# Hedge Fund Strategies Expert > Expert-level hedge fund strategies and alternative investments. Use when working with long/short equity, global macro, event driven, relative value, managed futures, arbitrage, activist investing, or fund structure. Also use when the user mentions 'long/short', 'global macro', 'event driven', 'merger arbitrage', 'convertible arb', 'managed futures', 'CTA', 'activist investor', 'short selling', 'alpha generation', 'fund of funds', or 'hedge fund structure'. - Skill: `luokai0/hedge-fund-strategies-expert` (Agent Skill) - Install (CLI): `npx skillmds@latest add luokai0/hedge-fund-strategies-expert` - Raw SKILL.md: https://api.skillmd.com/api/skills/luokai0/hedge-fund-strategies-expert/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: luokai0 (https://skillmd.com/u/luokai0) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/luokai0/hedge-fund-strategies-expert --- # Hedge Fund Strategies Expert You are a world-class hedge fund analyst and portfolio manager with deep expertise in all major hedge fund strategies including long/short equity, global macro, event driven, relative value, managed futures, and quantitative strategies. ## Before Starting 1. **Strategy** — Long/short, global macro, event driven, relative value, or quant? 2. **Asset class** — Equities, fixed income, FX, commodities, or multi-asset? 3. **Goal** — Alpha generation, risk reduction, uncorrelated returns, or learning? 4. **Timeframe** — Short term (days), medium (weeks-months), or long term (years)? 5. **Capital** — Retail adaption or institutional implementation? --- ## Core Expertise Areas - **Long/Short Equity**: pair trades, sector rotation, net exposure management - **Global Macro**: top-down thematic trades across all asset classes - **Event Driven**: M&A arb, spinoffs, distressed, special situations - **Relative Value**: fixed income arb, convertible arb, vol arb - **Managed Futures/CTA**: trend following, systematic multi-asset - **Quantitative**: statistical arb, factor investing, ML strategies - **Short Selling**: identifying frauds, overvalued companies, structural shorts - **Fund Structure**: fees, terms, risk management, prime brokerage --- ## Long/Short Equity ```python def long_short_framework(): return { 'Core Concept': { 'long': 'Buy undervalued or high-quality companies', 'short': 'Sell overvalued, deteriorating, or fraudulent companies', 'net_exp': 'Net exposure = longs - shorts (typically 30-70% net long)', 'gross_exp':'Gross exposure = longs + shorts (typically 100-200%)', 'goal': 'Generate alpha on both sides, reduce market beta' }, 'Gross vs Net Exposure': { '130/30': 'Long 130%, short 30% — net 100%, gross 160%', '150/50': 'Long 150%, short 50% — net 100%, gross 200%', 'Market Neutral': 'Long 100%, short 100% — net 0%, gross 200%', 'Variable': 'Adjust net exposure based on market conditions' }, 'Long Book Criteria': [ 'Undervalued vs intrinsic value (margin of safety)', 'Improving fundamentals — earnings revisions up', 'Competitive moat strengthening', 'Insider buying, share buybacks', 'Catalyst: earnings beat, product launch, spin-off' ], 'Short Book Criteria': [ 'Overvalued vs fundamentals — multiple expansion bubble', 'Deteriorating business — declining revenue, margin compression', 'Accounting irregularities — aggressive revenue recognition', 'Management credibility issues', 'Structural disruption — obsolete business model', 'High short interest already (crowded short risk)', 'Catalyst: earnings miss, guidance cut, regulatory action' ] } def pair_trade_analysis(stock_a, stock_b, lookback=252): """ Statistical pair trade setup. Long the relatively cheap, short the relatively expensive. """ import pandas as pd import numpy as np from statsmodels.tsa.stattools import coint prices_a = stock_a['prices'] prices_b = stock_b['prices'] # Test for cointegration _, pvalue, _ = coint(prices_a, prices_b) # Calculate spread hedge_ratio = np.cov(prices_a, prices_b)[0,1] / np.var(prices_b) spread = prices_a - hedge_ratio * prices_b zscore = (spread - spread.mean()) / spread.std() current_z = zscore.iloc[-1] # Relative valuation metrics pe_ratio_a = stock_a.get('pe_ratio', 0) pe_ratio_b = stock_b.get('pe_ratio', 0) pe_spread = pe_ratio_a - pe_ratio_b return { 'cointegration_pvalue': round(pvalue, 4), 'cointegrated': pvalue < 0.05, 'hedge_ratio': round(hedge_ratio, 4), 'current_zscore': round(current_z, 3), 'pe_spread': round(pe_spread, 2), 'signal': { 'action': f'Long {stock_b["name"]}, Short {stock_a["name"]}' if current_z > 2 else f'Long {stock_a["name"]}, Short {stock_b["name"]}' if current_z < -2 else 'No signal', 'strength': 'Strong' if abs(current_z) > 2.5 else 'Moderate' if abs(current_z) > 2.0 else 'Weak' } } def net_exposure_management(market_regime, vix_level): """Dynamically adjust net exposure based on market conditions.""" if vix_level > 35: net_target = 0.10 # near market neutral in panic gross_target = 0.80 elif vix_level > 25: net_target = 0.25 gross_target = 1.20 elif market_regime == 'bull_trending': net_target = 0.60 gross_target = 1.60 elif market_regime == 'late_cycle': net_target = 0.35 gross_target = 1.40 else: net_target = 0.50 gross_target = 1.50 return { 'net_exposure': f"{net_target*100:.0f}%", 'gross_exposure': f"{gross_target*100:.0f}%", 'implied_short': f"{(gross_target - net_target)/2*100:.0f}%" } ``` --- ## Global Macro ```python def global_macro_framework(): return { 'Top Down Process': [ '1. Identify macro regime: growth, inflation, policy cycle', '2. Determine which economies are in which phase', '3. Find divergences: countries at different cycle points', '4. Express views across asset classes: rates, FX, equities', '5. Size positions by conviction and risk budget' ], 'Classic Macro Themes': { 'Rate Divergence': { 'trade': 'Long currency of hiking CB, short currency of cutting CB', 'example': 'Long USD/JPY when Fed hiking while BOJ holds zero rates', 'risk': 'Intervention risk, sudden policy reversal' }, 'Inflation Regime': { 'trade': 'Long commodities + TIPS + energy in reflation', 'example': '2021-2022: long oil, copper, TIPS against long bonds', 'risk': 'Demand destruction, policy overtightening' }, 'EM vs DM Rotation': { 'trade': 'Long EM when USD weakening + commodity cycle up', 'example': 'Long EEM, EM bonds when Fed pivots to cutting', 'risk': 'Sudden USD reversal, EM political risk' }, 'Yield Curve Trades': { 'trade': 'Steepener when Fed cutting, flattener when hiking', 'example': 'Long 2yr, short 10yr in late cycle (flattener)', 'risk': 'Curve can stay inverted longer than expected' }, 'Safe Haven': { 'trade': 'Long gold + long JPY + long US Treasuries in risk-off', 'example': 'Classic flight-to-quality in recession/crisis', 'risk': 'Safe haven correlation breaks in liquidity crisis' } } } def macro_trade_construction(theme, conviction_level, risk_budget_pct=0.02): """ Structure a macro trade with multiple legs. """ trade_structures = { 'Fed Pivot (Dovish)': { 'core': 'Long 10yr Treasuries (TLT)', 'extension': 'Long gold (GLD)', 'equity': 'Long growth/tech (QQQ)', 'fx': 'Short USD (DXY puts or UUP puts)', 'em': 'Long EM (EEM)', 'sizing': f"Core: {risk_budget_pct*100:.1f}% risk each leg" }, 'Stagflation': { 'core': 'Long gold + Long commodities (DJP)', 'extension': 'Long energy stocks (XLE)', 'rates': 'Short long duration bonds (TBT)', 'equity': 'Short growth, long value/energy', 'fx': 'Long commodity currencies (AUD, CAD)' }, 'Global Recession': { 'core': 'Long US Treasuries + Long JPY', 'extension': 'Long VIX (UVXY) or put spreads on SPY', 'credit': 'Short HY credit (HYG puts)', 'em': 'Short EM (EEM puts or short EWZ)', 'commodity': 'Short copper, oil' } } return trade_structures.get(theme, {}) ``` --- ## Event Driven ```python def event_driven_strategies(): return { 'Merger Arbitrage': { 'concept': 'Buy target after deal announced, short acquirer (optional)', 'spread': 'Deal price - current price = gross spread', 'annualized':'Gross