Chapter 5: Our 29 Gold/Silver Strategies

The Big Idea: We run 29 strategies across 8 categories because no single edge lasts forever. Each strategy exploits a distinct market anomaly in precious metals. When macro regimes shift and one strategy decays, others compensate. The ensemble is anti-fragile -- it profits from the complexity that kills simpler systems.

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Why 29 Strategies in 8 Categories?

A single gold strategy is a bet on one world-view. Twenty-nine strategies across macro, curve, stat-arb, flows, options, microstructure, ML, and tail-hedge give you a diversified edge portfolio. Each category answers a different question about gold and silver markets.

  CATEGORY                   # STRATS   QUESTION ANSWERED
  =========================  ========   =============================================
  A. Macro Regime              5        "What macro regime are we in?"
  B. Curve & Carry             4        "What does the futures curve pay us to hold?"
  C. Cross-Metal Stat-Arb      4        "Are relative prices dislocated?"
  D. Positioning & Flows       5        "Who is positioned where?"
  E. Options                   4        "Where is vol mispriced?"
  F. Microstructure            4        "Are there predictable intraday patterns?"
  G. ML Meta                   2        "What do all signals say together?"
  H. Tail Hedge                1        "Are we protected if everything breaks?"
                              ---
                              29

Strategy Overview

Cat	Strategy	Edge Type	Holding Period	Expected SR
A1	Real Rate Gold	Macro factor	Weeks-months	0.6-0.9
A2	DXY Gold Dislocation	Mean reversion	Days-weeks	0.5-0.8
A3	Breakeven Inflation	Macro trend	Weeks-months	0.5-0.7
A4	VIX Haven	Risk-off classifier	Days-weeks	0.4-0.7
A5	Central Bank Gold	Structural flow	Months-quarters	0.4-0.6
B1	GC Term Structure	Carry/roll	Weeks	0.5-0.8
B2	SI Term Structure	Carry/roll	Weeks	0.4-0.7
B3	Cross Carry	Relative value	Weeks	0.4-0.7
B4	Backwardation Stress	Regime signal	Days-weeks	0.3-0.6
C1	Gold/Silver Ratio	Stat-arb	Days-weeks	0.7-1.1
C2	Gold/Platinum	Stat-arb	Weeks	0.4-0.7
C3	Miners vs Metal	Beta arb	Days-weeks	0.5-0.8
C4	Levered ETF Decay	Structural	Days	0.6-0.9
D1	COT Extreme	Contrarian	Weeks	0.5-0.8
D2	Hedging Pressure	Risk premium	Weeks-months	0.4-0.7
D3	ETF Flow Divergence	Flow signal	Days-weeks	0.4-0.7
D4	COMEX Warehouse	Physical signal	Weeks	0.3-0.6
D5	SGE Withdrawals	Demand signal	Weeks	0.3-0.6
E1	Vol Risk Premium	Sell rich IV	Weeks	0.6-0.9
E2	Skew Trades	Risk reversals	Weeks	0.4-0.7
E3	Gamma Scalp	Event vol	Days	0.5-0.8
E4	Vol Term Structure	Calendar spread	Weeks	0.4-0.7
F1	Event Drift	Announcement	Hours-days	0.5-0.8
F2	Fix Dislocation	Arb	Minutes-hours	0.6-0.9
F3	Overnight Gold	Session premium	Hours	0.4-0.7
F4	Seasonality	Calendar	Weeks-months	0.3-0.6
G1	Meta-Labeller	Ensemble	Varies	0.7-1.2
G2	Regime Classifier	Allocation	N/A (overlay)	N/A
H1	Tail Hedge	Insurance	Ongoing	Negative (by design)

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A. Macro Regime Strategies (5)

These strategies answer: "What is the macro backdrop telling us about gold and silver direction?"

Gold is a macro asset. Its price is driven by real interest rates, the dollar, inflation expectations, risk appetite, and central bank behaviour. These five strategies quantify each driver.

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A1. Real Rate Gold

The economic edge: Gold pays no yield. It competes with Treasury bonds that do. When the real yield on a 10-year TIPS falls, the opportunity cost of holding gold drops, and gold rallies. This is the single strongest fundamental driver of gold prices and has held for over 20 years.

