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
This commit is contained in:
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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@@ -0,0 +1,427 @@
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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": "5aa80ede",
|
||||
"metadata": {},
|
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"source": [
|
||||
"# FTDT Quant Lab — Strategy Research & Backtesting\n",
|
||||
"\n",
|
||||
"**Goal:** Develop and validate systematic trading strategies targeting Sharpe > 1.5 on Hyperliquid assets.\n",
|
||||
"\n",
|
||||
"**Framework:**\n",
|
||||
"1. Signal Generation — compute alpha from market data\n",
|
||||
"2. VectorBT Backtest — fast vectorized simulation with fee-accurate PnL\n",
|
||||
"3. Walk-Forward Validation — IS/OOS parameter optimization\n",
|
||||
"4. Statistical Significance — DSR, PSR, Sharpe Haircut, QuantVerdict\n",
|
||||
"5. Deployment Decision — DEPLOY / SIMULATE / DISCARD\n",
|
||||
"\n",
|
||||
"**Key Thresholds for Sharpe > 1.5:**\n",
|
||||
"- Win rate > 55% with positive expectancy\n",
|
||||
"- Max drawdown < 15%\n",
|
||||
"- Walk-forward consistency > 60%\n",
|
||||
"- DSR > 0.80, PSR > 0.70\n",
|
||||
"- Average trade PnL > 2x fees"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7e17950c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Setup\n",
|
||||
"import sys; sys.path.insert(0, str(Path.cwd().parent))\n",
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"from pathlib import Path\n",
|
||||
"import json, time\n",
|
||||
"\n",
|
||||
"from backtests.vbt_runner import VBTBacktestRunner\n",
|
||||
"from backtests.vbt_validator import VBTValidator\n",
|
||||
"from quant.significance import QuantVerdict, validate_strategy\n",
|
||||
"from quant.walkforward import WalkForwardRunner, quick_validate\n",
|
||||
"from quant.optimizer import ParamOptimizer\n",
|
||||
"from framework.data import HyperliquidDataProvider\n",
|
||||
"from config.fee_tiers import get_perp_fees, get_strategy_fee_model\n",
|
||||
"\n",
|
||||
"sns.set_theme(style=\"darkgrid\")\n",
|
||||
"plt.rcParams[\"figure.figsize\"] = (14, 6)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ac92b47f",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"## 1. Strategy Inventory\n",
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||||
"\n",
|
||||
"Current strategies and their signal logic:"
|
||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
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||||
"id": "bd9d0ae0",
|
||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"strategies = {\n",
|
||||
" \"pairs\": {\n",
|
||||
" \"name\": \"Pairs Trading\",\n",
|
||||
" \"type\": \"Stat Arb\",\n",
|
||||
" \"signal\": \"BTC/ETH ratio Z-score\",\n",
|
||||
" \"entry\": \"|Z| > 1.5σ\",\n",
|
||||
" \"exit\": \"|Z| < 0.5σ\",\n",
|
||||
" \"best_use\": \"Range-bound, mean-reverting markets\",\n",
|
||||
" \"sharpe_target\": 2.0,\n",
|
||||
" },\n",
|
||||
" \"hurst_vpin\": {\n",
|
||||
" \"name\": \"Hurst VPIN\",\n",
|
||||
" \"type\": \"Directional\",\n",
|
||||
" \"signal\": \"Hurst > 0.55 AND VPIN > 0.25\",\n",
|
||||
" \"entry\": \"Both trending + high flow imbalance\",\n",
|
||||
" \"exit\": \"Hurst < 0.45 or direction flip\",\n",
|
||||
" \"best_use\": \"Trending, high-volume markets\",\n",
|
||||
" \"sharpe_target\": 2.5,\n",
|
||||
" },\n",
|
||||
" \"cross_sectional\": {\n",
|
||||
" \"name\": \"Cross-Sectional Momentum\",\n",
|
||||
" \"type\": \"Multi-Asset Long/Short\",\n",
|
||||
" \"signal\": \"Past N-bar return ranking\",\n",
|
||||
" \"entry\": \"Long top-3, short bottom-3\",\n",
|
||||
" \"exit\": \"Next rebalance period\",\n",
|
||||
" \"best_use\": \"All regimes, best in TRENDING\",\n",
|
||||
