Initial commit - BTC Trading Dashboard
- FastAPI backend with PostgreSQL database connection - Frontend dashboard with lightweight-charts - Technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands, etc.) - Trading strategy simulation and backtesting - Database connection to NAS at 20.20.20.20:5433 - Development server setup and documentation
This commit is contained in:
DiTus
226
src/data_collector/brain.py
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226
src/data_collector/brain.py
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"""
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Brain - Strategy evaluation and decision logging
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Pure strategy logic separated from DB I/O for testability
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"""
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import json
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import logging
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from dataclasses import dataclass
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from datetime import datetime, timezone
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from typing import Dict, Optional, Any, List, Callable
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from .database import DatabaseManager
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from .indicator_engine import IndicatorEngine
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from src.strategies.base import BaseStrategy, StrategySignal, SignalType
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from src.strategies.ma_strategy import MAStrategy
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logger = logging.getLogger(__name__)
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def _create_ma44() -> BaseStrategy:
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return MAStrategy(config={"period": 44})
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def _create_ma125() -> BaseStrategy:
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return MAStrategy(config={"period": 125})
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STRATEGY_REGISTRY: Dict[str, Callable[[], BaseStrategy]] = {
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"ma_trend": MAStrategy,
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"ma44_strategy": _create_ma44,
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"ma125_strategy": _create_ma125,
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}
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def load_strategy(strategy_name: str) -> BaseStrategy:
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"""Load a strategy instance from registry"""
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if strategy_name not in STRATEGY_REGISTRY:
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logger.warning(f"Strategy {strategy_name} not found, defaulting to ma_trend")
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strategy_name = "ma_trend"
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factory = STRATEGY_REGISTRY[strategy_name]
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return factory()
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@dataclass
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class Decision:
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"""A single brain evaluation result"""
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time: datetime
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symbol: str
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interval: str
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decision_type: str # "buy", "sell", "hold" -> Now maps to SignalType
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strategy: str
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confidence: float
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price_at_decision: float
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indicator_snapshot: Dict[str, Any]
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candle_snapshot: Dict[str, Any]
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reasoning: str
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backtest_id: Optional[str] = None
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def to_db_tuple(self) -> tuple:
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"""Convert to positional tuple for DB insert"""
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return (
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self.time,
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self.symbol,
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self.interval,
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self.decision_type,
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self.strategy,
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self.confidence,
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self.price_at_decision,
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json.dumps(self.indicator_snapshot),
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json.dumps(self.candle_snapshot),
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self.reasoning,
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self.backtest_id,
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)
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class Brain:
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"""
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Evaluates market conditions using a loaded Strategy.
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"""
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def __init__(
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self,
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db: DatabaseManager,
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indicator_engine: IndicatorEngine,
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strategy: str = "ma44_strategy",
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):
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self.db = db
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self.indicator_engine = indicator_engine
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self.strategy_name = strategy
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self.active_strategy: BaseStrategy = load_strategy(strategy)
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logger.info(f"Brain initialized with strategy: {self.active_strategy.name}")
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async def evaluate(
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self,
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symbol: str,
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interval: str,
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timestamp: datetime,
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indicators: Optional[Dict[str, float]] = None,
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backtest_id: Optional[str] = None,
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current_position: Optional[Dict[str, Any]] = None,
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) -> Decision:
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"""
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Evaluate market conditions and produce a decision.
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"""
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# Get indicator values
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if indicators is None:
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indicators = await self.indicator_engine.get_values_at(
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symbol, interval, timestamp
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)
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# Get the triggering candle
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candle = await self._get_candle(symbol, interval, timestamp)
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if not candle:
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return self._create_empty_decision(timestamp, symbol, interval, indicators, backtest_id)
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price = float(candle["close"])
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candle_dict = {
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"time": candle["time"].isoformat(),
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"open": float(candle["open"]),
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"high": float(candle["high"]),
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"low": float(candle["low"]),
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"close": price,
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"volume": float(candle["volume"]),
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}
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# Delegate to Strategy
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signal: StrategySignal = self.active_strategy.analyze(
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candle_dict, indicators, current_position
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)
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# Build decision
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decision = Decision(
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time=timestamp,
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symbol=symbol,
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interval=interval,
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decision_type=signal.type.value,
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strategy=self.strategy_name,
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confidence=signal.confidence,
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price_at_decision=price,
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indicator_snapshot=indicators,
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candle_snapshot=candle_dict,
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reasoning=signal.reasoning,
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backtest_id=backtest_id,
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)
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# Store to DB
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await self._store_decision(decision)
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return decision
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def _create_empty_decision(self, timestamp, symbol, interval, indicators, backtest_id):
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return Decision(
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time=timestamp,
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symbol=symbol,
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interval=interval,
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decision_type="hold",
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strategy=self.strategy_name,
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confidence=0.0,
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price_at_decision=0.0,
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indicator_snapshot=indicators or {},
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candle_snapshot={},
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reasoning="No candle data available",
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backtest_id=backtest_id,
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)
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async def _get_candle(
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self,
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symbol: str,
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interval: str,
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timestamp: datetime,
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) -> Optional[Dict[str, Any]]:
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"""Fetch a specific candle from the database"""
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async with self.db.acquire() as conn:
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row = await conn.fetchrow("""
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SELECT time, open, high, low, close, volume
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FROM candles
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WHERE symbol = $1 AND interval = $2 AND time = $3
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""", symbol, interval, timestamp)
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return dict(row) if row else None
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async def _store_decision(self, decision: Decision) -> None:
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"""Write decision to the decisions table"""
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# Note: We might want to skip writing every single HOLD to DB to save space if simulating millions of candles
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# But keeping it for now for full traceability
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async with self.db.acquire() as conn:
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await conn.execute("""
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INSERT INTO decisions (
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time, symbol, interval, decision_type, strategy,
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confidence, price_at_decision, indicator_snapshot,
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candle_snapshot, reasoning, backtest_id
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)
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VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11)
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""", *decision.to_db_tuple())
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async def get_recent_decisions(
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self,
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symbol: str,
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limit: int = 20,
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backtest_id: Optional[str] = None,
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) -> List[Dict[str, Any]]:
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"""Get recent decisions, optionally filtered by backtest_id"""
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async with self.db.acquire() as conn:
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if backtest_id is not None:
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rows = await conn.fetch("""
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SELECT time, symbol, interval, decision_type, strategy,
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confidence, price_at_decision, indicator_snapshot,
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candle_snapshot, reasoning, backtest_id
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FROM decisions
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WHERE symbol = $1 AND backtest_id = $2
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ORDER BY time DESC
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LIMIT $3
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""", symbol, backtest_id, limit)
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else:
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rows = await conn.fetch("""
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SELECT time, symbol, interval, decision_type, strategy,
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confidence, price_at_decision, indicator_snapshot,
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candle_snapshot, reasoning, backtest_id
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FROM decisions
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WHERE symbol = $1 AND backtest_id IS NULL
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ORDER BY time DESC
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LIMIT $2
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""", symbol, limit)
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return [dict(row) for row in rows]
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def reset_state(self) -> None:
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"""Reset internal state tracking"""
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pass
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