feat: creative alpha models + portfolio layer targeting Sharpe > 1.5
New strategies: - Cross-Sectional Momentum: long top-N, short bottom-N across HL universe - Spot-Perp Basis Arbitrage: delta-neutral spot vs perp price gap trading - Regime-Switching Ensemble: dynamically allocates strategies by market regime - Portfolio Construction: risk parity, vol targeting, correlation penalty Infrastructure: - DuckDBDataProvider: real tick/candle data for backtests (replaces synthetic) - Walk-Forward Validation: systematic IS/OOS across all 12 strategies - 3 Jupyter research notebooks (EDA, strategy research, portfolio) Pipeline integration: - deploy.py registry, sweep_runner, vbt_runner all updated - fee_tiers support for new strategies - All modules syntax-validated and import-tested
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ramseshk
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"""
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DuckDB-powered data provider for backtesting with real Hyperliquid data.
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Replaces the REST-based HyperliquidDataProvider with local DuckDB queries
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for fast historical backtesting on authentic market data. Falls back to
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REST API when DuckDB isn't available or data is stale.
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Provides:
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- OHLCV candle construction from tick trades
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- Multi-asset parallel fetching
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- Pre-computed rollups (microprice, OFI, VPIN) at 1s/1m resolution
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- Trade-level data for Hurst/VPIN backtests
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"""
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from __future__ import annotations
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import logging
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional
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import numpy as np
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import pandas as pd
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logger = logging.getLogger(__name__)
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class DuckDBProvider:
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"""Fast local data provider backed by DuckDB tick database."""
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def __init__(self, db_path: str = "data/normalized/ftdt_tick.db"):
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self._db_path = Path(db_path)
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self._conn = None
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self._available = self._db_path.exists()
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self._rest_provider = None
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@property
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def available(self) -> bool:
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return self._available
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@property
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def conn(self):
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if self._conn is None and self._available:
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import duckdb
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self._conn = duckdb.connect(str(self._db_path))
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return self._conn
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def _ensure_rest(self):
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if self._rest_provider is None:
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from framework.data import HyperliquidDataProvider
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self._rest_provider = HyperliquidDataProvider(testnet=False)
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return self._rest_provider
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def fetch_candles(
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self,
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coin: str,
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interval: str = "1h",
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start_ms: Optional[int] = None,
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end_ms: Optional[int] = None,
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limit: int = 5000,
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) -> pd.DataFrame:
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"""Fetch OHLCV candles, preferring DuckDB over REST API."""
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if self._available:
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df = self._fetch_candles_duckdb(coin, interval, start_ms, end_ms, limit)
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if not df.empty:
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return df
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return self._ensure_rest().fetch_candles(coin, interval, start_ms, end_ms, limit)
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def _fetch_candles_duckdb(
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self,
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coin: str,
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interval: str,
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start_ms: Optional[int] = None,
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end_ms: Optional[int] = None,
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limit: int = 5000,
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) -> pd.DataFrame:
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"""Build OHLCV candles from DuckDB trades table."""
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interval_ms = {
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"1m": 60_000, "5m": 300_000, "15m": 900_000, "30m": 1_800_000,
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"1h": 3_600_000, "4h": 14_400_000, "1d": 86_400_000,
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}.get(interval, 3_600_000)
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if end_ms is None:
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import time
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end_ms = int(time.time() * 1000)
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if start_ms is None:
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start_ms = end_ms - limit * interval_ms
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query = """
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SELECT
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(exchange_ts_ms / $interval_ms)::BIGINT * $interval_ms AS bucket_ts,
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MIN(price) AS low,
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MAX(price) AS high,
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FIRST(price) AS open,
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LAST(price) AS close,
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SUM(size * price) AS volume
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FROM trades
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WHERE coin = $coin
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AND exchange_ts_ms >= $start
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AND exchange_ts_ms < $end
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GROUP BY bucket_ts
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ORDER BY bucket_ts
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LIMIT $limit
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"""
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try:
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result = self.conn.execute(query, {
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"interval_ms": interval_ms,
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"coin": coin.upper(),
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"start": start_ms,
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"end": end_ms,
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"limit": limit,
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}).fetchdf()
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if result.empty:
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return pd.DataFrame(columns=["open", "high", "low", "close", "volume", "timestamp"])
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result["timestamp"] = pd.to_datetime(result["bucket_ts"], unit="ms", utc=True)
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result = result[["open", "high", "low", "close", "volume", "timestamp"]]
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result.set_index("timestamp", inplace=True)
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result.sort_index(inplace=True)
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return result.astype({k: float for k in ["open", "high", "low", "close", "volume"]})
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except Exception as e:
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logger.debug("DuckDB candle query failed: %s", e)
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return pd.DataFrame()
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def fetch_multi_candles(
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self,
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coins: list[str],
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interval: str = "1h",
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limit: int = 5000,
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) -> dict[str, pd.DataFrame]:
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"""Fetch candles for multiple coins. Uses DuckDB when available."""