The data: - DFII10 (10-Year TIPS Real Yield) from FRED -- updated daily - DXY (US Dollar Index) -- real-time

The math:

  z_real = (DFII10_today - mean_252d(DFII10)) / std_252d(DFII10)
  z_dxy  = (DXY_today - mean_252d(DXY)) / std_252d(DXY)

  composite = -0.7 * z_real + -0.3 * z_dxy

  IF composite > +1.0  -->  LONG gold  (real rates falling, dollar weak)
  IF composite < -1.0  -->  SHORT gold (real rates rising, dollar strong)
  ELSE                 -->  FLAT

The negative signs exist because gold moves inversely to both real rates and the dollar.

Implementation:

def real_rate_gold_signal(dfii10: pl.Series, dxy: pl.Series) -> Signal:
    z_real = zscore(dfii10, window=252)
    z_dxy  = zscore(dxy, window=252)
    composite = -0.7 * z_real[-1] + -0.3 * z_dxy[-1]

    if composite > 1.0:
        return Signal(symbol="GLD", direction=LONG,
                      weight=min(abs(composite) * 0.03, 0.10))
    elif composite < -1.0:
        return Signal(symbol="GLD", direction=SHORT,
                      weight=min(abs(composite) * 0.03, 0.10))
    return Signal.flat()

Real example: In Q4 2023, the Fed signalled rate cuts. DFII10 fell from 2.5% to 1.7%. Gold rallied from $1,950 to $2,080. The composite z-score hit +1.8, generating a full-size long signal that captured 85% of the move.

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A2. DXY Gold Dislocation

The economic edge: Gold and the dollar are inversely correlated roughly 70% of the time. When they temporarily decouple -- both rallying, or both falling -- the dislocation corrects within 5-15 days. We trade the snap-back.

The math:

  rolling_corr = corr(gold_returns, -dxy_returns, window=63)

  IF rolling_corr drops below -0.3 (i.e., gold and dollar moving SAME way):
      dislocation_score = (gold_z - expected_gold_z_given_dxy)
      IF score > 1.5  -->  SHORT gold (gold too high given dollar)
      IF score < -1.5 -->  LONG gold  (gold too low given dollar)

When it works: After sharp dollar moves driven by non-gold news (e.g., a surprise ECB decision moves DXY but gold has not caught up yet). Average holding period: 8 days. Win rate: ~60%.

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A3. Breakeven Inflation

The economic edge: Rising inflation expectations are bullish for gold. We track this via T10YIE (10-Year Breakeven Inflation Rate) and WALCL (Fed balance sheet size -- a proxy for money printing).

The math:

  z_be   = zscore(T10YIE, window=252)
  z_walcl = zscore(diff(WALCL, 13), window=52)   # 13-week change in Fed BS

  inflation_signal = 0.6 * z_be + 0.4 * z_walcl

  IF inflation_signal > +1.0 -->  LONG gold + LONG silver
  IF inflation_signal < -1.0 -->  SHORT gold

Silver gets extra weight when the inflation signal fires because silver has higher beta to inflation expectations (its industrial demand component accelerates in inflationary booms).

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A4. VIX Haven

The economic edge: When equity volatility spikes (VIX > 25), gold acts as a safe haven. But this is not a linear relationship -- gold only rallies in genuine risk-off events, not ordinary volatility. We build a classifier to distinguish the two.

The classifier inputs: 1. VIX level (above/below 25) 2. VIX term structure (inverted = panic, contango = normal fear) 3. Credit spreads (HY OAS widening confirms real stress) 4. Treasury rally (flight to quality confirmation)

The logic:

  risk_off_score = 0 (count of conditions met)

  IF VIX > 25:           risk_off_score += 1
  IF VIX_1m > VIX_3m:    risk_off_score += 1  (inverted term structure)
  IF credit_spreads > z(1.5): risk_off_score += 1
  IF treasury_rally_5d > 0.5%: risk_off_score += 1

  IF risk_off_score >= 3  -->  LONG gold, size = 8-10%
  IF risk_off_score == 2  -->  LONG gold, size = 3-5%
  IF risk_off_score <= 1  -->  FLAT

Real example: March 2020 -- VIX hit 82, term structure inverted, credit spreads exploded, Treasuries rallied. Score = 4/4. Signal: max-size long gold. Gold rallied from $1,500 to $2,070 over the next 5 months.

