Systematic Trading Strategies for Institutional Capital
JTM SpreadHunters | 2022–2026 | 50 Months
@SpreadHunters 1 / 9Two uncorrelated strategies combined into a single portfolio
This portfolio combines two systematically different risk premium sources: a mean-reversion engine and a trend-following engine. Their near-zero correlation produces a blended return profile with significantly reduced drawdowns while maintaining strong absolute returns.
5-year backtest on BTC and SOL perpetual futures | 30/70 capital allocation
DGT — Dynamic Grid Trading System
The system deploys capital in a grid structure around a calculated fair value. As price deviates from the mean, positions accumulate. Profit is captured when price reverts.
Position sizing follows a geometric progression, allowing larger exposure at statistically favorable price levels.
Edge: Statistical volatility modeling combined with overfitting prevention. All parameters validated through stress testing against historical flash crashes. Mean reversion exploits crypto's natural oscillation — prices consistently snap back to equilibrium, generating systematic profit from volatility that directional strategies cannot capture.
APEX — Volatility Adjusted Tiered Strategies
The system accepts small, defined losses while positioning for directional moves. When trends emerge, positions are held to capture extended price movements.
A single successful trend typically recovers multiple small losses, generating positive expectancy despite a low win rate.
Dual Engine Architecture: Two independent sub-strategies operate on different time horizons. One captures macro swings; the other targets shorter momentum bursts. Contribution is balanced, providing internal diversification.
Complementary return profiles reduce aggregate risk
Ranging, consolidating markets favor mean reversion. Repeated oscillations around fair value generate consistent returns.
During these periods, trend following experiences controlled losses as directional signals fail to materialize.
Strong directional moves favor trend following. Extended price runs generate outsized returns on positioned capital.
During these periods, mean reversion draws down as price moves away from historical anchors.
| Metric | DGT (Mean Reversion) | APEX (Trend Following) | Blended Portfolio |
|---|---|---|---|
| Max Drawdown | 23.8% | 16.0% | 11.8% |
| Sharpe Ratio | 1.14 | 2.52 | 2.79 |
| Calmar Ratio | 1.25 | 3.97 | 4.64 |
5-year backtest results | 2022–2026
Note: Blended portfolio achieves significant drawdown reduction while maintaining comparable returns. Risk-adjusted metrics improve significantly through systematic uncorrelation.
@SpreadHunters 6 / 9Monthly returns analysis
The blended portfolio exhibits a right-skewed (positive) fat tail distribution. This is the preferred profile for systematic strategies:
| Profitable Months | 39 / 50 (78%) |
| Average Monthly Return | +3.8% |
| Best Month | +17.9% |
| Worst Month | -7.0% |
| Max Recovery Period | 120 days |
| Avg Yearly Drawdown | 7.5% |
January 2026 Results
Challenging month due to sustained BTC trend. Losses contained within expected parameters.
Strong performance capturing SOL momentum. 55 trades executed with 29% win rate.
January 2026 validates the portfolio thesis: while mean reversion struggled with trending conditions, trend following captured the directional move. Estimated blended return: +3.6% (30/70 allocation).
Quantitative risk management architecture
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