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ETF/Index Optimization Results - 2025-11-01

Date: November 1, 2025 Status: ❌ All 3 ETF Experiments Failed Validation Decision: Focus on individual stock strategies only

🎯 Executive Summary

All 3 ETF optimization experiments failed minimum validation criteria. ETFs (QQQ, VGT, VTI, ARKK) are too stable for mean reversion and momentum strategies during the 2024-2025 period. Individual stock strategies (TSLA, GME, RIOT, PLTR) remain the best performers.

Key Finding: ETF strategies generated 1-6 trades in 7 months (vs. 15-36 trades for volatile stocks). ETFs don't hit extreme RSI/Bollinger Band levels needed to trigger signals.

📊 Experiment Results Summary

Experiment Strategy Symbols Trades Return Sharpe Win Rate Status
1. ETF Mean Rev Mean Reversion (ETF params) QQQ, VGT, VTI 6 -9.2% -1.12 50% ❌ FAILED
2. ETF Momentum Micro-Cap Momentum QQQ, VGT, ARKK 2 0.1% 0.27 100% ❌ FAILED
3. ARKK Solo Mean Reversion (ETF params) ARKK 1 1.1% 0.83 100% ❌ FAILED

Validation Period: 2024-04-01 to 2025-10-30 (7 months)

Minimum Criteria (not met): - ✅ Trades >= 10 (required) - ✅ Win Rate >= 50% - ✅ Positive Sharpe

All experiments failed: Insufficient trade count (1-6 trades vs. minimum 10)

🔍 Detailed Results

Experiment 1: ETF Mean Reversion (QQQ, VGT, VTI)

Configuration: - Strategy: mean_reversion with --asset-type etf - Symbols: QQQ, VGT, VTI (low-volatility tech ETFs) - Training: 2020-01-01 to 2024-03-31 - Validation: 2024-04-01 to 2025-10-30 - Population: 20, Generations: 20

ETF Parameter Ranges (wider than stocks): 

{
    "rsi_oversold": (20, 30),       # More extreme
    "rsi_overbought": (70, 80),     # More extreme
    "bb_std": (2.5, 3.5),           # Wider bands
    "stop_loss_pct": (0.08, 0.15),  # Tighter stops
    "base_position_size": (1500, 2000),  # Larger positions
    "max_positions": (2, 3),        # Fewer positions
}

Best Strategy: 

{
  "base_position_size": 1616,
  "bb_std": 2.5,
  "max_positions": 2,
  "rsi_overbought": 80,
  "rsi_oversold": 30,
  "stop_loss_pct": 0.14
}

Results: - Training: Sharpe -0.54, Return -6.4%, Drawdown 6.9% - Validation: Sharpe -1.12, Return -9.2%, Drawdown 11.8% - Trades: 6 (4 wins, 2 losses) - Win Rate: 50.0% (barely meets threshold)

Why It Failed: - Only 6 trades in 7 months (need 10+) - Negative returns in both training and validation - ETFs didn't hit extreme RSI levels (20-30 oversold, 70-80 overbought) - Bollinger Bands at 2.5 std still too narrow for ETF stability

Experiment 2: ETF Momentum (QQQ, VGT, ARKK)

Configuration: - Strategy: micro_cap_momentum (designed for stocks) - Symbols: QQQ, VGT, ARKK (ARKK more volatile) - Training: 2020-01-01 to 2024-03-31 - Validation: 2024-04-01 to 2025-10-30 - Population: 20, Generations: 20

Best Strategy: 

{
  "base_position_size": 438,
  "breakout_period": 17,
  "max_positions": 7,
  "rsi_threshold": 60,
  "time_stop_days": 13,
  "trailing_stop_pct": 0.15,
  "volume_multiplier": 2.0
}

Results: - Training: Sharpe -0.74, Return -1.2%, Drawdown 1.5% - Validation: Sharpe 0.27, Return 0.1%, Drawdown 0.2% - Trades: 2 (2 wins, 0 losses) - Win Rate: 100.0% (but only 2 trades!)

Why It Failed: - Only 2 trades in 7 months (far below minimum 10) - ETFs don't have breakout momentum signals - Volume confirmation (2x average) rarely triggers on ETFs - Micro-cap momentum strategy not suitable for ETFs

Experiment 3: ARKK Solo Volatility

Configuration: - Strategy: mean_reversion with --asset-type etf - Symbol: ARKK only (most volatile ETF) - Training: 2020-01-01 to 2024-03-31 - Validation: 2024-04-01 to 2025-10-30 - Population: 20, Generations: 20

Rationale: ARKK has individual-stock-like volatility (3-8% daily moves vs. 1-3% for QQQ/VGT). Expected to generate more signals.