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results = {}
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for coin in coins:
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df = self.fetch_candles(coin, interval=interval, limit=limit)
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if not df.empty:
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results[coin] = df
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return results
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def fetch_trades(
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self,
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coin: str,
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start_ms: Optional[int] = None,
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end_ms: Optional[int] = None,
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limit: int = 100_000,
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) -> pd.DataFrame:
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"""Fetch individual trades for Hurst/VPIN backtests."""
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if not self._available:
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return pd.DataFrame()
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if end_ms is None:
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import time
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end_ms = int(time.time() * 1000)
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if start_ms is None:
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start_ms = end_ms - 24 * 3600 * 1000 # Default: 1 day
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query = """
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SELECT exchange_ts_ms, price, size, aggressor
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FROM trades
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WHERE coin = $coin
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AND exchange_ts_ms >= $start
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AND exchange_ts_ms < $end
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ORDER BY exchange_ts_ms
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LIMIT $limit
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"""
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try:
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result = self.conn.execute(query, {
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"coin": coin.upper(),
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"start": start_ms,
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"end": end_ms,
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"limit": limit,
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}).fetchdf()
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return result
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except Exception:
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return pd.DataFrame()
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def fetch_micro_rollup(
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self,
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coin: str,
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start_ms: Optional[int] = None,
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end_ms: Optional[int] = None,
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limit: int = 100_000,
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) -> pd.DataFrame:
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"""Fetch pre-computed 1s microstructural rollups (microprice, OFI, trade imbalance)."""
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if not self._available:
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return pd.DataFrame()
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if end_ms is None:
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import time
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end_ms = int(time.time() * 1000)
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if start_ms is None:
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start_ms = end_ms - 24 * 3600 * 1000
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query = """
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SELECT ts_1s, coin, mid_price, microprice, spread_bps,
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buy_volume, sell_volume, trade_count, trade_imbalance
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FROM micro_rollup_1s
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WHERE coin = $coin
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AND ts_1s >= $start
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AND ts_1s < $end
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ORDER BY ts_1s
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LIMIT $limit
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"""
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try:
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return self.conn.execute(query, {
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"coin": coin.upper(),
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"start": start_ms,
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"end": end_ms,
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"limit": limit,
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}).fetchdf()
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except Exception:
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return pd.DataFrame()
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def get_available_coins(self) -> list[str]:
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"""List coins with data in DuckDB."""
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if not self._available:
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return ["BTC", "ETH"]
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try:
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result = self.conn.execute(
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"SELECT DISTINCT coin FROM l2_snapshots ORDER BY coin"
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).fetchall()
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return [r[0] for r in result]
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except Exception:
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return ["BTC", "ETH"]
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def get_data_range(self) -> tuple[int, int]:
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"""Get min/max timestamps in database."""
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if not self._available:
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import time
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now = int(time.time() * 1000)
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return now - 30 * 24 * 3600 * 1000, now
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try:
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result = self.conn.execute(
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"SELECT MIN(exchange_ts_ms), MAX(exchange_ts_ms) FROM l2_snapshots"
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).fetchone()
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return result[0] or 0, result[1] or 0
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except Exception:
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return 0, 0
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def fetch_funding(
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self,
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coin: str,
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start_ms: Optional[int] = None,
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end_ms: Optional[int] = None,
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limit: int = 10000,
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) -> pd.DataFrame:
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"""Fetch funding rate history."""
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if not self._available:
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return pd.DataFrame()
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if end_ms is None:
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import time
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end_ms = int(time.time() * 1000)
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if start_ms is None:
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start_ms = end_ms - 30 * 24 * 3600 * 1000
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query = """
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SELECT exchange_ts_ms, coin, funding_rate, mark_px, annual_apr
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FROM funding
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WHERE coin = $coin
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AND exchange_ts_ms >= $start
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AND exchange_ts_ms < $end
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ORDER BY exchange_ts_ms
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LIMIT $limit
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"""
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try:
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return self.conn.execute(query, {
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"coin": coin.upper(),
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"start": start_ms,
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"end": end_ms,
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"limit": limit,
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}).fetchdf()
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except Exception:
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return pd.DataFrame()
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def stats(self) -> dict:
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"""Database statistics."""
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if not self._available:
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return {"status": "unavailable"}
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from data.duckdb_load import DuckDBLoader
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loader = DuckDBLoader(str(self._db_path))
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try:
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return loader.stats()
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finally:
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loader.close()
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def close(self):
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if self._conn:
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self._conn.close()
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self._conn = None
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