backtest/runner.py

import logging
from datetime import datetime
from data.prices import fetch_ohlcv, ohlcv_to_records
from data.indicators import compute_indicators, get_latest_indicators
from data.news import fetch_news
from data.onchain import fetch_onchain_data
from backtest.portfolio import Portfolio, compute_metrics
from agents.pipeline import build_pipeline

logger = logging.getLogger(__name__)


def run_backtest(
    benchmark: str,
    model: str,
    asset: str,
    start_date: str,
    end_date: str,
    progress_callback=None,
) -> dict:
    """
    Full backtest loop. Returns dict with metrics, equity_curve, decisions, hodl_curve.
    """
    logger.info(f"Starting backtest: benchmark={benchmark} model={model} asset={asset} {start_date}->{end_date}")

    # Fetch and prepare price data
    df_raw = fetch_ohlcv(asset, start_date, end_date)
    df = compute_indicators(df_raw)

    if df.empty or len(df) < 2:
        raise ValueError(f"Insufficient data for {asset} from {start_date} to {end_date}")

    ohlcv_records = ohlcv_to_records(df)

    # Portfolio for agent strategy
    portfolio = Portfolio()

    # HODL portfolio (buy on day 1, hold)
    hodl_portfolio = Portfolio()
    first_price = float(df.iloc[0]["close"])
    hodl_portfolio.apply_decision({"action": "BUY", "size": 1.0}, first_price, str(df.iloc[0]["date"]))

    decisions_log = []
    pipeline = build_pipeline(benchmark, model)

    total_days = len(df)

    for i, row in df.iterrows():
        date = str(row["date"])
        price = float(row["close"])

        # Build market_data context (data available up to this day)
        recent_records = ohlcv_records[: i + 1]
        indicators = get_latest_indicators(df.iloc[: i + 1])

        portfolio_snapshot = portfolio.snapshot(price)

        market_data = {
            "asset": asset,
            "current_price": price,
            "date": date,
            "recent_ohlcv": recent_records[-30:],  # last 30 days
            "indicators": indicators,
            "portfolio": portfolio_snapshot,
        }

        # Add news/onchain for benchmarks B and C (only in live-style; skip for backtest speed)
        if benchmark in ("B", "C"):
            try:
                news = fetch_news(asset, date=date, limit=5)
                market_data["news"] = news
            except Exception as e:
                logger.warning(f"News fetch failed for {date}: {e}")
                market_data["news"] = []

        if benchmark == "C":
            try:
                onchain = fetch_onchain_data(asset)
                market_data["onchain"] = onchain
            except Exception as e:
                logger.warning(f"On-chain fetch failed for {date}: {e}")
                market_data["onchain"] = {}

        # Get decision from pipeline
        try:
            result = pipeline.decide(market_data)
            decision = result["decision"]
            agent_outputs = result.get("agent_outputs", {})
        except Exception as e:
            logger.error(f"Pipeline error on {date}: {e}")
            decision = {"action": "HOLD", "size": 0.0, "confidence": 0.0, "reason": f"Error: {e}"}
            agent_outputs = {}

        # Apply to portfolio
        portfolio.apply_decision(decision, price, date)

        # Update HODL
        hodl_portfolio.equity_history.append({
            "date": date,
            "value": round(hodl_portfolio.cash + hodl_portfolio.position * price, 2),
            "price": price,
            "action": "HOLD",
            "trade_executed": False,
            "trade_value": 0.0,
            "cash": hodl_portfolio.cash,
            "position": hodl_portfolio.position,
        })

        decisions_log.append({
            "date": date,
            "price": price,
            "action": decision.get("action"),
            "size": decision.get("size"),
            "confidence": decision.get("confidence"),
            "reason": decision.get("reason"),
            "agent_outputs": agent_outputs,
            "portfolio_value": portfolio_snapshot["total_value"],
        })

        if progress_callback:
            progress_callback(i + 1, total_days)

        logger.debug(f"{date} | {asset} | {decision.get('action')} | price={price:.2f} | portfolio={portfolio_snapshot['total_value']:.2f}")

    # Final metrics
    hodl_final = hodl_portfolio.equity_history[-1]["value"] if hodl_portfolio.equity_history else portfolio.initial_capital
    metrics = compute_metrics(portfolio.equity_history, portfolio.initial_capital, hodl_final)

    hodl_curve = [{"date": e["date"], "value": e["value"]} for e in hodl_portfolio.equity_history]
    equity_curve = [{"date": e["date"], "value": e["value"], "action": e.get("action", "HOLD")} for e in portfolio.equity_history]

    return {
        "benchmark": benchmark,
        "model": model,
        "asset": asset,
        "start_date": start_date,
        "end_date": end_date,
        "metrics": metrics,
        "equity_curve": equity_curve,
        "hodl_curve": hodl_curve,
        "decisions": decisions_log,
    }