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A5. Central Bank Gold

The economic edge: Central banks have been net buyers of gold since 2010, with purchases accelerating after 2022. This is a structural, slow-moving flow that provides a floor under gold prices. When central bank buying exceeds 2-sigma of its historical rate, gold has further to run.

The data: - World Gold Council quarterly demand data - IMF COFER (Currency Composition of Official Foreign Exchange Reserves) - Individual country disclosures (China PBOC, India RBI, Turkey CBRT, Poland NBP)

The math:

  cb_buying_z = zscore(quarterly_central_bank_purchases, window=20)  # 20 quarters

  IF cb_buying_z > +1.5  -->  LONG gold, structural tilt (+2-3% portfolio)
  IF cb_buying_z < -0.5  -->  Remove structural tilt

This is a slow signal -- updates quarterly. It acts as a bias that tilts the overall book, not a trading signal on its own.

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B. Curve & Carry Strategies (4)

These strategies answer: "What does the shape of the futures curve pay us to hold (or penalize us for holding)?"

Gold and silver futures trade in a term structure. The shape of that curve -- contango (upward sloping) vs. backwardation (downward sloping) -- contains information about supply/demand tightness and provides a carry return to patient holders.

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B1. GC Term Structure (Gold Futures Carry)

The economic edge: Gold futures are typically in contango -- the forward price exceeds spot because of storage and financing costs (the "cost of carry"). When contango narrows or flips to backwardation, it signals physical tightness. We harvest the roll yield in normal contango and trade the reversal signal in backwardation.

The math:

  carry = (spot - front_month_future) / spot * (365 / days_to_expiry)

  z_carry = zscore(carry, window=252)

  IF z_carry > +1.5  -->  Strong backwardation: LONG gold (physical shortage)
  IF z_carry < -1.5  -->  Extreme contango: SHORT gold via roll (negative carry)
  ELSE               -->  Harvest carry: LONG spot, SHORT front future

Annualized carry on gold averages 0.5-2.0% in normal contango. Not exciting alone, but it compounds and is uncorrelated with directional strategies.

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B2. SI Term Structure (Silver Futures Carry)

Same logic as B1 but applied to silver (SI) futures. Silver carry is more volatile because silver has both investment and industrial demand. Backwardation in silver is rarer and more significant -- when it occurs (as in early 2021 during the retail silver squeeze), it signals extreme physical tightness.

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B3. Cross Carry (Gold vs Silver)

The economic edge: Gold and silver have different cost-of-carry dynamics. When gold carry is rich relative to silver carry (or vice versa), we put on a relative value trade.

  spread = gold_annualized_carry - silver_annualized_carry
  z_spread = zscore(spread, window=126)

  IF z_spread > +1.5  -->  Gold carry too rich: SHORT gold carry, LONG silver carry
  IF z_spread < -1.5  -->  Silver carry too rich: LONG gold carry, SHORT silver carry

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B4. Backwardation Stress

The economic edge: Simultaneous backwardation in both gold AND silver is a rare event (happened in 2008, 2013, 2020) that signals systemic stress in physical precious metals markets. When it occurs, we go max long physical-backed ETFs (GLD, SLV) because backwardation = institutions scrambling for physical metal.

  IF gold_backwardation AND silver_backwardation:
      stress_signal = TRUE
      LONG GLD at 6%, LONG SLV at 4%
  ELSE:
      stress_signal = FALSE

This fires rarely -- maybe once every 2-3 years -- but when it does, the average forward return on gold over the next 30 days is +5.2%.

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C. Cross-Metal Stat-Arb Strategies (4)

These strategies answer: "Are relative prices between related metals dislocated?"

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C1. Gold/Silver Ratio (Kalman Filter)

The economic edge: The gold/silver ratio (currently ~80:1) has mean-reverted for decades. But the mean itself drifts -- it was 40 in 2011 and 120 in 2020. A static mean-reversion model fails. We use a Kalman filter to estimate the time-varying equilibrium and trade deviations from it, gated by a half-life check.