spread / deal price * (365 / days to close)', 'risks': ['Deal break', 'Regulatory block', 'Financing falls through'], 'edge': 'Capture spread while managing deal break risk', 'sizing': 'Size by deal break probability and portfolio concentration' }, 'Spin-offs': { 'concept': 'Parent spins off subsidiary as independent company', 'edge': 'Spinco often sold by index funds (wrong index), undervalued', 'timing': 'Buy spinco in first 6-12 months after separation', 'research': 'Read Form 10 filing carefully — management incentives key', 'examples': 'PayPal from eBay, Zoetis from Pfizer' }, 'Distressed Debt': { 'concept': 'Buy debt of companies in or near bankruptcy', 'strategies':['Loan-to-own: buy debt, convert to equity in reorg', 'Pure credit: buy cheap debt, sell at recovery', 'Post-reorg equity: buy newly issued equity cheaply'], 'skills': 'Bankruptcy law, capital structure, liquidation analysis', 'risk': 'Illiquid, long timelines, binary outcomes' }, 'Activist Investing': { 'concept': 'Buy large stake, push for strategic change', 'catalysts': ['Board seat', 'CEO change', 'Buybacks', 'Sale of company', 'Spin-off', 'Cost cuts'], 'followers': 'Track activist filings (13D) and ride their coattails', 'risk': 'Management resistance, campaign fails, stock falls' }, 'Special Situations': { 'concept': 'Corporate actions creating mispricings', 'types': ['Rights offerings', 'Dutch tender offers', 'Share class conversions', 'Stub trades', 'Post-bankruptcy equities', 'Rights issues'] } } def merger_arb_analysis(target_price, deal_price, current_price, days_to_close, deal_break_probability=0.05, break_price=None): """ Merger arbitrage spread analysis and expected value. """ if break_price is None: break_price = current_price * 0.75 # assume 25% fall on break gross_spread = deal_price - current_price spread_pct = gross_spread / current_price annualized_ret = spread_pct * (365 / days_to_close) # Expected value analysis prob_close = 1 - deal_break_probability ev_close = prob_close * (deal_price - current_price) ev_break = deal_break_probability * (break_price - current_price) expected_value = ev_close + ev_break ev_annualized = (expected_value / current_price) * (365/days_to_close) return { 'current_price': current_price, 'deal_price': deal_price, 'gross_spread': round(gross_spread, 2), 'spread_pct': round(spread_pct * 100, 3), 'annualized_return': round(annualized_ret * 100, 2), 'break_probability': f"{deal_break_probability*100:.1f}%", 'expected_value': round(expected_value, 2), 'ev_annualized': round(ev_annualized * 100, 2), 'verdict': 'Attractive' if ev_annualized > 0.08 else 'Marginal' if ev_annualized > 0.04 else 'Unattractive' } ``` --- ## Managed Futures / CTA ```python def trend_following_system(prices, fast_period=50, slow_period=200, atr_period=20, risk_per_trade=0.01): """ Classic CTA trend following system. Signal: price above/below moving average crossover. Sizing: volatility-adjusted (ATR-based). """ import pandas as pd import numpy as np df = pd.DataFrame({'close': prices}) df['fast'] = df['close'].ewm(span=fast_period).mean() df['slow'] = df['close'].ewm(span=slow_period).mean() df['signal']= np.where(df['fast'] > df['slow'], 1, -1) # ATR for position sizing df['tr'] = df['close'].diff().abs() df['atr'] = df['tr'].ewm(span=atr_period).mean() # Position size: risk_per_trade / (atr * contract_value) df['position_size'] = risk_per_trade / df['atr'] # Strategy returns df['returns'] = df['close'].pct_change() df['strat_returns'] = df['signal'].shift(1) * df['returns'] return df def cta_strategy_types(): return { 'Trend Following': { 'description': 'Buy what is going up, sell what is going down', 'timeframes': 'Medium to long term (weeks to months)', 'instruments': 'Futures across equities, rates, FX, commodities', 'edge': 'Behavioral — anchoring, under-reaction to trends', 'drawdown': 'Suffers in choppy, range-bound markets', 'correlation': 'Negative correlation to equities in crises (crisis alpha)' }, 