Best Strategy: 

{
  "base_position_size": 1632,
  "bb_std": 2.6,
  "max_positions": 3,
  "rsi_overbought": 80,
  "rsi_oversold": 28,
  "stop_loss_pct": 0.11
}

Results: - Training: Sharpe 0.00, Return 0.0%, Drawdown 0.0% - Validation: Sharpe 0.83, Return 1.1%, Drawdown 0.3% - Trades: 1 (1 win, 0 losses) - Win Rate: 100.0% (but only 1 trade!)

Why It Failed: - Only 1 trade in 7 months (far below minimum 10) - Even ARKK (most volatile ETF) didn't trigger enough signals - RSI 28/80 thresholds still too extreme for ARKK during 2024-2025 - Bollinger Band 2.6 std still too wide

🔴 Root Cause Analysis

Why ETFs Failed vs. Why Individual Stocks Succeeded

Factor Individual Stocks (TSLA, GME, RIOT, PLTR) ETFs (QQQ, VGT, VTI, ARKK)
Daily Volatility 5-15% daily moves 1-3% daily moves (3-8% for ARKK)
RSI Extremes Frequently hit 20-30 (oversold) and 60-80 (overbought) Rarely hit 20-30 or 70-80
BB Breaches Frequently breach 2.0-2.4 std bands Rarely breach even 2.5-3.5 std bands
Signal Frequency 15-36 trades/year 1-6 trades/year
Market Regime Volatile stocks thrive in choppy markets ETFs grind up slowly (2024-2025 bull)
Diversification Single stock risk = high volatility Diversified = low volatility

Specific Issues

1. 2024-2025 Bull Market: - Tech ETFs (QQQ, VGT) grinding upward slowly - No major corrections to trigger mean reversion signals - Momentum strategies need breakouts, not slow grinds

2. ETF Structural Stability: - ETFs are baskets of 50-100+ stocks - Diversification smooths volatility - Individual stock crashes don't affect ETF much - Example: QQQ has 100 Nasdaq stocks - one stock crashing barely moves it

3. Parameter Tuning Insufficient: - Even with extreme ETF params (RSI 20/80, BB 2.5-3.5 std), not enough signals - Would need RSI 10/90 or BB 4.0-5.0 std to match stock signal frequency - But those parameters would have massive drawdowns

📈 Comparison: ETF vs. Individual Stock Strategies

Individual Stock Strategies (Oct 30-31, 2025) ✅ SUCCESS

Strategy Symbols Trades Sharpe Return Win Rate Status
Mean Rev - Volatile ⭐ TSLA, RIOT, PLTR, GME 18 1.08 502% 61% ✅ PASS
Micro-Cap - 4 Symbols PLTR, SOFI, RIOT, MARA 15 0.42 199% 53% ✅ PASS
Micro-Cap - 12 Symbols 12 symbols 36 0.19 96% 53% ✅ PASS

Why Stocks Succeeded: - High volatility → frequent RSI extremes - Choppy price action → mean reversion opportunities - 15-36 trades in 7 months (vs. 1-6 for ETFs) - Strong returns (96-502%)

ETF Strategies (Nov 1, 2025) ❌ FAILURE

Strategy Symbols Trades Sharpe Return Win Rate Status
ETF Mean Rev QQQ, VGT, VTI 6 -1.12 -9.2% 50% ❌ FAIL
ETF Momentum QQQ, VGT, ARKK 2 0.27 0.1% 100% ❌ FAIL
ARKK Solo ARKK 1 0.83 1.1% 100% ❌ FAIL

Why ETFs Failed: - Low volatility → insufficient signal generation - Bull market grind → no mean reversion opportunities - 1-6 trades in 7 months (vs. 15-36 for stocks) - Weak returns (-9% to +1%)

💡 Lessons Learned

1. Volatility is King for Signal-Based Strategies

Mean reversion and momentum require volatility to generate signals. ETFs are structurally less volatile due to diversification.

Takeaway: Focus strategies on high-volatility assets (individual stocks, crypto, leveraged ETFs).

2. Bull Markets Kill Mean Reversion

2024-2025 tech bull market = QQQ/VGT grinding upward with no corrections. Mean reversion needs choppy, oscillating markets.

Takeaway: Mean reversion strategies need sideways or volatile markets, not trending bull markets.

3. Strategy-Asset Mismatch

Micro-cap momentum (breakout strategy) was designed for volatile individual stocks. Applying it to stable ETFs was a category error.

Takeaway: Match strategy type to asset characteristics. Don't force a strategy onto incompatible assets.

4. Parameter Tuning Has Limits

Even extreme ETF parameters (RSI 20/80, BB 2.5-3.5 std) couldn't generate enough signals. The asset itself (ETF stability) was the blocker.

Takeaway: No amount of parameter tuning can overcome fundamental asset characteristics.

5. Historical Period Matters

2020-2024 training period included COVID crash and recovery (high volatility). 2024-2025 validation period = steady bull market (low volatility). Regime change killed the strategy.

