Курнат Андрей
227 lines
8.6 KiB
Python
227 lines
8.6 KiB
Python
from __future__ import annotations
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import json
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from dataclasses import asdict, dataclass, field
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from typing import Any
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import requests
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from crypto_spot_bot.config import Settings
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from crypto_spot_bot.models import Candle, Ticker, utc_now
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from crypto_spot_bot.storage import Storage
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REGIMES = {"uptrend", "downtrend", "range", "breakout", "breakdown", "panic", "unknown"}
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RISK_LEVELS = {"low", "medium", "high"}
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@dataclass(slots=True)
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class LlmAdvice:
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symbol: str
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enabled: bool
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model: str
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market_regime: str = "unknown"
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risk_level: str = "medium"
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confidence_adjustment: float = 0.0
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block_entry: bool = False
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grid_suitable: bool = False
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reason_ru: str = "LLM Advisor не дал активной поправки."
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error: str = ""
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created_at: str = field(default_factory=lambda: utc_now().isoformat())
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def as_dict(self) -> dict[str, Any]:
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return asdict(self)
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class LlmAdvisor:
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def __init__(self, settings: Settings, storage: Storage):
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self.settings = settings
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self.storage = storage
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self._cache: dict[str, LlmAdvice] = {}
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def advice_for(
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self,
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*,
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symbol: str,
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candles: list[Candle],
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ticker: Ticker | None,
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pattern: dict[str, Any],
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learning: dict[str, Any],
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open_positions_for_symbol: int,
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account: dict[str, float],
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) -> LlmAdvice:
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if not self.settings.llm_advisor_enabled:
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return LlmAdvice(
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symbol=symbol,
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enabled=False,
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model=self.settings.ollama_model,
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reason_ru="LLM Advisor выключен.",
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)
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cached = self._cache.get(symbol)
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if cached and _age_seconds(cached.created_at) < self.settings.llm_advisor_min_interval_seconds:
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return cached
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context = _build_context(symbol, candles, ticker, pattern, learning, open_positions_for_symbol, account)
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prompt = _prompt(context, self.settings.llm_advisor_max_adjustment)
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response_text = ""
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error = ""
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try:
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response = requests.post(
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f"{self.settings.ollama_base_url}/api/generate",
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json={
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"model": self.settings.ollama_model,
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"prompt": prompt,
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"stream": False,
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"options": {"temperature": 0.1},
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},
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timeout=self.settings.llm_advisor_timeout_seconds,
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)
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response.raise_for_status()
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payload = response.json()
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response_text = str(payload.get("response", ""))
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advice = self._parse(symbol, response_text)
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except Exception as exc:
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error = str(exc)
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advice = LlmAdvice(
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symbol=symbol,
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enabled=True,
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model=self.settings.ollama_model,
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reason_ru="LLM Advisor временно недоступен; используется нейтральная поправка.",
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error=error,
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)
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self._cache[symbol] = advice
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self.storage.insert_llm_advice(
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symbol=symbol,
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model=self.settings.ollama_model,
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prompt_json=context,
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response_text=response_text,
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advice_json=advice.as_dict(),
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error=error or advice.error,
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)
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return advice
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def snapshot(self) -> dict[str, Any]:
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return {
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"enabled": self.settings.llm_advisor_enabled,
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"base_url": self.settings.ollama_base_url,
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"model": self.settings.ollama_model,
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"min_interval_seconds": self.settings.llm_advisor_min_interval_seconds,
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"max_adjustment": self.settings.llm_advisor_max_adjustment,
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"items": [advice.as_dict() for advice in self._cache.values()],
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}
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def _parse(self, symbol: str, response_text: str) -> LlmAdvice:
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data = _extract_json(response_text)
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regime = str(data.get("market_regime", "unknown")).strip().lower()
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risk = str(data.get("risk_level", "medium")).strip().lower()
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adjustment = _clamp_float(
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data.get("confidence_adjustment", 0.0),