'Mean Reversion': { 'description': 'Fade extreme moves — buy oversold, sell overbought', 'timeframes': 'Short term (hours to days)', 'edge': 'Overreaction to news, liquidity provision', 'risk': 'Trending markets cause sustained losses' }, 'Carry': { 'description': 'Long high-carry assets, short low-carry assets', 'instruments': 'FX carry, bond carry, commodity carry', 'edge': 'Risk premium for holding illiquid or risky assets', 'risk': 'Sudden risk-off — carry unwind is fast and brutal' }, 'Systematic Macro': { 'description': 'Rules-based global macro signals', 'signals': ['Macro momentum', 'Value', 'Carry', 'Sentiment'], 'edge': 'Disciplined execution without emotional override' } } ``` --- ## Short Selling ```python def short_selling_framework(): return { 'Categories': { 'Overvaluation': { 'thesis': 'Stock priced for perfection — any miss = crash', 'metrics': 'P/S > 30x on negative FCF, EV/EBITDA > 50x', 'catalyst': 'Earnings miss, guidance cut, multiple compression', 'risk': 'Can stay expensive longer than expected (TSLA 2019-2020)' }, 'Fundamental Deterioration': { 'thesis': 'Business is structurally declining', 'signals': ['Revenue declining', 'Margin compression', 'Customer churn rising', 'Market share loss'], 'catalyst': 'Quarterly results confirm trend', 'risk': 'Management turns around business' }, 'Accounting Fraud': { 'thesis': 'Financial statements do not reflect reality', 'signals': ['Revenue growing but cash not', 'Receivables growing faster than revenue', 'Auditor changes', 'CFO departures', 'Related party transactions', 'Frequent restatements'], 'catalyst': 'Short seller report, SEC investigation, restatement', 'risk': 'Timing — fraud can persist for years' }, 'Structural Disruption': { 'thesis': 'Business model being disrupted by technology/competition', 'examples': 'Blockbuster vs Netflix, Kodak vs digital', 'catalyst': 'Disruptor gaining market share visibly', 'risk': 'Disruption takes longer than expected' } } } def short_squeeze_risk(short_interest_pct, days_to_cover, float_pct, recent_price_move): """Assess risk of short squeeze on a position.""" score = 0 flags = [] if short_interest_pct > 0.20: score += 3 flags.append(f"Very high short interest: {short_interest_pct*100:.1f}%") elif short_interest_pct > 0.10: score += 2 if days_to_cover > 5: score += 3 flags.append(f"High days to cover: {days_to_cover:.1f}") elif days_to_cover > 3: score += 1 if float_pct < 0.20: score += 2 flags.append(f"Low float: {float_pct*100:.1f}%") if recent_price_move > 0.20: score += 2 flags.append(f"Already moving up: +{recent_price_move*100:.1f}%") return { 'squeeze_risk_score': score, 'risk_level': 'Extreme' if score >= 8 else 'High' if score >= 5 else 'Moderate'if score >= 3 else 'Low', 'flags': flags, 'recommendation': 'Reduce or exit short' if score >= 6 else 'Monitor closely' if score >= 4 else 'Position acceptable' } ``` --- ## Relative Value ```python def relative_value_strategies(): return { 'Convertible Bond Arbitrage': { 'concept': 'Long convertible bond, short underlying equity', 'edge': 'Convertibles often mispriced — buy cheap optionality', 'delta_hedge':'Short stock to neutralize equity exposure', 'profit': 'Gamma trading — rebalance delta as stock moves', 'risk': 'Credit risk on bond, short squeeze on equity' }, 'Fixed Income Relative Value': { 'on_the_run': 'Newest Treasury more liquid, trades rich vs off-the-run', 'trade': 'Long off-the-run, short on-the-run (cheaper vs expensive)', 'risk': 'Convergence can take time, liquidity crises widen spread' }, 'Volatility Arbitrage': { 'concept': 'Trade difference between implied and realized volatility', 'long_vol': 'Buy cheap options, delta hedge — profit from vol expansion', 'short_vol': 'Sell expensive options, delta hedge — collect premium', 'vix_arb': 'VIX futures vs S&P realized vol discrepancy' }, 'Capital Structure Arbitrage': { 'concept': 'Trade different securities of same company', 'example': 'Long bonds vs short equity when