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feat: train forecasts on trade outcomes

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Курнат Андрей
2026-07-14 07:49:32 +03:00
parent 668e606ee2
commit 7186acb9a1
18 changed files with 867 additions and 94 deletions
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crypto_spot_bot/training_coordination.py
+45 -6
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@@ -379,11 +379,19 @@ def _safe_parameters(value: Any) -> dict[str, Any]:
"layers",
"dropouts",
"epochs",
"validation_window",
"holdout_window",
"ensemble_seeds",
"selection_folds",
"learning_rate",
"weight_decay",
"horizon",
"horizons",
"patience",
"context_symbols",
"features",
"seed",
"interval",
"pooled",
"resume_candidate",
}
@@ -391,8 +399,12 @@ def _safe_parameters(value: Any) -> dict[str, Any]:
for key, low, high in (
("limit", 500, 20000),
("epochs", 1, 200),
("validation_window", 64, 2000),
("holdout_window", 64, 1000),
("selection_folds", 1, 12),
("horizon", 1, 96),
("patience", 1, 50),
("seed", 1, 2_147_483_647),
):
if key not in result:
continue
@@ -414,9 +426,19 @@ def _safe_parameters(value: Any) -> dict[str, Any]:
if item.strip().lower() in {"lstm", "gru"}
]
result["architectures"] = ",".join(architectures) or "lstm,gru"
for key in ("lookbacks", "hidden_sizes", "layers", "dropouts", "ensemble_seeds"):
for key in (
"lookbacks",
"hidden_sizes",
"layers",
"dropouts",
"ensemble_seeds",
"horizons",
"context_symbols",
"features",
"interval",
):
if key in result:
result[key] = str(result[key])[:200]
result[key] = str(result[key])[: 4000 if key == "features" else 500]
for key, low, high in (
("learning_rate", 0.00001, 0.1),
("weight_decay", 0.0, 0.1),
@@ -471,10 +493,27 @@ def _validate_symbol_models(symbols: dict[str, Any]) -> None:
raise ValueError(f"candidate model dimensions are invalid: {symbol}") from exc
if not 4 <= lookback <= 512 or not 1 <= input_size <= 256 or not 1 <= hidden_size <= 1024:
raise ValueError(f"candidate model dimensions are out of range: {symbol}")
if not isinstance(entry.get("state_dict"), dict):
raise ValueError(f"candidate recurrent state is missing: {symbol}")
if not isinstance(entry.get("head_weight"), list) or not isinstance(entry.get("head_bias"), list):
raise ValueError(f"candidate forecast head is missing: {symbol}")
members = entry.get("ensemble_members")
payloads = members if isinstance(members, list) and members else [entry]
for payload in payloads:
if not isinstance(payload, dict) or not isinstance(payload.get("state_dict"), dict):
raise ValueError(f"candidate recurrent state is missing: {symbol}")
merged = {**entry, **payload}
if merged.get("multitask_head") is True:
required = (
"head_hidden_weight",
"head_hidden_bias",
"return_head_weight",
"return_head_bias",
"event_head_weight",
"event_head_bias",
)
if any(not isinstance(merged.get(name), list) for name in required):
raise ValueError(f"candidate multitask forecast head is missing: {symbol}")
elif not isinstance(merged.get("head_weight"), list) or not isinstance(
merged.get("head_bias"), list
):
raise ValueError(f"candidate forecast head is missing: {symbol}")
def _compact_now() -> str: