158 lines
6.5 KiB
Python
158 lines
6.5 KiB
Python
from apscheduler.schedulers.background import BackgroundScheduler
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from datetime import datetime, timedelta
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import json
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from sqlalchemy import func
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from config import settings
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from db import SessionLocal, BotDecision, TradeExecution, CuratedInsight
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from services import (
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searx_news,
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extra_research,
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summarize_news_with_ollama,
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strategy_signals,
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llm_final_decision,
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place_order,
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market_open,
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positions_snapshot,
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qdrant_similar,
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qdrant_add_memory,
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trilium_log,
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n8n_emit,
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)
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scheduler = BackgroundScheduler(timezone=settings.timezone)
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def _daily_spent(db):
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since = datetime.utcnow() - timedelta(hours=24)
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return float(db.query(func.coalesce(func.sum(TradeExecution.notional), 0)).filter(TradeExecution.ts >= since).scalar() or 0)
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def curate_cycle():
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db = SessionLocal()
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try:
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for symbol in settings.symbols:
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news = searx_news(symbol)
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summary = summarize_news_with_ollama(symbol, news)
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row = CuratedInsight(symbol=symbol, summary=summary, sources=json.dumps(news)[:60000])
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db.add(row)
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db.commit()
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finally:
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db.close()
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def run_cycle():
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db = SessionLocal()
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try:
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if not market_open() and not (settings.paper_mode and settings.force_paper_trades):
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return
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spent = _daily_spent(db)
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if spent >= settings.max_daily_notional:
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return
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pos = positions_snapshot()
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if len(pos) >= settings.max_open_positions:
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return
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for symbol in settings.symbols:
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if spent >= settings.max_daily_notional:
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break
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news = searx_news(symbol)
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strat = strategy_signals(symbol, news)
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memory_hits = qdrant_similar(symbol, json.dumps(news)[:2000], limit=5)
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decision = llm_final_decision(symbol, news, strat, memory_hits)
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if decision.get("needs_more_research") or decision.get("confidence", 0) < settings.min_confidence:
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more = extra_research(symbol, decision.get("research_topics", []))
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if more:
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news = news + more
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memory_hits = qdrant_similar(symbol, json.dumps(news)[:2000], limit=5)
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decision = llm_final_decision(symbol, news, strat, memory_hits)
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if settings.paper_mode and settings.force_paper_trades and decision["action"] == "hold":
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decision["action"] = "buy"
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decision["confidence"] = max(decision.get("confidence", 0.0), settings.min_confidence)
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decision["order_usd"] = max(decision.get("order_usd", 0.0), settings.force_paper_min_usd)
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decision["reason"] = f"{decision.get('reason','')} | force_paper_trades"
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drow = BotDecision(
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symbol=symbol,
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action=decision["action"],
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confidence=decision["confidence"],
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reason=f"{decision['reason']} | strat={strat['strategy']} score={strat['score']}",
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market_context=json.dumps(news)[:60000],
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order_usd=decision["order_usd"],
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status="planned",
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)
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db.add(drow)
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db.commit()
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db.refresh(drow)
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should_trade = decision["action"] in {"buy", "sell"} and decision["confidence"] >= settings.min_confidence
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if should_trade:
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effective_cost = settings.fee_per_trade_usd + (settings.slippage_bps / 10000.0) * decision["order_usd"]
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notional = min(settings.max_order_usd, decision["order_usd"], settings.max_daily_notional - spent)
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if notional <= effective_cost:
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drow.status = "risk_blocked"
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db.add(drow)
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db.commit()
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continue
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res = place_order(symbol, decision["action"], notional)
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ok = bool(res and res.get("ok"))
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drow.status = "executed" if ok else "failed"
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db.add(drow)
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if ok:
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trade = TradeExecution(
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symbol=symbol,
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side=decision["action"],
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qty=float((res or {}).get("json", {}).get("qty", 0) or 0),
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notional=notional,
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alpaca_order_id=(res or {}).get("json", {}).get("id", ""),
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raw=json.dumps({"decision": decision, "strategy": strat, "memory": memory_hits, "broker": res})[:60000],
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)
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db.add(trade)
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db.commit()
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# learning memory + notes + orchestration signal
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qdrant_add_memory(symbol, f"{symbol} {decision['action']} conf={decision['confidence']} reason={decision['reason']}", {
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"status": drow.status,
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"action": decision["action"],
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"confidence": decision["confidence"],
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"ts": datetime.utcnow().isoformat(),
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})
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trilium_log(
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f"Trade {symbol} {decision['action']} {drow.status}",
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f"## Decision\n- symbol: {symbol}\n- action: {decision['action']}\n- confidence: {decision['confidence']:.2f}\n- status: {drow.status}\n- notional: ${notional:.2f}\n\n## Reason\n{decision['reason']}\n\n## Strategy\n{json.dumps(strat, indent=2)}\n"
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)
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n8n_emit({"event": "trade", "symbol": symbol, "status": drow.status, "decision": decision, "notional": notional})
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if ok:
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spent += notional
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else:
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drow.status = "skipped"
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db.add(drow)
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db.commit()
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# store non-trade decisions too for memory
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qdrant_add_memory(symbol, f"{symbol} decision={decision['action']} conf={decision['confidence']} status={drow.status}", {
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"status": drow.status,
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"action": decision["action"],
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"confidence": decision["confidence"],
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"ts": datetime.utcnow().isoformat(),
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})
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finally:
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db.close()
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def start_scheduler():
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scheduler.add_job(curate_cycle, "interval", minutes=settings.curate_interval_minutes, id="curate_cycle", replace_existing=True)
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if settings.trade_interval_minutes and settings.trade_interval_minutes > 0:
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scheduler.add_job(run_cycle, "interval", minutes=settings.trade_interval_minutes, id="trade_cycle", replace_existing=True)
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else:
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scheduler.add_job(run_cycle, "interval", hours=settings.trade_interval_hours, id="trade_cycle", replace_existing=True)
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scheduler.start()
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