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-self.settings.llm_advisor_max_adjustment,
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self.settings.llm_advisor_max_adjustment,
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)
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return LlmAdvice(
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symbol=symbol,
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enabled=True,
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model=self.settings.ollama_model,
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market_regime=regime if regime in REGIMES else "unknown",
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risk_level=risk if risk in RISK_LEVELS else "medium",
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confidence_adjustment=adjustment,
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block_entry=bool(data.get("block_entry", False)),
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grid_suitable=bool(data.get("grid_suitable", False)),
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reason_ru=str(data.get("reason_ru", "LLM Advisor не объяснил вывод."))[:240],
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)
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def _build_context(
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symbol: str,
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candles: list[Candle],
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ticker: Ticker | None,
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pattern: dict[str, Any],
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learning: dict[str, Any],
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open_positions_for_symbol: int,
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account: dict[str, float],
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) -> dict[str, Any]:
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latest = candles[-1] if candles else None
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return {
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"mode": "paper_demo_only",
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"symbol": symbol,
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"objective": "reduce avoidable losing spot-long entries; do not promise profit",
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"market": {
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"last_price": ticker.last_price if ticker else None,
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"spread_percent": ticker.spread_percent if ticker else None,
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"turnover_24h": ticker.turnover_24h if ticker else None,
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"change_24h": ticker.change_24h if ticker else None,
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"close": latest.close if latest else None,
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"rsi_14": latest.rsi_14 if latest else None,
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"ema_20": latest.ema_20 if latest else None,
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"ema_50": latest.ema_50 if latest else None,
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"ema_200": latest.ema_200 if latest else None,
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"atr_14": latest.atr_14 if latest else None,
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"volume": latest.volume if latest else None,
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"volume_ma_20": latest.volume_ma_20 if latest else None,
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},
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"pattern": pattern,
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"learning": learning,
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"risk_state": {
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"equity": account.get("equity"),
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"cash": account.get("cash"),
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"exposure": account.get("exposure"),
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"drawdown": account.get("drawdown"),
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"open_positions_for_symbol": open_positions_for_symbol,
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},
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"allowed_output": {
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"market_regime": sorted(REGIMES),
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"risk_level": sorted(RISK_LEVELS),
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"confidence_adjustment": "number within configured bounds",
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"block_entry": "boolean; can only block buy, never force buy",
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"grid_suitable": "boolean",
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"reason_ru": "short Russian explanation",
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},
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}
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def _prompt(context: dict[str, Any], max_adjustment: float) -> str:
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return (
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"Ты LLM Advisor для paper-only crypto spot LONG бота. "
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"Ты не открываешь сделки и не обещаешь прибыль. "
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"Верни только валидный JSON без markdown. "
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f"confidence_adjustment должен быть от {-max_adjustment:.4f} до {max_adjustment:.4f}. "
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"Если рынок падающий, шаблон отрицательный или обучение убыточное, используй отрицательную поправку или block_entry=true. "
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"Если боковик и риск умеренный, можешь отметить grid_suitable=true. "
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"JSON keys: market_regime, risk_level, confidence_adjustment, block_entry, grid_suitable, reason_ru. "
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f"Context: {json.dumps(context, ensure_ascii=False, separators=(',', ':'))}"
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)
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def _extract_json(text: str) -> dict[str, Any]:
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stripped = text.strip()
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if stripped.startswith("```"):
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stripped = stripped.strip("`").strip()
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if stripped.lower().startswith("json"):
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stripped = stripped[4:].strip()
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try:
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data = json.loads(stripped)
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except json.JSONDecodeError:
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start = stripped.find("{")
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end = stripped.rfind("}")
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if start < 0 or end <= start:
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raise
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data = json.loads(stripped[start : end + 1])
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if not isinstance(data, dict):
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raise ValueError("LLM response JSON is not an object")
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return data
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def _clamp_float(value: Any, low: float, high: float) -> float:
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try:
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parsed = float(value)
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except (TypeError, ValueError):
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parsed = 0.0
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return round(max(low, min(high, parsed)), 4)
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def _age_seconds(created_at: str) -> float:
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from datetime import datetime
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return (utc_now() - datetime.fromisoformat(created_at)).total_seconds()
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