📖 How to Read This Leaderboard
This compares WhiteWolf v1 (original: 7 sub-strategies with vol-targeting and SQQQ crash sprints),
WhiteWolf v2 (production ✅: vol-adaptive weights, ATR entries/exits, trailing stops),
and WhiteWolf v3 (composite-gated: regime detection via composite score thresholds +0.25/-0.15)
against 18 standard research strategies (SMA crossovers, MACD, RSI, Bollinger, etc.).
All strategies are tested on the same period: loading....
This is a long-only TQQQ comparison — research strategies trade TQQQ only,
while WhiteWolf also uses SQQQ (short sprints) and cash (vol-targeting), giving it a structural edge in bear markets.
Key metrics:
CAGR = compound annual growth rate,
Max DD = worst peak-to-trough drop (how much pain),
Sharpe = return per unit of risk (>1 is good),
Calmar = CAGR ÷ Max DD (higher = better risk/reward),
PF = profit factor (gross wins ÷ gross losses),
$100K → = what $100K would have become.
Master Leaderboard — All Strategies Ranked
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| # | Strategy | CAGR | Max DD |
Sharpe | Calmar | PF | Trades |
Win% | $100K → |
CAGR vs Max Drawdown — Risk/Return Map
WhiteWolf v1 vs v2 vs v3
3-instrument simulation (TQQQ/SQQQ/BIL) across multiple time periods. All starting with $100K.
v1: Original (7 sub-strategies + vol-targeting + SQQQ sprints) ·
v2: Production ✅ (vol-adaptive weights + ATR stops + RSI filter + vol sizing) ·
v3: Composite-gated (regime detection: bull >+0.25, bear <-0.15, else BIL)
CAGR (Annual Return)
Sharpe Ratio (Risk-Adjusted Return)
Max Drawdown (Worst Peak-to-Trough)
Final Portfolio Value ($100K Start)
Calmar Ratio (Return / Max Drawdown)
Equity Curves — Select Period
Summary
v1 — Original
7 sub-strategies + volatility-targeted combiner + SQQQ sprint. Highest raw returns in bull markets (101.9% CAGR in 2023-24) but devastating in bears (-42.1% in 2022, -52% max drawdown). 15yr: $100K → $11.9M. 6yr: $100K → $951K. Not suitable for real money — drawdowns too severe.
v2 — Production ✅ (Live on Alpaca)
Vol-adaptive weighting, ATR stops, RSI filter, vol-based sizing. Best risk-adjusted performer. Turned the 2022 bear into just -1.7% (vs v1's -42.1%). Highest Sharpe (1.061) and Calmar (1.349) over recent 6yr window. COVID: +124.7% with only 27% drawdown (Sharpe 2.06). 6yr: $100K → $1.04M.
v3 — Composite-Gated
Regime detection via composite score thresholds (bull >+0.25, bear <-0.15, else 100% BIL). Cleanest design but too conservative — sits in cash too often, missing rallies. 2022 bear: -22.7% (better than v1, worse than v2). 6yr: $100K → $757K. Fewest trades (357 over 15yr). Needs threshold tuning before competitive.
Apples-to-Apples: 3.5 Year Comparison (Jul 2022 – Feb 2026)
All strategies backtested over the same period as WhiteWolf. The production strategy is highlighted.
| # | Strategy | CAGR | Max DD | Sharpe | Calmar | PF | Trades | Win% | $100K → |
|---|
💰 Investment Calculator
Simulate any contribution schedule against the WhiteWolf v2 production engine.
📖 How This Works
Strategy: This uses the WhiteWolf v2 production code — 7 sub-strategies
(Primary Trend, Intermediate Trend, Short-Term Trend, Trend Strength, Momentum Velocity,
Mean Reversion, Volatility Regime) combined through a vol-adaptive allocator with ATR stops, RSI filter, and SQQQ crash sprints.
Each day the engine decides: allocate X% to TQQQ (3x leveraged Nasdaq), Y% to SQQQ (3x inverse Nasdaq),
and Z% to cash (earning ~4% annual money market rate).
Data: The engine was run on every trading day from 2011 to 2026 (3,731 days)
using real NDX/TQQQ/SQQQ prices. Daily allocations are cached — no approximations or simplified models.
Simulation: When you hit "Run Simulation," it picks 500 random time windows
from that 15-year history (random start date, random duration matching your horizon). For each window,
your contributions buy into the strategy at the current portfolio NAV — like dollar-cost averaging into a fund.
Some windows start before COVID, some during the 2022 bear, some in bull runs — you see the full range of outcomes.