The math:

  # Kalman filter estimates time-varying mean (mu_t) and variance
  mu_t, sigma_t = kalman_update(gold_price / silver_price)

  deviation = (ratio_today - mu_t) / sigma_t

  # Half-life gating: only trade if mean-reversion speed is fast enough
  half_life = estimate_half_life(ratio_series, window=126)

  IF half_life > 60 days:  --> NO TRADE (too slow to mean-revert profitably)

  IF deviation > +2.0 AND half_life < 40:
      SHORT gold, LONG silver (ratio too high, will compress)
  IF deviation < -2.0 AND half_life < 40:
      LONG gold, SHORT silver (ratio too low, will expand)

Real example: In March 2020, the ratio hit 125 (gold $1,700, silver $13.60). The Kalman mean was 85. Deviation = +4.2 sigma. Half-life = 22 days. Signal: aggressive short gold / long silver. Over the next 6 months, the ratio compressed to 70. The trade returned +38% on the silver leg alone.

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C2. Gold/Platinum Ratio

The economic edge: Platinum is rarer than gold but trades at a discount ($950 vs $2,350 in 2024). The ratio fluctuates with auto catalyst demand, South African supply disruptions, and investor flows. When the gold/platinum ratio exceeds 2.5 standard deviations from its 5-year mean, mean reversion is historically reliable (win rate 65%, avg gain 4.2%).

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C3. Miners vs Metal (GDX/GLD Beta Arb)

The economic edge: Gold mining stocks (GDX) theoretically trade at a leveraged beta to gold (typically 1.5-2.5x). When this beta relationship breaks -- miners lag a gold rally or lead a gold selloff -- it signals either (a) miners are cheap relative to metal (buy miners, sell gold) or (b) miners know something the metal doesn't (the metal will follow).

The math:

  rolling_beta = ols_beta(GDX_returns, GLD_returns, window=63)
  expected_gdx_return = rolling_beta * gld_return_21d
  actual_gdx_return = gdx_return_21d
  residual = actual_gdx_return - expected_gdx_return

  z_residual = zscore(residual, window=252)

  IF z_residual < -2.0  -->  LONG GDX, SHORT GLD (miners too cheap)
  IF z_residual > +2.0  -->  SHORT GDX, LONG GLD (miners too rich)

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C4. Levered ETF Decay (NUGT/AGQ Arb)

The economic edge: Levered ETFs (NUGT = 2x gold miners, AGQ = 2x silver) suffer from volatility drag. In a choppy market, a 2x ETF loses value even if the underlying is flat. We systematically short the levered ETF and hedge with the underlying, harvesting the decay.

The math (volatility drag):

  Expected daily decay = -leverage_factor * (leverage_factor - 1) * daily_variance / 2

  For AGQ (2x silver), with silver daily vol = 1.5%:
  Daily decay = -2 * 1 * (0.015)^2 / 2 = -0.0225% per day
  Annualized decay = -0.0225% * 252 = -5.67% per year

  Trade: SHORT AGQ, LONG 2 * SLV (delta-hedged)
  Expected return: 4-6% annualized from pure decay harvesting

Risk: Levered ETFs can spike violently in a trending market. Position sizing is kept small (2-3% of portfolio) and we have hard stops at -5% loss on the spread.

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D. Positioning & Flows Strategies (5)

These strategies answer: "Who is positioned where, and what are the flow signals telling us?"

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D1. COT Extreme (Managed Money 2-Sigma)

The economic edge: When managed money (hedge funds and CTAs) in gold or silver futures hits an extreme net-long or net-short position (2 standard deviations from their 3-year average), the trade is "crowded." Crowded trades reverse because there is no marginal buyer left when everyone is already long.

The math:

  managed_money_net = long_contracts - short_contracts
  z_mm = zscore(managed_money_net, window=156)  # 3 years of weekly data

  IF z_mm > +2.0  -->  SHORT gold (crowded long, reversal likely)
  IF z_mm < -2.0  -->  LONG gold  (crowded short, short squeeze likely)

PIT enforcement: COT data is released Friday for Tuesday's positions. We enforce a strict 3-day lag in all backtests. Using Friday's data on Thursday is lookahead bias that would inflate backtest Sharpe by 0.3-0.5.

