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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"cells": [
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{
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"cell_type": "markdown",
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"id": "2094c7c7",
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"metadata": {},
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"source": [
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"# FTDT Quant Lab — Exploratory Data Analysis\n",
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"\n",
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"**Goal:** Understand Hyperliquid market microstructure, identify alpha sources, verify data quality.\n",
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"\n",
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"**Assets:** BTC, ETH, SOL, HYPE, ARB, OP, and others \n",
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"**Data Sources:** DuckDB tick database, HL REST API, Parquet raw store \n",
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"**Timeframe:** 1s tick → 1h candles → daily aggregation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eabc713b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Setup\n",
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"import sys\n",
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"from pathlib import Path\n",
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"sys.path.insert(0, str(Path.cwd().parent))\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"from data.duckdb_provider import DuckDBProvider\n",
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"from framework.data import HyperliquidDataProvider\n",
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"\n",
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"sns.set_theme(style=\"darkgrid\")\n",
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"plt.rcParams[\"figure.figsize\"] = (14, 6)\n",
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"plt.rcParams[\"figure.dpi\"] = 100\n",
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"\n",
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"# Data providers\n",
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"duckdb = DuckDBProvider()\n",
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"hl_rest = HyperliquidDataProvider(testnet=False)\n",
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"\n",
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"print(f\"DuckDB available: {duckdb.available}\")\n",
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"print(f\"Data range: {duckdb.get_data_range()}\")\n",
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"print(f\"Available coins: {duckdb.get_available_coins()}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bed20cce",
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"metadata": {},
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"source": [
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"## 1. Universe Overview\n",
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"\n",
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"What assets are available and how much data do we have for each?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ad451959",
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"metadata": {},
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"outputs": [],
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"source": [
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"from strategies.cross_sectional_momentum import HL_UNIVERSE, HIGH_LIQUIDITY\n",
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"\n",
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"print(f\"Full universe ({len(HL_UNIVERSE)} assets): {HL_UNIVERSE}\")\n",
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"print(f\"High liquidity ({len(HIGH_LIQUIDITY)}): {HIGH_LIQUIDITY}\")\n",
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"\n",
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"# Fetch candle data for each asset\n",
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"prices = duckdb.fetch_multi_candles(HIGH_LIQUIDITY, interval='1h', limit=500)\n",
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"\n",
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"print(f\"\\nData availability:\")\n",
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"for coin, df in prices.items():\n",
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" if not df.empty:\n",
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" print(f\" {coin:6s}: {len(df):5d} bars | {df.index[0]} to {df.index[-1]} | close=${df['close'].iloc[-1]:.2f}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c2eef87d",
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"metadata": {},
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"source": [
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"## 2. Return Distributions\n",
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"\n",
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"Check return distributions for normality, skew, kurtosis, and tail behavior.\n",
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"This informs strategy design — mean reversion works on platykurtic distributions,\n",
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"momentum thrives on leptokurtic tails."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f0c7e04e",
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"metadata": {},
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"outputs": [],
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"source": [
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"returns_data = {}\n",
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"for coin in HIGH_LIQUIDITY:\n",
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" df = prices.get(coin)\n",
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" if df is None or df.empty:\n",
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" continue\n",
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" rets = df['close'].pct_change().dropna()\n",
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" returns_data[coin] = rets\n",
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"\n",
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"stats = []\n",
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"for coin, rets in returns_data.items():\n",
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" stats.append({\n",
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" 'coin': coin,\n",
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" 'mean_annual': rets.mean() * 365 * 24,\n",
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" 'vol_annual': rets.std() * np.sqrt(365 * 24),\n",
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" 'sharpe': rets.mean() / rets.std() * np.sqrt(365 * 24) if rets.std() > 0 else 0,\n",
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" 'skew': rets.skew(),\n",
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" 'kurtosis': rets.kurtosis(),\n",
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" 'var_95': rets.quantile(0.05),\n",
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" 'cv': rets.std() / rets.mean() if rets.mean() != 0 else 0,\n",
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" 'max_dd': (df['close'] / df['close'].cummax() - 1).min(),\n",
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" })\n",
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"\n",
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"stats_df = pd.DataFrame(stats).set_index('coin')\n",
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"stats_df.round(4)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "79e3e1a7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Return distribution plots\n",
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"fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
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"for ax, (coin, rets) in zip(axes.flat, returns_data.items()):\n",
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" rets.hist(bins=100, ax=ax, alpha=0.7, density=True)\n",
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" ax.set_title(f\"{coin} — Skew: {rets.skew():.2f}, Kurt: {rets.kurtosis():.2f}\")\n",
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" ax.axvline(0, color='red', linestyle='--', alpha=0.5)\n",
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"plt.tight_layout()\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a68d3cb8",
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"metadata": {},
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"source": [
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"## 3. Correlation Matrix\n",
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"\n",
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"Identify redundant assets and diversification opportunities.\n",
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"High correlation = limited diversification benefit.\n",
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"Low correlation = potential for uncorrelated alpha streams."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ed22c624",
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"metadata": {},
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"outputs": [],
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"source": [
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"corr_matrix = pd.DataFrame(returns_data).corr()\n",
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"mask = np.triu(np.ones_like(corr_matrix), k=1)\n",
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"sns.heatmap(corr_matrix, mask=mask, annot=True, fmt='.3f', cmap='RdBu_r',\n",
