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68025ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | 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,
}
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