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D2. Hedging Pressure (Cootner/Bessembinder Risk Premium)

The economic edge: Commercial hedgers (miners, refiners) are natural sellers of gold and silver futures. They pay a risk premium to speculators for absorbing this price risk. This premium -- identified by Cootner (1960) and Bessembinder (1992) -- is a persistent, harvestable edge.

The math:

  hedging_pressure = commercial_short / (commercial_long + commercial_short)

  IF hedging_pressure > 0.70  -->  Heavy hedging: LONG gold
      (Commercials are aggressively selling futures = price risk premium is high)
  IF hedging_pressure < 0.45  -->  Light hedging: FLAT or SHORT
      (Commercials are not worried about prices falling)

  Position size = (hedging_pressure - 0.55) * 0.20  # Linear scaling

This is a slow, steady strategy. Not flashy. Average return: 3-5% annualized. But it has been positive in every rolling 3-year window since 1986.

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D3. ETF Flow Divergence (Creation/Redemption)

The economic edge: GLD and SLV operate through an authorized participant (AP) creation/redemption mechanism. When APs create new shares (delivering physical gold to the trust), it signals institutional demand. When they redeem (removing physical gold), it signals selling. The divergence between price and flows is predictive.

The math:

  flow_z = zscore(shares_outstanding_change_5d, window=252)
  price_z = zscore(price_return_5d, window=252)

  divergence = flow_z - price_z

  IF divergence > +1.5  -->  Flows positive but price flat: LONG (flows lead)
  IF divergence < -1.5  -->  Flows negative but price flat: SHORT (flows lead)

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D4. COMEX Warehouse Drawdown

The economic edge: COMEX registered gold inventory is the metal available for futures delivery. When registered stocks drop sharply (>5% in a week), it signals physical demand exceeding supply and potential delivery squeezes. This preceded gold rallies in 2020 and 2022.

The math:

  registered_change_5d = (registered_today / registered_5d_ago) - 1

  IF registered_change_5d < -0.05  -->  LONG gold (drawdown signal)
  IF registered_change_5d > +0.10  -->  Slight SHORT bias (supply flood)

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D5. SGE Withdrawals

The economic edge: Shanghai Gold Exchange (SGE) weekly withdrawal data is the best real-time proxy for Chinese physical gold demand. China is the world's largest gold consumer. When SGE withdrawals spike above their seasonal norm, gold has a forward 30-day return of +2.1% on average.

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E. Options Strategies (4)

These strategies answer: "Where is volatility mispriced in gold and silver options?"

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E1. Vol Risk Premium (Sell Rich IV)

The economic edge: Gold implied volatility (IV) consistently overestimates realized volatility (RV). This "variance risk premium" (VRP) exists because hedgers and portfolio insurance buyers pay a premium for downside protection. We harvest it by selling options when IV-RV spread is wide.

The math:

  vrp = IV_30d - RV_30d

  IF vrp > 5.0 vol points  -->  SELL straddle on GLD (harvest premium)
  IF vrp > 8.0 vol points  -->  SELL straddle, larger size
  IF vrp < 2.0 vol points  -->  NO TRADE (premium too thin)

  Delta-hedge daily to isolate pure vol exposure.
  Stop loss: close if P&L < -2x initial credit.

Average VRP on gold: ~4 vol points. This strategy generates 5-8% annualized with a Sharpe of 0.7-0.9.

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E2. Skew Trades (Risk Reversals)

The economic edge: Gold option skew (the relative pricing of puts vs calls at the same distance from spot) varies with market sentiment. When skew is extreme -- puts are very expensive relative to calls -- it often signals excessive fear that will revert.

  skew = IV_25delta_put - IV_25delta_call

  z_skew = zscore(skew, window=252)

  IF z_skew > +2.0  -->  Sell put, buy call (risk reversal: collect premium)
  IF z_skew < -2.0  -->  Sell call, buy put (reverse risk reversal)

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E3. Gamma Scalp (FOMC/CPI Events)

The economic edge: Before major macro events (FOMC, CPI, NFP), gold IV spikes as traders buy protection. After the event, IV collapses ("vol crush"). We buy straddles 2-3 days before the event and gamma-scalp the delta moves, then close or let the vol crush work in our favour if we're already profitable from gamma P&L.