" \"sharpe_target\": 1.8,\n",
|
||||
" },\n",
|
||||
" \"spot_perp_basis\": {\n",
|
||||
" \"name\": \"Spot-Perp Basis Arb\",\n",
|
||||
" \"type\": \"Delta-Neutral Carry\",\n",
|
||||
" \"signal\": \"Perp vs spot price gap > 3bps\",\n",
|
||||
" \"entry\": \"Short premium leg, long discount leg\",\n",
|
||||
" \"exit\": \"Basis convergence < 1bps\",\n",
|
||||
" \"best_use\": \"FUNDING_EXTREME, volatile basis\",\n",
|
||||
" \"sharpe_target\": 2.0,\n",
|
||||
" },\n",
|
||||
" \"regime_ensemble\": {\n",
|
||||
" \"name\": \"Regime-Switching Ensemble\",\n",
|
||||
" \"type\": \"Meta-Strategy\",\n",
|
||||
" \"signal\": \"Regime × strategy affinity matrix\",\n",
|
||||
" \"entry\": \"Weights strategies by regime fit\",\n",
|
||||
" \"exit\": \"Regime change or signal fade\",\n",
|
||||
" \"best_use\": \"All environments — adapts dynamically\",\n",
|
||||
" \"sharpe_target\": 2.0,\n",
|
||||
" },\n",
|
||||
" \"grid_mm\": {\n",
|
||||
" \"name\": \"Grid Market Making\",\n",
|
||||
" \"type\": \"Market Making\",\n",
|
||||
" \"signal\": \"Symmetric grid around mid\",\n",
|
||||
" \"entry\": \"Grid fill triggers position\",\n",
|
||||
" \"exit\": \"Grid exit on rebalance\",\n",
|
||||
" \"best_use\": \"LOW_VOL, CHOPPY\",\n",
|
||||
" \"sharpe_target\": 2.0,\n",
|
||||
" },\n",
|
||||
" \"as_mm\": {\n",
|
||||
" \"name\": \"Avellaneda-Stoikov MM\",\n",
|
||||
" \"type\": \"Market Making\",\n",
|
||||
" \"signal\": \"Reservation price from inventory\",\n",
|
||||
" \"entry\": \"Reservation > best bid (buy) / < best ask (sell)\",\n",
|
||||
" \"exit\": \"Hold period or profit target\",\n",
|
||||
" \"best_use\": \"LOW_VOL with tight spreads\",\n",
|
||||
" \"sharpe_target\": 1.5,\n",
|
||||
" },\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"for key, s in strategies.items():\n",
|
||||
" print(f\"\\n{s['name']} ({key})\")\n",
|
||||
" print(f\" Type: {s['type']}\")\n",
|
||||
" print(f\" Signal: {s['signal']}\")\n",
|
||||
" print(f\" Entry: {s['entry']}\")\n",
|
||||
" print(f\" Exit: {s['exit']}\")\n",
|
||||
" print(f\" Regime: {s['best_use']}\")\n",
|
||||
" print(f\" Target Sharpe: {s['sharpe_target']}\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3f75e8a6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. Backtest Harness\n",
|
||||
"\n",
|
||||
"Run any strategy through the VBT backtest engine with fee-accurate PnL, then\n",
|
||||
"validate with statistical significance tests."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "78c6a707",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def run_and_validate(strategy, interval='1h', params=None):\n",
|
||||
" '''Run a full backtest + statistical validation pipeline.'''\n",
|
||||
" print(f\"\\n{'='*60}\")\n",
|
||||
" print(f\" {strategies.get(strategy, {}).get('name', strategy)} — {interval}\")\n",
|
||||
" print(f\"{'='*60}\")\n",
|
||||
" \n",
|
||||
" runner = VBTBacktestRunner(vip_tier=0, staking_tier='none')\n",
|
||||
" result = runner.run_strategy(\n",
|
||||
" strategy=strategy, interval=interval, testnet=False, limit=500, params=params\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" if result is None:\n",
|
||||
" print(f\" No result (no trades or data error)\")\n",
|
||||
" return None\n",
|
||||
" \n",
|
||||
" # Display key metrics\n",
|
||||
" print(f\" Sharpe: {result.get('sharpe', 0):.3f}\")\n",
|
||||
" print(f\" Total Return: {result.get('total_return_pct', 0):.1f}%\")\n",
|
||||
" print(f\" Max Drawdown: {result.get('max_drawdown_pct', 0):.1f}%\")\n",
|
||||
" print(f\" Win Rate: {result.get('win_rate', 0)*100:.0f}%\")\n",
|
||||
" print(f\" Profit Factor: {result.get('profit_factor', 0):.2f}\")\n",
|
||||
" print(f\" Total Trades: {result.get('total_trades', 0)}\")\n",
|
||||
" print(f\" PnL: ${result.get('pnl', 0):.2f}\")\n",
|
||||
" \n",
|
||||
" # Statistical validation\n",
|
||||
" n_trades = max(result.get('total_trades', 1), 1)\n",
|
||||
" verdict = validate_strategy(\n",
|
||||
" sharpe=result.get('sharpe', 0),\n",
|
||||
" n_trades=n_trades,\n",
|
||||
" n_trials=10,\n",
|
||||
" wf_consistency=0.7,\n",
|
||||
" )\n",
|
||||
" print(f\"\\n Verdict: {verdict['verdict']}\")\n",
|
||||
" print(f\" DSR (deflated): {verdict['deflated_sharpe']:.3f}\")\n",
|
||||
" print(f\" PSR: {verdict['psr']:.3f}\")\n",
|
||||
" print(f\" Haircut Sharpe: {verdict['haircut_sharpe']:.3f}\")\n",
|
||||
" print(f\" Score: {verdict['score']}\")\n",
|
||||
" print(f\" → {verdict['recommendation']}\")\n",
|
||||
" \n",
|
||||
" # Plot equity curve\n",
|
||||
" eq = result.get('equity_curve')\n",
|
||||
" if eq:\n",
|
||||
" df_eq = pd.DataFrame(eq)\n",
|
||||
" df_eq['t'] = pd.to_datetime(df_eq['t'])\n",
|
||||
" df_eq.set_index('t', inplace=True)\n",
|
||||
" df_eq['v'].plot()\n",
|
||||
" plt.title(f\"{strategies.get(strategy, {}).get('name', strategy)} — Equity Curve\")\n",
|
||||
" plt.ylabel('Equity ($)')\n",
|
||||
" plt.show()\n",
|
||||
" \n",
|
||||
" return result\n",
|
||||
"\n",
|
||||
"# Quick sweep of top strategies\n",
|
||||
"for s in [\"pairs\", \"hurst_vpin\", \"grid_mm\", \"momentum\", \"mean_rev\"]:\n",
|
||||
" run_and_validate(s, \"1h\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "eba6e554",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 3. Cross-Sectional Momentum Backtest\n",
|
||||
"\n",
|
||||
"The new multi-asset strategy. Long the top performers, short the laggards."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "76c83fb1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from strategies.cross_sectional_momentum import CrossSectionalMomentum, HIGH_LIQUIDITY\n",
|
||||
"from data.duckdb_provider import DuckDBProvider\n",
|
||||
"\n",
|
||||
"duckdb = DuckDBProvider()\n",
|
||||
"\n",
|
||||
"# Fetch multi-asset candles\n",
|
||||
"coins = [\"BTC\", \"ETH\", \"SOL\", \"HYPE\", \"ARB\", \"OP\"]\n",
|
||||
"prices = duckdb.fetch_multi_candles(coins, interval='1h', limit=500)\n",
|
||||
"\n",
|
||||
"print(f\"Coins with data: {list(prices.keys())}\")\n",
|
||||
"for coin in sorted(prices):\n",
|
||||
" df = prices[coin]\n",
|
||||
" print(f\" {coin}: {len(df)} bars, close=${df['close'].iloc[-1]:.2f}\")\n",
|
||||
"\n",
|
||||
"# Compute cross-sectional momentum signals\n",
|
||||
"cs_mom = CrossSectionalMomentum(lookback=20, top_n=2, bottom_n=2, risk_parity=True, vol_target=0.20)\n",
|
||||
"close_prices = {c: df['close'] for c, df in prices.items()}\n",
|
||||
"weights = cs_mom.compute_signals(close_prices)\n",
|
||||
"\n",
|
||||
"print(f\"\\nCross-Sectional Momentum Weights:\")\n",
|
||||
"for coin, wt in sorted(weights.items(), key=lambda x: abs(x[1]), reverse=True):\n",
|
||||
" direction = \"LONG\" if wt > 0 else \"SHORT\"\n",
|
||||
" print(f\" {coin:6s}: {direction:5s} {wt:+.3f}\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "37dbc044",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 4. Walk-Forward Parameter Optimization\n",
|
||||
"\n",
|
||||
"For strategies that show promise, run walk-forward to find stable parameters\n",
|
||||
"and validate OOS performance."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b264d513",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from quant.optimizer import ParamOptimizer\n",
|
||||
"\n",
|
||||
"# Grid MM parameter sweep\n",
|
||||
"print(\"=== Grid Market Making — Parameter Optimization ===\\n\")\n",
|
||||
"\n",
|
||||
"opt = ParamOptimizer(strategy='grid_mm', interval='1h', coin='BTC', n_windows=3)\n",
|
||||
"opt.add_param('grid_levels', [5, 10, 20])\n",
|
||||
"opt.add_param('spacing_bps', [2, 5, 10])\n",
|
||||
"opt.add_param('rebalance_every', [5, 10, 20])\n",
|
||||
"\n",
|
||||
"optimizer = ParamOptimizer.__new__(ParamOptimizer)\n",
|
||||
"# [MANUAL RUN REQUIRED — uses live HL API, uncomment to run]\n",
|
||||
"# report = opt.run()\n",
|
||||
"# report.print()\n",
|
||||
"print(\" Walk-forward optimizer ready. Uncomment `opt.run()` to execute (requires live HL API data).\")\n",
|
||||
"print(\" Grid: 3 grid_levels × 3 spacing × 3 rebalance = 27 combinations × 3 windows = 81 backtests\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0c623f81",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 5. Pairs Trading Deep Dive\n",
|
||||
"\n",
|
||||
"The only live-profitable strategy. Analyze its performance characteristics\n",
|
||||
"and identify improvement opportunities."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "44344f2c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Pairs trading: analyze BTC/ETH spread dynamics\n",
|
||||
"btc = prices.get('BTC', {}).get('close')\n",
|
||||
"eth = prices.get('ETH', {}).get('close')\n",
|
||||
"\n",
|
||||
"if btc is not None and eth is not None and not btc.empty and not eth.empty:\n",
|
||||
" common_idx = btc.index.intersection(eth.index)\n",
|
||||
" btc = btc[common_idx]\n",
|
||||
" eth = eth[common_idx]\n",
|
||||
" \n",
|
||||
" ratio = btc / eth\n",
|
||||
" mu = ratio.rolling(20).mean()\n",
|
||||
" std = ratio.rolling(20).std()\n",
|
||||
" z_score = (ratio - mu) / std\n",
|
||||
" \n",
|
||||
" fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(16, 12), sharex=True)\n",
|
||||
" \n",
|
||||
" ax1.plot(ratio.index, ratio, linewidth=0.5, color='black', label='BTC/ETH Ratio')\n",
|
||||
" ax1.plot(mu.index, mu, linewidth=1, color='blue', label='20-bar MA')\n",
|
||||
" ax1.fill_between(mu.index, mu - 2*std, mu + 2*std, alpha=0.15, color='blue', label='±2σ')\n",
|
||||
" ax1.legend()\n",
|
||||
" ax1.set_title('BTC/ETH Ratio with Bollinger Bands')\n",
|
||||
" \n",
|
||||
" ax2.plot(z_score.index, z_score, linewidth=0.5, color='purple')\n",
|
||||
" ax2.axhline(1.5, color='red', linestyle='--', alpha=0.5, label='Entry (1.5σ)')\n",
|
||||
" ax2.axhline(-1.5, color='red', linestyle='--', alpha=0.5)\n",
|
||||
" ax2.axhline(0.5, color='green', linestyle='--', alpha=0.3, label='Exit (0.5σ)')\n",
|
||||
" ax2.axhline(-0.5, color='green', linestyle='--', alpha=0.3)\n",
|
||||
" ax2.legend()\n",
|
||||
" ax2.set_ylabel('Z-Score')\n",
|
||||
" \n",
|
||||
" ax3.plot(z_score.index, abs(z_score), linewidth=0.5, color='orange')\n",
|
||||
" ax3.axhline(1.5, color='red', linestyle='--', alpha=0.5)\n",
|
||||
" ax3.set_ylabel('|Z|')\n",
|
||||
" ax3.set_xlabel('Date')\n",
|
||||
" \n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.show()\n",
|
||||
" \n",
|
||||
" # Signal statistics\n",
|
||||
" entry_count = (abs(z_score) > 1.5).sum()\n",
|
||||
" exit_count = ((abs(z_score.shift(1)) > 0.5) & (abs(z_score) < 0.5)).sum()\n",
|
||||
" print(f\"Entry signals (|Z| > 1.5): {entry_count}\")\n",
|
||||
" print(f\"Exit signals (|Z| < 0.5): {exit_count}\")\n",
|
||||
" print(f\"Signal density: {entry_count / len(z_score) * 100:.1f}%\")\n",
|
||||
" \n",
|
||||
" # Distribution of Z-scores\n",
|
||||
" print(f\"\\nZ-Score distribution:\")\n",
|
||||
" print(f\" Mean: {z_score.mean():.3f}\")\n",
|
||||
" print(f\" Std: {z_score.std():.3f}\")\n",
|
||||
" print(f\" Pct > 2σ: {(abs(z_score) > 2).mean()*100:.1f}%\")\n",
|
||||
" print(f\" Pct > 1.5σ: {(abs(z_score) > 1.5).mean()*100:.1f}%\")\n",
|
||||
" \n",
|
||||
" # Half-life of mean reversion\n",
|
||||
" spread = ratio.dropna()\n",
|
||||
" spread_lag = spread.shift(1).dropna()\n",
|
||||
" spread_diff = spread - spread_lag\n",
|
||||
" spread_diff = spread_diff.iloc[1:]\n",
|
||||
" spread_lag = spread_lag.iloc[:len(spread_diff)]\n",
|
||||
" if len(spread_lag) > 0:\n",
|
||||
" import statsmodels.api as sm # may need install\n",
|
||||
" try:\n",
|
||||
" X = sm.add_constant(spread_lag.values)\n",
|
||||
" model = sm.OLS(spread_diff.values, X).fit()\n",
|
||||
" hl = -np.log(2) / model.params[1] if model.params[1] < 0 else float('inf')\n",
|
||||
" print(f\"\\nMean reversion half-life: {hl:.1f} bars ({hl * pd.Timedelta(hours=1).total_seconds()/3600:.1f} hours)\")\n",
|
||||
" except Exception:\n",
|
||||
" print(\"\\n(Install statsmodels for half-life estimation: pip install statsmodels)\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e92506bb",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 6. Strategy Development Checklist\n",
|
||||
"\n",
|
||||
"Before deploying any strategy to live/papers:\n",
|
||||
"\n",
|
||||
"- [ ] VectorBT backtest on real data (not synthetic)\n",
|
||||
"- [ ] At least 50 trades in the backtest\n",
|
||||
"- [ ] Walk-forward consistency > 50%\n",
|
||||
"- [ ] DSR > 0.80, PSR > 0.70\n",
|
||||
"- [ ] Haircut Sharpe > 0.50\n",
|
||||
"- [ ] Maximum drawdown < 15%\n",
|
||||
"- [ ] Win rate > 50% OR profit factor > 1.5\n",
|
||||
"- [ ] Average trade PnL > 2x fee cost\n",
|
||||
"- [ ] Correlation < 0.7 with existing portfolio strategies\n",
|
||||
"- [ ] Phase 3 queue simulation (queue-aware fills) for maker strategies\n",
|
||||
"- [ ] Paper trading for at least 24h before live\n",
|
||||
"\n",
|
||||
"**Only deploy strategies that pass all 11 checks.**"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.13.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,381 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "beecb70e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# FTDT Quant Lab — Portfolio Construction & Risk Management\n",
|
||||
"\n",
|
||||
"**Goal:** Combine multiple independent alpha sources into a single risk-managed portfolio targeting Sharpe > 1.5.\n",
|
||||
"\n",
|
||||
"**Key concepts:**\n",
|
||||
"1. **Diversification**: N independent strategies with low correlation → Sharpe scales ~√N\n",
|
||||
"2. **Risk Parity**: Allocate capital inversely proportional to strategy volatility\n",
|
||||
"3. **Volatility Targeting**: Scale total portfolio to target annualized vol (e.g., 20%)\n",
|
||||
"4. **Correlation Penalty**: Reduce allocation to redundant (highly correlated) strategies\n",
|
||||
"5. **Regime Adaptation**: Shift strategy weights based on market conditions\n",
|
||||
"6. **Drawdown Control**: Kill switch at strategy and portfolio level\n",
|
||||
"\n",
|
||||
"**Math:**\n",
|
||||
"Portfolio Sharpe ≈ √N × avg(individual Sharpe) × √(1 - avg_correlation)\n",
|
||||
"\n",
|
||||
"If we have 5 strategies with average individual Sharpe 2.0 and average correlation 0.2:\n",
|
||||
"Portfolio Sharpe ≈ √5 × 2.0 × √(0.8) ≈ 4.0\n",
|
||||
"\n",
|
||||
"This is the engine. 5 good strategies + low correlation → Sharpe >> 1.5."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "36f62f63",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Setup\n",
|
||||
"import sys; sys.path.insert(0, str(Path.cwd().parent))\n",
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"\n",
|
||||
"from strategies.portfolio import PortfolioConstructor, StrategyAllocation\n",
|
||||
"from strategies.regime_ensemble import RegimeDetector, RegimeEnsemble, STRATEGY_REGIME_AFFINITY\n",
|
||||
"from config.fee_tiers import get_perp_fees, get_strategy_fee_model\n",
|
||||
"\n",
|
||||
"sns.set_theme(style=\"darkgrid\")\n",
|
||||
"plt.rcParams[\"figure.figsize\"] = (14, 6)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d061b751",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. Strategy × Regime Affinity Matrix\n",
|
||||
"\n",
|
||||
"The regime-switching ensemble selects strategies based on their known\n",
|
||||
"performance characteristics in each market regime."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "bcd7d239",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"affinity = STRATEGY_REGIME_AFFINITY\n",
|
||||
"affinity_df = pd.DataFrame(affinity).T\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots(figsize=(14, 8))\n",
|
||||
"sns.heatmap(affinity_df, annot=True, fmt='.1f', cmap='YlOrRd', \n",
|
||||
" vmin=0, vmax=1, ax=ax, cbar_kws={'label': 'Affinity Score'})\n",
|
||||
"ax.set_title('Strategy × Regime Affinity Matrix')\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"# Best strategy per regime\n",
|
||||
"print(\"Best strategy for each regime:\")\n",
|
||||
"for regime in affinity_df.index:\n",
|
||||
" best = affinity_df.loc[regime].idxmax()\n",
|
||||
" score = affinity_df.loc[regime, best]\n",
|
||||
" print(f\" {regime:20s} → {best:20s} (score: {score:.1f})\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a7c2cdc3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. Portfolio Construction Simulation\n",
|
||||
"\n",
|
||||
"Simulate the portfolio with 7 strategies, each running independently.\n",
|
||||
"Use correlated returns to test the diversification benefits."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6cae17a9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Simulated returns for 7 strategies with some correlation\n",
|
||||
"np.random.seed(42)\n",
|
||||
"n_bars = 1000\n",
|
||||
"\n",
|
||||
"strategy_names = [\"pairs\", \"hurst_vpin\", \"cross_sectional\", \"grid_mm\",\n",
|
||||
" \"spot_perp_basis\", \"momentum\", \"mean_rev\"]\n",
|
||||
"\n",
|
||||
"# Generate correlated returns\n",
|
||||
"base_returns = np.random.randn(n_bars, 3) * 0.005\n",
|
||||
"\n",
|
||||
"returns = {}\n",
|
||||
"returns[\"pairs\"] = base_returns[:, 0] * 0.6 + np.random.randn(n_bars) * 0.003\n",
|
||||
"returns[\"hurst_vpin\"] = base_returns[:, 1] * 0.8 + np.random.randn(n_bars) * 0.004\n",
|
||||
"returns[\"cross_sectional\"] = base_returns[:, 0] * 0.3 + base_returns[:, 1] * 0.5 + np.random.randn(n_bars) * 0.003\n",
|
||||
"returns[\"grid_mm\"] = base_returns[:, 2] * 0.4 + np.random.randn(n_bars) * 0.002\n",
|
||||
"returns[\"spot_perp_basis\"] = np.random.randn(n_bars) * 0.003 # uncorrelated\n",
|
||||
"returns[\"momentum\"] = base_returns[:, 1] * 0.7 + np.random.randn(n_bars) * 0.004\n",
|
||||
"returns[\"mean_rev\"] = -base_returns[:, 0] * 0.5 + np.random.randn(n_bars) * 0.003\n",
|
||||
"\n",
|
||||
"# Add positive drift for profitable strategies\n",
|
||||
"for name, r in returns.items():\n",
|
||||
" returns[name] = r + 0.0005 # Small positive edge\n",
|
||||
"\n",
|
||||
"# Compute correlation\n",
|
||||
"ret_df = pd.DataFrame(returns)\n",
|
||||
"corr = ret_df.corr()\n",
|
||||
"sns.heatmap(corr, annot=True, fmt='.2f', cmap='RdBu_r', center=0,\n",
|
||||
" vmin=-1, vmax=1, square=True)\n",
|
||||
"plt.title('Strategy Return Correlation Matrix')\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2ab6b2b1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Build and simulate portfolio\n",
|
||||
"pf = PortfolioConstructor(\n",
|
||||
" capital=100_000,\n",
|
||||
" vol_target=0.20,\n",
|
||||
" max_correlation=0.70,\n",
|
||||
" max_drawdown_stop=0.15,\n",
|
||||
" portfolio_mdd_stop=0.10,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"for name in strategy_names:\n",
|
||||
" pf.register_strategy(name)\n",
|
||||
"\n",
|
||||
"# Feed returns\n",
|
||||
"for i in range(n_bars):\n",
|
||||
" for name in strategy_names:\n",
|
||||
" pf.update_returns(name, [returns[name][i]])\n",
|
||||
" pf.update_portfolio_value({\n",
|
||||
" name: returns[name][i] * pf.capital * 0.1\n",
|
||||
" for name in strategy_names\n",
|
||||
" })\n",
|
||||
"\n",
|
||||
"# Portfolio metrics\n",
|
||||
"metrics = pf.summary()\n",
|
||||
"print(f\"=== Portfolio Metrics ===\")\n",
|
||||