Results: P5 = worst 5% of outcomes (bad luck timing), Median = middle outcome,
P95 = best 5%. P(2x) = probability your portfolio doubles vs what you invested.
All returns include the effect of TQQQ's leveraged decay, which the strategy mitigates by going to cash in high-volatility environments.
Instruments: TQQQ (Nasdaq-100 3x leveraged ETF), SQQQ (Nasdaq-100 3x inverse ETF), Cash/BIL (T-bill proxy).
Results
| Horizon | Invested | Worst (P5) | Conservative |
Median | Optimistic | Best (P95) | P(+) | P(2x) | P(10x) |
🎲 Monte Carlo Analysis — Historical Slice Sampling
500 random time windows from real TQQQ history (2010–2026). Random start dates, random durations (1–12 years).
Every strategy tested on identical real market data — no synthetic paths, no bootstrap artifacts.
Duration Bucket
| # | Strategy | Score | Med CAGR | P5–P95 |
Med DD | Sharpe | P(+) | P(>20%) | Category |
CAGR Distribution by Strategy
Risk vs Return — Monte Carlo
Strategy Encyclopedia
Every strategy in the system explained — what it does, how it works, when it shines, and its actual backtest results. Click "View Backtest →" to drill into the numbers.
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Glossary of Terms
Key metrics, concepts, and terminology used throughout the WolfSignal — WhiteWolf Dashboard.
📊 Performance Metrics
CAGR Compound Annual Growth Rate
The annualized rate of return, smoothing out volatility. A strategy returning $100K → $200K over 3 years has ~26% CAGR. Higher is better, but always weigh against drawdown risk.
Max Drawdown (Max DD)
The largest peak-to-trough decline during the test period. A 50% drawdown means at some point your portfolio lost half its value before recovering. For leveraged ETFs like TQQQ, 40-60% drawdowns are common. Below 30% is excellent; above 60% is brutal.
Sharpe Ratio
Risk-adjusted return: how much excess return you earn per unit of volatility. Calculated as (strategy return − risk-free rate) / standard deviation. <0.5 = poor, 0.5–1.0 = acceptable, 1.0–2.0 = good, >2.0 = excellent.
Sortino Ratio
Like Sharpe, but only penalizes downside volatility (not upside). More useful for strategies with asymmetric returns. Generally preferred over Sharpe for real-world evaluation.
Calmar Ratio
CAGR divided by Max Drawdown. Measures return per unit of worst-case pain. A Calmar of 1.0 means your annual return equals your worst drawdown. >0.5 is the minimum threshold; >1.0 is strong.
Profit Factor (PF)
Total gross profits divided by total gross losses. PF of 2.0 means you make $2 for every $1 you lose. <1.0 = losing money, 1.0–1.5 = marginal, 1.5–2.0 = good, >2.0 = strong edge.
Win Rate
Percentage of trades that were profitable. A 60% win rate means 6 out of 10 trades made money. Note: win rate alone is misleading — a 30% win rate strategy can be highly profitable if winners are much larger than losers.
Composite Score
WhiteWolf's proprietary ranking metric combining: Sharpe (25%), Calmar (25%), Max Drawdown (20%), Profit Factor (15%), and Trade Frequency (15%). Normalized 0–1. >0.7 = production-ready, 0.4–0.7 = promising, <0.4 = needs work.
📈 Strategy Types
Trend Following
Strategies that identify and ride sustained price movements. They buy when price is rising and sell (or short) when falling. Typically use moving averages, breakouts, or momentum indicators. Works well in trending markets, struggles in sideways chop.
Mean Reversion
Strategies that bet prices will return to their average after extreme moves. Buy oversold conditions, sell overbought. Uses RSI, Bollinger Bands, or z-scores. Opposite of trend following — works in range-bound markets.
Momentum
Buy assets that have been going up, avoid those going down. Based on the empirical observation that recent winners tend to keep winning in the short-to-medium term. Dual momentum compares both absolute returns and relative strength.
Volatility-Based
Strategies that trade based on market volatility levels (VIX, ATR, Bollinger Band width). May go to cash in high-volatility regimes or increase position size in low-volatility environments.
🔧 Technical Indicators
SMA Simple Moving Average
The average closing price over N days. SMA(50) = average of last 50 days. Used to smooth noise and identify trend direction. When a faster SMA crosses above a slower one (e.g., SMA20 over SMA50), it's a bullish "crossover" signal.