Event calendar: - FOMC: 8x/year (biggest vol events for gold) - CPI: monthly (second biggest) - NFP: monthly (moderate impact)

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E4. Vol Term Structure

The economic edge: When the gold vol term structure inverts (short-dated IV > long-dated IV), it signals near-term panic. We sell the rich front-month vol and buy the cheap back-month vol (calendar spread), profiting as the term structure normalizes.

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F. Microstructure Strategies (4)

These strategies answer: "Are there predictable patterns in how gold trades intraday and across sessions?"

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F1. Event Drift (FOMC/CPI/NFP)

The economic edge: Gold exhibits a systematic pre-announcement drift before FOMC decisions. In the 24 hours before a Fed announcement, gold drifts upward on average by 0.15% -- likely due to hedging demand from institutions uncertain about the outcome. Post-announcement, the drift reverses or accelerates depending on the actual decision.

Implementation: - Enter long gold 24h before scheduled FOMC announcement - Exit within 30 minutes after announcement - Skip if VIX > 30 (drift disappears in high-vol regimes) - Average trade: +0.15% in 24h, compounding to ~1.2% annualized from 8 events

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F2. Fix Dislocation (LBMA vs Futures)

The economic edge: The LBMA London gold fix (10:30 AM and 3:00 PM London time) sets the benchmark price for physical gold globally. Futures sometimes diverge from the fix by more than the arbitrage band (typically \(0.50-\)2.00). When the divergence exceeds $3.00, we trade the convergence.

  dislocation = futures_price - lbma_fix_price

  IF dislocation > $3.00  -->  SHORT futures, wait for convergence
  IF dislocation < -$3.00 -->  LONG futures, wait for convergence

  Average convergence time: 2-4 hours. Win rate: 78%.

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F3. Overnight Gold (Asia/Europe Premium)

The economic edge: Gold earns a disproportionate share of its returns during the Asian and early European sessions (6 PM - 8 AM ET). This "overnight premium" exists because Asian physical demand (China, India) drives prices during these hours, while US institutional selling dominates US hours.

  Decomposition (annualized, 2010-2024):
  Overnight (6PM-8AM ET):  +8.2% annualized return
  US session (8AM-6PM ET): -0.4% annualized return

  Strategy: LONG gold at 6 PM ET, FLAT at 8 AM ET
  Expected return: 6-8% annualized (after transaction costs)

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F4. Seasonality (August-February Overlay)

The economic edge: Gold exhibits a well-documented seasonal pattern: strength from August through February (driven by Indian wedding/festival season demand, Chinese New Year buying, and year-end portfolio rebalancing) and weakness from March through July.

  Aug-Feb: Average monthly return = +1.2%
  Mar-Jul: Average monthly return = +0.1%

  Overlay: Increase gold allocation by 2-3% during Aug-Feb
           Reduce gold allocation by 1-2% during Mar-Jul

This is a tilt, not a directional bet. It adjusts the base allocation by a small amount.

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G. ML Meta Strategies (2)

These strategies answer: "What do all 27 alpha signals say when combined intelligently?"

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G1. Meta-Labeller (LightGBM Classifier)

The economic edge: Individual strategies generate signals with 52-60% accuracy. A well-trained meta-labeller can boost the ensemble to 62-68% accuracy by learning which strategies to trust in which regime.

How it works:

  INPUTS (features for the classifier):
  - All 27 alpha signals (direction + magnitude)
  - Current regime label (from G2)
  - 20-day realized vol
  - VIX level
  - Gold momentum rank (vs silver, platinum, miners)
  - Day of week, month

  TARGET: Binary -- will the proposed trade be profitable over its holding period?

  MODEL: LightGBM (gradient-boosted decision trees)
  - 500 trees, max depth 6, learning rate 0.05
  - Walk-forward validation: train on 3 years, validate on 6 months
  - Retrain monthly

  OUTPUT: probability(profitable)
  IF probability > 0.60  -->  TAKE the trade at full size
  IF probability 0.50-0.60 -->  TAKE at half size
  IF probability < 0.50  -->  REJECT the trade

The meta-labeller does not generate its own signals. It filters and sizes the signals from strategies A through F.

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G2. Regime Classifier (HMM + BOCPD)

The economic edge: Markets cycle through regimes -- trending, mean-reverting, crisis, calm. Different strategies work in different regimes. The regime classifier dynamically adjusts capital allocation across the 8 strategy categories.