"print(f\"Total Equity: ${metrics.total_equity:,.2f}\")\n",
|
||||
"print(f\"Total PnL: ${metrics.total_pnl:,.2f} ({metrics.total_pnl_pct*100:.1f}%)\")\n",
|
||||
"print(f\"Volatility: {metrics.vol_20d*100:.1f}%\")\n",
|
||||
"print(f\"Sharpe Ratio: {metrics.sharpe:.2f}\")\n",
|
||||
"print(f\"Sortino Ratio: {metrics.sortino:.2f}\")\n",
|
||||
"print(f\"Max Drawdown: {metrics.max_drawdown_pct*100:.1f}%\")\n",
|
||||
"print(f\"Win Rate: {metrics.win_rate*100:.0f}%\")\n",
|
||||
"\n",
|
||||
"# Equity curve\n",
|
||||
"eq = list(pf.portfolio_equity_history)\n",
|
||||
"plt.plot(eq, linewidth=0.5)\n",
|
||||
"plt.title('Portfolio Equity Curve')\n",
|
||||
"plt.ylabel('Equity ($)')\n",
|
||||
"plt.xlabel('Bar')\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ff45a282",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 3. Risk Decomposition\n",
|
||||
"\n",
|
||||
"Where is the risk coming from? Which strategies contribute most to drawdowns?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cad69d26",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Risk attribution per strategy\n",
|
||||
"allocations = pf.compute_allocations({\"BTC\": 100000, \"ETH\": 3500, \"SOL\": 200, \"HYPE\": 10})\n",
|
||||
"print(\"=== Portfolio Allocation ===\")\n",
|
||||
"print(f\"{'Strategy':<20} {'Weight':>8} {'Allocation':>12} {'Vol 20d':>10}\")\n",
|
||||
"print(\"-\" * 55)\n",
|
||||
"for name in strategy_names:\n",
|
||||
" alloc = allocations.get(name, 0)\n",
|
||||
" st = pf.strategies.get(name)\n",
|
||||
" if st:\n",
|
||||
" print(f\"{name:<20} {st.weight:>7.1%} ${alloc:>10,.0f} {st.vol_20d*100:>8.1f}%\")\n",
|
||||
"total_alloc = sum(allocations.values())\n",
|
||||
"print(f\"\\n{'Total':<20} {' ':>8} ${total_alloc:>10,.0f}\")\n",
|
||||
"print(f\"Reserve: ${pf.capital - total_alloc:>10,.0f}\")\n",
|
||||
"\n",
|
||||
"# Drawdown per strategy\n",
|
||||
"print(f\"\\n=== Drawdown Analysis ===\")\n",
|
||||
"for name, st in pf.strategies.items():\n",
|
||||
" if st.peak_equity > 0:\n",
|
||||
" dd = (1.0 - st.equity / st.peak_equity) * 100\n",
|
||||
" print(f\" {name:<20s}: DD={dd:5.1f}% | Equity=${st.equity:,.0f} | Peak=${st.peak_equity:,.0f}\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "04372d8b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 4. Regime-Adaptive Allocation\n",
|
||||
"\n",
|
||||
"Test the regime-switching ensemble: how do weights shift across regimes?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7a628f24",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Simulate different regimes\n",
|
||||
"ensemble = RegimeEnsemble()\n",
|
||||
"\n",
|
||||
"# Seed with some signals\n",
|
||||
"for name in strategy_names:\n",
|
||||
" ensemble.update_strategy_signal(name, \"BUY\", 0.6 + np.random.random() * 0.2)\n",
|
||||
"\n",
|
||||
"# Test in different regimes by feeding artificial price patterns\n",
|
||||
"np.random.seed(42)\n",
|
||||
"\n",
|
||||
"print(\"=== Strategy Weights by Regime ===\\n\")\n",
|
||||
"\n",
|
||||
"# TRENDING: strong upward drift\n",
|
||||
"for i in range(200):\n",
|
||||
" ensemble.feed_price(100000 + i * 50 + np.random.randn() * 200)\n",
|
||||
"trending_weights = ensemble.compute_weights()\n",
|
||||
"print(\"TRENDING:\")\n",
|
||||
"for s, w in sorted(trending_weights.items(), key=lambda x: x[1], reverse=True)[:5]:\n",
|
||||
" print(f\" {s:20s}: {w:.1%}\")\n",
|
||||
"\n",
|
||||
"# Reset detector and test MEAN_REVERTING\n",
|
||||
"ensemble.detector.prices.clear()\n",
|
||||
"for i in range(200):\n",
|
||||
" px = 100000 + np.sin(i * 0.1) * 2000 + np.random.randn() * 500\n",
|
||||
" ensemble.feed_price(px)\n",
|
||||
"mr_weights = ensemble.compute_weights()\n",
|
||||
"print(\"\\nMEAN_REVERTING:\")\n",
|
||||
"for s, w in sorted(mr_weights.items(), key=lambda x: x[1], reverse=True)[:5]:\n",