EMA Exponential Moving Average
Like SMA but gives more weight to recent prices, making it faster to react. EMA(9) responds quicker than SMA(9). Preferred for short-term trading. The 9/21 EMA cross is a popular fast signal.
RSI Relative Strength Index
Oscillator ranging 0–100 measuring speed of price changes. >70 = overbought (potential sell), <30 = oversold (potential buy). Standard period is 14 days. Often used for mean reversion entries.
MACD Moving Average Convergence Divergence
Difference between EMA(12) and EMA(26), with a signal line (EMA(9) of MACD). Crossovers of MACD over its signal line generate buy/sell signals. Histogram shows momentum strength. Lagging but reliable.
ATR Average True Range
Measures daily price volatility (range between high and low, adjusted for gaps). Used for position sizing and stop-loss placement. ATR breakout strategies enter when price moves more than N × ATR from a reference point.
Bollinger Bands
SMA(20) with bands at ±2 standard deviations. Price touching the upper band suggests overbought; lower band suggests oversold. Band width measures volatility — squeeze (narrow bands) often precedes a big move.
Golden Cross / Death Cross
Golden Cross: SMA(50) crosses above SMA(200) — strong bullish signal. Death Cross: SMA(50) crosses below SMA(200) — bearish. These are slow but historically reliable for major trend changes.
🏦 Instruments
TQQQ 3× Bull
ProShares UltraPro QQQ. Delivers 3× the daily return of the Nasdaq-100 (QQQ). If QQQ goes up 1%, TQQQ goes up ~3%. Extremely volatile — can gain or lose 10%+ in a single day. Subject to volatility decay over time.
SQQQ 3× Bear
ProShares UltraPro Short QQQ. Delivers 3× the inverse daily return of QQQ. If QQQ drops 1%, SQQQ gains ~3%. Used as a hedge or for bearish bets. Long-term holding is destructive due to volatility decay + upward market bias.
QQQ
Invesco QQQ ETF tracking the Nasdaq-100 index — the 100 largest non-financial companies on Nasdaq. Heavy tech weighting (Apple, Microsoft, Nvidia, etc.). The "benchmark" for WhiteWolf strategies.
NDX Nasdaq-100 Index
The underlying index that QQQ tracks. Not directly tradeable — used for analysis and signal generation. Has the longest history of the four instruments in our data cache.
BIL Treasury Bills
SPDR Bloomberg 1-3 Month T-Bill ETF. Essentially "cash" — earns the risk-free rate (~5% in 2024-2026). WhiteWolf parks capital here when out of TQQQ/SQQQ positions to earn yield instead of sitting idle.
Volatility Decay
The erosion of value in leveraged ETFs over time due to daily rebalancing. If QQQ goes +5% then -5%, it's down 0.25%. TQQQ goes +15% then -15%, ending down 2.25%. This is why leveraged ETFs need active management — buy-and-hold guarantees decay.
⚙️ System Concepts
Backtesting
Running a strategy against historical data to see how it would have performed. Essential for validation but can be misleading — past performance doesn't guarantee future results. Watch for overfitting (curve-fitting to historical data).
Paper Trading
Simulated live trading using real market data but fake money. WhiteWolf runs in paper mode via Alpaca's paper trading API ($100K virtual account). Used to validate strategy behavior in real-time before committing real capital.
VectorBT
High-performance Python backtesting library using vectorized operations (NumPy/Pandas). Much faster than event-driven frameworks like Backtrader. WhiteWolf uses Portfolio.from_signals() for rapid strategy evaluation.
Pine Script
TradingView's proprietary scripting language for technical indicators and strategies. The research pipeline discovers Pine Script strategies online, converts them to Python, and backtests them locally via VectorBT.
Overfitting / Curve Fitting
When a strategy is tuned so perfectly to historical data that it fails on new data. Signs: extremely high backtest returns, too many parameters, unrealistic trade counts. Guard against it with out-of-sample testing and parameter robustness checks.
Walk-Forward Analysis
Optimization technique where you train on a window of data, test on the next period, then roll forward. Simulates real-world strategy development more honestly than a single backtest. Helps detect overfitting.
Regime Detection
Identifying whether the market is in a bull, bear, or sideways phase. Different strategies work in different regimes. WhiteWolf's core edge is switching between TQQQ (bull), SQQQ (bear), and BIL (sideways) based on regime signals.
Trade Frequency
How often the strategy trades, measured in trades per year. Too few (<10/year) = not statistically significant. Too many (>200/year) = high transaction costs and slippage. Sweet spot is 10–200 trades/year for daily strategies.