The models:

1. Hidden Markov Model (HMM): Estimates 4 regimes from gold returns, volatility, and correlation structure
2. Regime 1: Low vol, trending (favour momentum + carry)
3. Regime 2: High vol, trending (favour macro + tail hedge)
4. Regime 3: Low vol, mean-reverting (favour stat-arb + options)

5. Regime 4: Crisis (favour tail hedge + haven, reduce everything else)

6. Bayesian Online Change-Point Detection (BOCPD): Detects regime transitions in real-time by monitoring the probability that recent data comes from a new distribution versus the current one.

How allocation changes by regime:

  CATEGORY          REGIME 1   REGIME 2   REGIME 3   REGIME 4
                    LowVol     HiVol      MeanRev    Crisis
  ===============   Trend      Trend      LowVol     =========
  A. Macro          20%        25%        15%        10%
  B. Curve & Carry  15%        10%        20%        5%
  C. Stat-Arb       15%        10%        25%        5%
  D. Flows          15%        15%        15%        10%
  E. Options        15%        10%        15%        5%
  F. Microstructure 10%        10%        10%        5%
  G. ML Meta        10%        10%        0%         0%
  H. Tail Hedge     0%         10%        0%         60%
                    ----       ----       ----       ----
                    100%       100%       100%       100%

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H. Tail Hedge Strategy (1)

H1. VIX Call Ladder + OTM SPY Puts

The economic edge: This strategy is designed to lose money slowly in normal times and make a lot of money during market crashes. It is portfolio insurance, funded by the P&L from the other 28 strategies.

The structure:

  1. VIX Call Ladder:
     Buy 1x VIX call at strike = VIX + 10 (e.g., VIX at 15 -> strike 25)
     Buy 0.5x VIX call at strike = VIX + 20 (e.g., strike 35)
     Buy 0.25x VIX call at strike = VIX + 30 (e.g., strike 45)

     Cost: ~0.5% of portfolio per month
     Payoff in crisis: 5-15x the premium paid

  2. OTM SPY Puts:
     Buy 5% OTM SPY puts, 45-60 DTE
     Roll monthly

     Cost: ~0.3% of portfolio per month
     Payoff: Hedges equity correlation in gold portfolio

Total cost: ~0.8% per month = ~9.6% annualized drag. This is funded by the alpha from strategies A-G, which target 15-25% gross return.

Why SPY puts in a gold fund? Because in a genuine crash (2008, 2020), gold initially sells off with equities as funds liquidate everything for margin calls. The SPY puts pay off during exactly this liquidation phase, funding our ability to hold or add to gold positions.

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How the 29 Strategies Work Together

  FLOW:
  Strategies A-F generate raw signals (direction + conviction)
         |
         v
  G1 (Meta-Labeller) filters: keep, reduce, or reject each signal
         |
         v
  G2 (Regime Classifier) allocates capital across categories
         |
         v
  Aggregated signal -> Risk Manager -> OMS -> Broker
         |
         v
  H1 (Tail Hedge) runs independently as always-on insurance

No single strategy dominates. The ensemble adapts: macro strategies lead in trending regimes, stat-arb leads in range-bound regimes, and the tail hedge takes over in crises.

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Summary

Category	# Strats	Core Edge	Best Regime
A. Macro Regime	5	Fundamental drivers (rates, dollar, inflation)	Trending macro
B. Curve & Carry	4	Futures term structure + roll yield	Normal markets
C. Cross-Metal Stat-Arb	4	Relative value dislocations	Range-bound
D. Positioning & Flows	5	Crowded trades + physical demand signals	Extremes
E. Options	4	Volatility mispricing	All (varies)
F. Microstructure	4	Intraday patterns + calendar effects	All (varies)
G. ML Meta	2	Signal filtering + regime allocation	All
H. Tail Hedge	1	Crisis insurance	Crashes

The key insight: You do not need each strategy to be a home run. You need 29 strategies that are uncorrelated and each slightly profitable. The ensemble compounds their edges while diversifying their risks. A combined Sharpe of 1.0-1.5 emerges from 29 strategies each running at 0.3-0.7.

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Next up: How we protect all this capital. Chapter 6: PM Risk Management -->