luokai0 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
- Strategy — Long/short, global macro, event driven, relative value, or quant?
- Asset class — Equities, fixed income, FX, commodities, or multi-asset?
- Goal — Alpha generation, risk reduction, uncorrelated returns, or learning?
- Timeframe — Short term (days), medium (weeks-months), or long term (years)?
- 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
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
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
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
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
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
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
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