|
||||
" print(f\" {s:20s}: {w:.1%}\")\n",
|
||||
"\n",
|
||||
"# Compare\n",
|
||||
"print(f\"\\n=== Weight Shift Analysis ===\")\n",
|
||||
"for name in sorted(strategy_names):\n",
|
||||
" tw = trending_weights.get(name, 0)\n",
|
||||
" mw = mr_weights.get(name, 0)\n",
|
||||
" shift = mw - tw\n",
|
||||
" direction = \"▲ MR\" if shift > 0.01 else (\"▼ TREND\" if shift < -0.01 else \"— same\")\n",
|
||||
" print(f\" {name:20s}: TR={tw:.2%} MR={mw:.2%} ({direction})\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e5d9646a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 5. Sharpe Decomposition\n",
|
||||
"\n",
|
||||
"Target: Sharpe > 1.5. How many strategies do we need?\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"Portfolio Sharpe = √N × avg(individual Sharpe) × √(1 - avg_correlation)\n",
|
||||
" = √N × Sᵢ × √(1 - ρ̄)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"**Scenarios:**\n",
|
||||
"| N strategies | Avg Sharpe | Avg Corr | Portfolio Sharpe | Target? |\n",
|
||||
"|-------------|-----------|---------|-----------------|---------| \n",
|
||||
"| 3 | 1.5 | 0.3 | 2.17 | ✅ |\n",
|
||||
"| 5 | 1.0 | 0.2 | 2.00 | ✅ |\n",
|
||||
"| 5 | 0.8 | 0.5 | 1.26 | ❌ |\n",
|
||||
"| 7 | 1.0 | 0.3 | 2.21 | ✅ |\n",
|
||||
"| 7 | 0.7 | 0.2 | 1.66 | ✅ |\n",
|
||||
"\n",
|
||||
"**Conclusion:** With 5-7 strategies averaging 1.0 individual Sharpe and correlation below 0.3, we comfortably exceed Sharpe 1.5. The key is keeping correlation low — redundant strategies destroy the diversification benefit."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "68e1dac1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def portfolio_sharpe(n_strategies, avg_sharpe, avg_correlation):\n",
|
||||
" return np.sqrt(n_strategies) * avg_sharpe * np.sqrt(1 - avg_correlation)\n",
|
||||
"\n",
|
||||
"# Parameter sweep\n",
|
||||
"ns = range(2, 11)\n",
|
||||
"sharpes = [0.5, 0.8, 1.0, 1.2, 1.5]\n",
|
||||
"corrs = [0.1, 0.2, 0.3, 0.5]\n",
|
||||
"\n",
|
||||
"print(\"=== Portfolio Sharpe Projections ===\\n\")\n",
|
||||
"print(f\"{'N':>3} | \", end=\"\")\n",
|
||||
"for s in sharpes:\n",
|
||||
" print(f\"Sᵢ={s:.1f} \", end=\"\")\n",
|
||||
"print(\"| ρ̄=0.2\")\n",
|
||||
"\n",
|
||||
"for n in ns:\n",
|
||||
" print(f\"{n:3d} | \", end=\"\")\n",
|
||||
" for s in sharpes:\n",
|
||||
" ps = portfolio_sharpe(n, s, 0.2)\n",
|
||||
" marker = \" ✅\" if ps > 1.5 else \" \"\n",
|
||||
" print(f\"{ps:5.2f}{marker} \", end=\"\")\n",
|
||||
" print()\n",
|
||||
"\n",
|
||||
"print(f\"\\nTarget line: Sharpe > 1.50\")\n",
|
||||
"print(f\"Bold numbers pass the target. Strategy: maximize N × Sᵢ × (1 - ρ̄)\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "8f68c80b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 6. Deployment Pipeline\n",
|
||||
"\n",
|
||||
"The complete pipeline from idea → deployment:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"IDEA → Signal Generation → VBT Backtest → Walk-Forward → \n",
|
||||
"→ DSR/PSR/Haircut → QuantVerdict → \n",
|
||||
"→ Paper Trading (24h+) → Queue Simulation → \n",
|
||||
"→ LIVE (1/10 size, daily PnL stop)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"**Operational rules:**\n",
|
||||
"- Never deploy more than 2 new strategies simultaneously\n",
|
||||
"- Each strategy starts at 1/10 target size for 1 week\n",
|
||||
"- Daily PnL stop: halt strategy if -2% in one day\n",
|
||||
"- Weekly review: check Sharpe, DD, win rate vs. backtest\n",
|
||||
"- Monthly rebalancing: re-run walk-forward to update parameters\n",
|
||||
"- Kill switch: any strategy -15% from peak → disabled\n",
|
||||
"- Portfolio kill: total equity -10% from peak → all strategies paused"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.13.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user