||||
random_state=42,
|
|
||||||
)
|
|
||||||
final_model.fit(X_scaled, y)
|
final_model.fit(X_scaled, y)
|
||||||
|
|
||||||
# Feature importances
|
# Feature importances
|
||||||
@@ -357,48 +466,7 @@ def train_model(rows):
|
|||||||
bar = "#" * int(imp * 200)
|
bar = "#" * int(imp * 200)
|
||||||
log.info(" %-30s %.4f %s", name, imp, bar)
|
log.info(" %-30s %.4f %s", name, imp, bar)
|
||||||
|
|
||||||
# Extract optimal weights by aggregating importance per metric
|
weights = derive_metric_weights(feature_cols, importances)
|
||||||
# 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))
|
|
||||||
|
|
||||||
log.info("\nOptimal Metric Weights:")
|
log.info("\nOptimal Metric Weights:")
|
||||||
log.info("-" * 50)
|
log.info("-" * 50)
|
||||||
@@ -413,6 +481,7 @@ def train_model(rows):
|
|||||||
log.info("COMPARISON BACKTEST: ML-Weighted vs Equal-Weight")
|
log.info("COMPARISON BACKTEST: ML-Weighted vs Equal-Weight")
|
||||||
log.info("=" * 60)
|
log.info("=" * 60)
|
||||||
comparison = run_comparison(rows, weights)
|
comparison = run_comparison(rows, weights)
|
||||||
|
out_of_sample_comparison = run_out_of_sample_comparison(labeled, fold_results)
|
||||||
|
|
||||||
# Build output
|
# Build output
|
||||||
result = {
|
result = {
|
||||||
@@ -424,6 +493,9 @@ def train_model(rows):
|
|||||||
"mean_f1": round(float(np.mean(cv_f1)), 4),
|
"mean_f1": round(float(np.mean(cv_f1)), 4),
|
||||||
"mean_precision": round(float(np.mean(cv_precision)), 4),
|
"mean_precision": round(float(np.mean(cv_precision)), 4),
|
||||||
"mean_recall": round(float(np.mean(cv_recall)), 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": {
|
"training_info": {
|
||||||
"n_samples": len(labeled),
|
"n_samples": len(labeled),
|
||||||
@@ -435,66 +507,47 @@ def train_model(rows):
|
|||||||
"model": "GradientBoostingClassifier",
|
"model": "GradientBoostingClassifier",
|
||||||
},
|
},
|
||||||
"comparison": comparison,
|
"comparison": comparison,
|
||||||
|
"out_of_sample_comparison": out_of_sample_comparison,
|
||||||
"trained_at": datetime.now(tz=__import__('datetime').timezone.utc).isoformat(),
|
"trained_at": datetime.now(tz=__import__('datetime').timezone.utc).isoformat(),
|
||||||
}
|
}
|
||||||
|
|
||||||
return result
|
return result
|
||||||
|
|
||||||
|
|
||||||
def run_comparison(rows, ml_weights):
|
def _composite_score(row, mode, ml_weights=None):
|
||||||
"""Compare ML-weighted scoring vs equal-weight scoring across score brackets."""
|
scores = [row[f"score_{k}"] for k in SCORE_KEYS]
|
||||||
# Metrics used in scoring (maps to score_* columns)
|
if mode == "equal_weight" or not ml_weights:
|
||||||
score_keys = [
|
return sum(scores) / len(SCORE_KEYS) * 10
|
||||||
"puell_multiple", "mvrv_zscore", "reserve_risk", "rhodl_ratio",
|
equal_weight = 1.0 / len(SCORE_KEYS)
|
||||||
"nupl", "fear_greed", "drawdown", "pct_above_200w_sma", "pct_above_lth_rp",
|
weighted_sum = sum(row[f"score_{k}"] * ml_weights.get(k, equal_weight) for k in SCORE_KEYS)
|
||||||
]
|
return weighted_sum * 10
|
||||||
n_metrics = len(score_keys)
|
|
||||||
equal_weight = 1.0 / n_metrics
|
|
||||||
|
|
||||||
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
|
def _summarize_brackets(scored_rows, score_key):
|
||||||
scored_rows = [r for r in rows if "fwd_365d" in r]
|
results = []
|
||||||
|
for low, high, label in BRACKETS:
|
||||||
results = {"equal_weight": [], "ml_weighted": []}
|
days_in = [r for r in scored_rows if low <= r[score_key] <= high]
|
||||||
|
|
||||||
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]
|
|
||||||
if not days_in:
|
if not days_in:
|
||||||
results[mode].append({
|
results.append({
|
||||||
"range": f"{low}-{high}", "label": label,
|
"range": f"{low}-{high}", "label": label,
|
||||||
"days": 0, "avg_365d": None,
|
"days": 0, "avg_365d": None,
|
||||||
})
|
})
|
||||||
continue
|
continue
|
||||||
returns_365 = [r["fwd_365d"] for r in days_in]
|
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
|
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}",
|
"range": f"{low}-{high}",
|
||||||
"label": label,
|
"label": label,
|
||||||
"days": len(days_in),
|
"days": len(days_in),
|
||||||
"avg_365d": round(sum(returns_365) / len(returns_365), 2),
|
"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),
|
"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",
|
log.info("\n%-18s | %-8s %-8s %-8s | %-8s %-8s %-8s",
|
||||||
"Bracket", "EQ Avg", "EQ Med", "EQ Win%", "ML Avg", "ML Med", "ML Win%")
|
"Bracket", "EQ Avg", "EQ Med", "EQ Win%", "ML Avg", "ML Med", "ML Win%")
|
||||||
log.info("-" * 80)
|
log.info("-" * 80)
|
||||||
@@ -508,6 +561,47 @@ def run_comparison(rows, ml_weights):
|
|||||||
log.info("%-18s | %-8s %-8s %-8s | %-8s %-8s %-8s",
|
log.info("%-18s | %-8s %-8s %-8s | %-8s %-8s %-8s",
|
||||||
eq["label"], eq_avg, eq_med, eq_win, ml_avg, ml_med, ml_win)
|
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
|
return results
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@@ -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
|
||||||
@@ -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
|
||||||
Reference in New Issue
Block a user