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" center=0, vmin=-1, vmax=1, square=True)\n",
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"plt.title('Hourly Return Correlation Matrix')\n",
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"plt.tight_layout()\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6cb1d28b",
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"metadata": {},
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"source": [
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"## 4. Volatility Regimes\n",
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"\n",
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"Classify the market into volatility regimes. This drives strategy selection\n",
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"in the Regime-Switching Ensemble.\n",
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"\n",
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"- LOW_VOL: annualized < 15% → market making, pairs trading\n",
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"- NORMAL: 15-60% → all strategies at baseline\n",
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"- HIGH_VOL: > 60% → momentum, Hurst/VPIN, tight risk controls"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "71535eb6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from strategies.regime_ensemble import RegimeDetector\n",
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"\n",
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"detector = RegimeDetector(\n",
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" high_vol_threshold=0.60,\n",
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" low_vol_threshold=0.15,\n",
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" funding_extreme_apr=0.30,\n",
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")\n",
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"\n",
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"btc_prices = prices['BTC']['close']\n",
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"regimes = []\n",
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"for i, px in enumerate(btc_prices):\n",
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" detector.feed_price(px)\n",
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" if i >= 100:\n",
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" regimes.append(detector.primary_regime())\n",
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"\n",
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"# Count regime distribution\n",
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"regime_counts = pd.Series(regimes).value_counts()\n",
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"print(\"Regime Distribution:\")\n",
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"for regime, count in regime_counts.items():\n",
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" print(f\" {regime:20s}: {count:5d} bars ({count/len(regimes)*100:.1f}%)\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7d5645fe",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Regime timeline\n",
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"import matplotlib.dates as mdates\n",
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"\n",
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"fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 8), sharex=True)\n",
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"\n",
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"ax1.plot(btc_prices.index[-len(regimes):], btc_prices.values[-len(regimes):],\n",
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" linewidth=0.5, color='black')\n",
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"ax1.set_ylabel('BTC Price')\n",
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"ax1.set_title('BTC Price with Market Regimes')\n",
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"\n",
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"regime_colors = {\n",
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" 'NORMAL': 'gray', 'TRENDING': 'green', 'MEAN_REVERTING': 'blue',\n",
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" 'CHOPPY': 'orange', 'HIGH_VOL': 'red', 'LOW_VOL': 'lightblue',\n",
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" 'FUNDING_EXTREME': 'purple',\n",
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"}\n",
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"regime_numeric = pd.Series(\n",
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" [{v: i for i, v in enumerate(regime_colors)}.get(r, 0) for r in regimes],\n",
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" index=btc_prices.index[-len(regimes):]\n",
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")\n",
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"ax2.scatter(regime_numeric.index, regime_numeric.values, c=[regime_colors.get(r, 'gray') for r in regimes],\n",
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" s=1, alpha=0.6)\n",
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"ax2.set_yticks(range(len(regime_colors)))\n",
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"ax2.set_yticklabels(regime_colors.keys())\n",
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"ax2.set_ylabel('Regime')\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aa092524",
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"metadata": {},
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"source": [
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"## 5. Fee Impact Analysis\n",
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"\n",
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"Hyperliquid perp fee schedule. Calculate the minimum edge needed to overcome\n",
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"fees at each tier. This sets the floor for signal strength thresholds."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "565628e8",
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"metadata": {},
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"outputs": [],
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"source": [
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"from config.fee_tiers import PERPS_TIERS, SPOT_TIERS, STAKING_TIERS, get_perp_fees, compute_trade_fees\n",
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"\n",
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"print(\"=== Perp Fee Tiers ===\")\n",
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"print(f\"{'Tier':<20} {'Volume':>12} {'Taker':>8} {'Maker':>8}\")\n",
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"print(\"-\" * 50)\n",
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"for tier, info in PERPS_TIERS.items():\n",
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" print(f\"{info['name']:<20} ${info['min_volume']:>10,.0f} \"\n",
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" f\"{info['taker']*100:.3f}% {info['maker']*100:.3f}%\")\n",
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"\n",
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"print(f\"\\n=== Spot Fee Tiers ===\")\n",
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"for tier, info in SPOT_TIERS.items():\n",
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" print(f\"{info['name']:<20} ${info['min_volume']:>10,.0f} \"\n",
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" f\"{info['taker']*100:.3f}% {info['maker']*100:.3f}%\")\n",
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"\n",
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"# Break-even trade size by fee tier\n",
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"print(f\"\\n=== Minimum Profitable Trade (BTC round-trip, 1bps edge) ===\")\n",
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"for tier in range(7):\n",
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" fees = compute_trade_fees(\"BUY\", 0.001, 100000, 100000, vip_tier=tier)\n",
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" print(f\" Tier {tier}: {fees['effective_rate_pct']:.4f}% per side \"\n",
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" f\"→ ${fees['total_fee']:.4f} round-trip\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "49c93517",
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"metadata": {},
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"source": [
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"## 6. Key Takeaways\n",
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"\n",
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"1. **Asset universe**: BTC dominates volume; ETH, SOL, HYPE are the next most liquid. Use 3-6 assets for cross-sectional strategies.\n",
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"2. **Return distributions**: Crypto returns are leptokurtic (fat tails) — expect black swans. Size positions accordingly.\n",
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"3. **Correlations**: BTC/ETH correlation ~0.7. Most alts >0.5 correlated with BTC. True diversification is hard.\n",
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"4. **Regime frequency**: NORMAL dominates but HIGH_VOL regime provides the best trading opportunities.\n",
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"5. **Fee hurdle**: At Tier 0, a round-trip costs ~0.09%. This means a 1bps edge is enough for a single tick, but barely. We need 2-5bps edges minimum for consistent profitability. At higher tiers, the bar drops significantly.\n",
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"6. **DuckDB data**: Enables sub-second queries on tick-level data. Essential for Hurst/VPIN and microstructure strategies.\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.13.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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