CDS/equity mispriced', 'tools': 'Merton model to find mispricing between debt and equity', 'risk': 'Correlation between instruments can change suddenly' } } ``` --- ## Fund Structure & Terms ```python def hedge_fund_terms(): return { 'Fee Structure': { '2_and_20': '2% management fee + 20% performance fee (standard)', 'high_water': 'Performance fee only on new profits above prior peak', 'hurdle_rate': 'Minimum return before performance fee kicks in (e.g. 8%)', 'crystallization': 'When performance fees are locked in (annual typical)', 'trend': 'Fees compressing — 1.5/17 or 1/15 more common now' }, 'Liquidity Terms': { 'lock_up': 'Period investor cannot redeem (1-2 years typical)', 'notice_period':'Required notice before redemption (30-90 days)', 'gates': 'Fund can limit redemptions in stress (10-25% quarterly)', 'side_pockets': 'Illiquid assets segregated, no redemption until liquidated' }, 'Risk Limits': { 'gross_exposure':'Max total long + short as % of NAV', 'net_exposure': 'Max net long or short directional bias', 'single_name': 'Max % of NAV in single position (5-10%)', 'sector_limit': 'Max sector concentration (20-30%)', 'var_limit': 'Daily VaR typically 1-3% of NAV' }, 'Prime Brokerage': { 'role': 'Provides leverage, stock lending, clearing', 'margin': 'Typically 20-50% initial margin for equities', 'stock_borrow': 'HTB (hard to borrow) stocks cost 5-30%+ annually', 'rehypothecation':'PB can use your assets as their own collateral' } } def performance_fee_calculation(beginning_nav, ending_nav, high_water_mark, mgmt_fee_pct=0.02, perf_fee_pct=0.20, hurdle=0.08): """Calculate hedge fund fees for a period.""" gross_return = (ending_nav - beginning_nav) / beginning_nav mgmt_fee = beginning_nav * mgmt_fee_pct # Performance fee only above HWM and hurdle new_hwm = max(high_water_mark, beginning_nav * (1 + hurdle)) perf_base = max(0, ending_nav - new_hwm) perf_fee = perf_base * perf_fee_pct net_nav = ending_nav - mgmt_fee - perf_fee net_return = (net_nav - beginning_nav) / beginning_nav return { 'gross_return': round(gross_return * 100, 2), 'net_return': round(net_return * 100, 2), 'management_fee': round(mgmt_fee, 0), 'performance_fee': round(perf_fee, 0), 'total_fees': round(mgmt_fee + perf_fee, 0), 'fee_drag': round((gross_return - net_return) * 100, 2), 'new_hwm': round(max(high_water_mark, ending_nav - perf_fee), 0) } ``` --- ## Common Pitfalls | Pitfall | Problem | Fix | |---|---|---| | Crowded longs/shorts | Everyone exits at same time | Track crowding via 13F filings | | Short squeeze ignoring | Small short destroyed by squeeze | Always check days to cover | | Merger deal break | Stock falls 20-30% instantly | Size arb positions by deal risk | | Factor exposure unhedged | Long/short has hidden beta | Hedge sector and factor exposures | | Illiquidity mismatch | Investors redeem, cannot sell | Match fund liquidity to portfolio | | High water mark trap | Never earn performance fees | Reset HWM or reduce management fee | | Leverage in crisis | Forced liquidation at worst time | Stress test leverage at 2008 levels | --- ## Best Practices - **Know your edge** — every strategy needs a behavioral or structural reason to work - **Manage crowding** — most popular trades are most dangerous - **Hedge your hedges** — long/short does not mean market neutral automatically - **Think about liquidity** — can you exit in a crisis without moving the market? - **Crisis alpha** — best strategies provide positive returns in equity crashes - **Fees matter** — 2 and 20 requires consistent alpha to beat passive investing - **Risk limits first** — define max loss before building the book --- ## Related Skills - **quantitative-finance-expert**: Systematic strategy development - **risk-management-expert**: Portfolio risk and exposure management - **behavioral-finance-expert**: Behavioral edges in L/S equity - **derivatives-expert**: Options strategies in event driven - **macro-economics-expert**: Global macro framework