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Add Torch probe entries and Pi artifact sync

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Курнат Андрей
2026-06-24 21:31:05 +03:00
parent 15a50fb575
commit cb8efb7cd7
16 changed files with 307266 additions and 363669 deletions
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crypto_spot_bot/strategy.py
+35 -2
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@@ -633,6 +633,34 @@ def _torch_forecast_entry_signal(
skill = _safe_float(forecast.get("skill"), 0.0)
min_edge = max(0.0, settings.time_series_min_edge_percent)
min_probability = _torch_min_probability(settings)
probe_min_edge = max(0.0, min(settings.time_series_probe_min_edge_percent, min_edge))
probe_min_probability = round(
_clamp(settings.time_series_probe_min_probability_up, min_probability, 0.85),
4,
)
full_edge_ok = expected_return >= min_edge
probe_edge_ok = bool(
settings.time_series_probe_enabled
and not full_edge_ok
and expected_return >= probe_min_edge
and probability_up >= probe_min_probability
)
edge_mode = "full" if full_edge_ok else ("probe" if probe_edge_ok else "blocked")
if probe_edge_ok and position_notional > 0:
probe_multiplier = _clamp(settings.time_series_probe_size_multiplier, 0.05, 1.0)
position_notional = round(
min(
settings.max_position_usdt,
max(settings.min_position_usdt, position_notional * probe_multiplier),
),
2,
)
sizing = {
**sizing,
"notional_usdt": position_notional,
"probe_size_multiplier": round(probe_multiplier, 4),
"edge_mode": "probe",
}
confidence = _torch_forecast_confidence(settings, forecast)
spread_ok = ticker.spread_percent <= settings.max_spread_percent
liquidity_ok = ticker.turnover_24h >= settings.min_24h_turnover_usdt
@@ -641,7 +669,7 @@ def _torch_forecast_entry_signal(
"torch_model_ok": model_ok,
"forecast_usable": bool(forecast.get("usable", False)),
"forecast_not_blocked": not bool(forecast.get("block_entry", False)),
"expected_edge_ok": expected_return >= min_edge,
"expected_edge_ok": full_edge_ok or probe_edge_ok,
"probability_ok": probability_up >= min_probability,
"skill_ok": skill > 0.0,
"confidence_ok": confidence >= settings.time_series_min_confidence,
@@ -659,6 +687,10 @@ def _torch_forecast_entry_signal(
"atr_trailing_multiplier": _clamp(settings.atr_trailing_multiplier, 0.5, 10.0),
"expected_return_percent": expected_return,
"min_edge_percent": min_edge,
"probe_enabled": settings.time_series_probe_enabled,
"probe_min_edge_percent": probe_min_edge,
"probe_min_probability_up": probe_min_probability,
"edge_mode": edge_mode,
"probability_up": probability_up,
"min_probability_up": min_probability,
"min_confidence": settings.time_series_min_confidence,
@@ -679,7 +711,8 @@ def _torch_forecast_entry_signal(
(
"torch_forecast: PyTorch edge confirmed; "
f"model={forecast.get('model')}, p_up={probability_up:.3f}, "
f"expected={expected_return:.4f}%, size={position_notional:.2f} USDT"
f"expected={expected_return:.4f}%, edge_mode={edge_mode}, "
f"size={position_notional:.2f} USDT"
),
diagnostics,
)