Investment thesis
Portfolio optimisation — economic intuition. Given a universe of imperfectly-correlated return streams, convex optimisation (Markowitz, risk-parity, recursive decay) produces weights that dominate naive equal-weight on a risk-adjusted basis. The edge is not in alpha discovery but in the disciplined combination of existing signals — consistent with institutional multi-manager allocation.
Risk-adjusted performance — live track record
Forward-tested daily against live market data. Metrics derived from end-of-day portfolio marks; methodology documented on the Due Diligence and About pages.
| Return | Value | Risk-adjusted | Value | |
|---|---|---|---|---|
| Current portfolio worth | $11515.70 | Sharpe ratio | 8.78 | |
| Total return | 15.16% | Sortino ratio | 70.78 | |
| CAGR | 199.44% | Calmar ratio | 267.55 | |
| Volatility (annualised) | 16.39% | Profit factor | 9.37 | |
| Days live | 26 | Maximum drawdown | -0.75% |
Process consistency
| Positive months | 100.0% |
| Best month | 8.17% |
| Worst month | 5.94% |
| Recovery from max drawdown | 1 days |
Equity curve
Live track record — forward-tested performance from the strategy's production start date.
Drawdown profile
Underwater curve — percentage below the running high-water mark. Institutional allocators read this before the equity curve.
Current holdings
| Symbol | Quantity |
|---|---|
| TQQQ | 63.7356 |
| USD | 6653.9466 |
Research & documentation
- Strategy deep-dive: SynthesizedHyperConvexityBot: strategy deep-dive & live performance
- Methodology write-up: SynthesizedHyperConvexityBot
- Reference implementation:
tradingbot/synthesizedhyperconvexitybot.py - Framework: python_tradingbot_framework (open source, fully inspectable)
Related strategies
Other strategies in the Portfolio optimisation family:
- SharpePortfolioOptWeeklyBot · research note- RecursiveDecayHarvestBot · research note Or view the full strategy roster.
For professional investors
Request the investor deck, DDQ, and extended analytics. Firm-gated and reviewed manually.
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