Takeaway: Test strategies across multiple market regimes (bull, bear, sideways, crisis).

🚀 Recommendations

✅ DO: Focus on What Works

Individual Stock Strategies (proven winners): 1. Mean Reversion - Volatile Stocks (TSLA, GME, RIOT, PLTR): 502% return, 1.08 Sharpe ⭐ 2. Micro-Cap Momentum (PLTR, SOFI, RIOT, MARA): 199% return, 0.42 Sharpe 3. Deploy these to Alpaca paper trading immediately

❌ DON'T: Force ETF Strategies

ETF strategies are not viable with current mean reversion/momentum approaches: - QQQ, VGT, VTI too stable - ARKK slightly better but still insufficient - Bull market regime prevents mean reversion

🔬 EXPERIMENT: Alternative ETF Approaches

If we still want ETF exposure, try different strategies:

Option 1: Trend Following (not mean reversion) - Buy when ETF above 50-day/200-day MA - Sell when below MA - Ride long-term trends instead of short-term oscillations

Option 2: Leveraged ETFs (3x volatility) - TQQQ (3x QQQ), SOXL (3x semiconductors) - 3x leverage = 3x volatility = more signals - Warning: Higher risk, larger drawdowns

Option 3: Sector Rotation - Rotate between sector ETFs (XLK tech, XLE energy, XLF finance) - Buy strongest sector, sell weakest - Monthly/quarterly rebalancing

Option 4: Options on ETFs - Sell covered calls on QQQ/VGT (theta decay) - Buy protective puts (hedging) - More complex but generates income in sideways markets

📊 Capital Allocation Update

Original Plan (before ETF results)

Total Capital: $99,000 (Alpaca Paper Account) - Individual Stocks: 50% ($49,500) - ETFs/Indexes: 40% ($39,600) - Cash Reserve: 10% ($9,900)

Revised Plan (after ETF failure)

Total Capital: $99,000 (Alpaca Paper Account) - Individual Stocks: 80% ($79,200) ← Increased - Mean Reversion - Volatile Stocks: 35% ($34,650) - Micro-Cap Momentum - 4 Symbols: 25% ($24,750) - Micro-Cap Momentum - 12 Symbols: 20% ($19,800) - Cash Reserve: 20% ($19,800) ← Increased

Rationale: - Focus 80% on proven winners (individual stock strategies) - Increase cash reserve to 20% (from 10%) for opportunities - Eliminate ETF allocation until better strategy developed

ETF Optimization Reports: - reports/etf_mean_reversion_conservative.json (Experiment 1) - reports/etf_momentum.json (Experiment 2) - reports/arkk_volatility.json (Experiment 3)

ETF Pareto Plots: - reports/pareto_etf_mean_rev.png - reports/pareto_etf_momentum.png - reports/pareto_arkk.png

Individual Stock Success Reports (for comparison): - reports/mean_reversion_volatile_stocks.json (502% return, 1.08 Sharpe) ⭐ - reports/micro_cap_momentum_optimized.json (199% return, 0.42 Sharpe) - reports/micro_cap_momentum_12symbols.json (96% return, 0.19 Sharpe)

Documentation: - docs/trading/ETF_INDEX_STRATEGY_PLAN_2025-11-01.md (original plan) - docs/status/OPTIMIZATION_ANALYSIS_2025-11-01.md (individual stock results)

Implementation Files: - tools/data_cache.py (ETF data caching, 277 lines) - tools/mean_reversion_optimized.py (ETF parameter support added) - tools/optimize_strategy.py (--asset-type etf flag added)

✅ Next Steps

Immediate (Today - Nov 1)

  • [x] Complete 3 ETF optimization experiments
  • [x] Document ETF failure analysis (this file)
  • [ ] Update ETF strategy plan with failure results
  • [ ] Focus on deploying individual stock strategies

Week 2 (Nov 2-8)

  • [ ] Deploy Mean Reversion - Volatile Stocks to paper trading (primary)
  • [ ] Deploy Micro-Cap Momentum to paper trading (secondary)
  • [ ] Monitor daily performance vs. backtests
  • [ ] Track actual vs. expected trade frequency

Week 3 (Nov 9-15)

  • [ ] Analyze 1-week paper trading results
  • [ ] Decide: deploy to live or adjust
  • [ ] Research alternative ETF strategies (trend following, leveraged ETFs)

Future (If ETF Exposure Desired)

  • [ ] Design trend-following strategy for QQQ/VGT
  • [ ] Test leveraged ETFs (TQQQ, SOXL) with mean reversion
  • [ ] Explore options strategies on ETFs (covered calls)
  • [ ] Consider sector rotation approach

Prepared by: Claude Code Date: 2025-11-01 Status: ❌ All ETF experiments failed validation Decision: Focus on individual stock strategies (proven winners) Next Step: Deploy volatile stocks mean reversion to paper trading