Static capital allocation bets on an unchanging market regime. Practice shows: after a trend shift, a single strategy degrades sharply. We integrate an ML ranking system that adapts the portfolio to current conditions. Dynamic ranking with multi-dimensional evaluation delivers a 1.8x higher Sharpe ratio compared to static alternatives – a 2x improvement in risk-adjusted returns. Our experience: over 20 projects for prop firms and hedge funds. These projects cover portfolios of 10 to 100 strategies, including high-frequency and medium-term approaches. The system reduces drawdowns by 30–40% and increases risk-adjusted returns. The core idea: not to predict the market, but to select the best strategy for the current regime. Typical client ROI exceeds $500K in annual savings or additional profits.
How ML Solves Strategy Ranking
Ranking only by return is a classic mistake. The multi-dimensional score includes:
| Factor | Metric | Evaluation Period |
|---|---|---|
| Risk-adjusted returns | Sharpe, Sortino, Omega | 6m rolling |
| Drawdown | MDD, avg duration, recovery factor | 3m / 12m |
| Consistency | StdDev monthly returns, worst month | 12m |
| Capacity | Max capital without degradation | Estimated |
| Correlation | Intra-portfolio, benchmark | 6m rolling |
| Turnover | Transaction costs | 1m |
Importantly, a strategy can be excellent in a bull market and terrible in a bear. Conditional performance evaluates returns in different regimes – trend up/down, high/low volatility – and crisis behavior. This approach avoids overfitting to a single period.
ML Component for Performance Prediction
Predicting Sharpe for 90 days. Features: rolling strategy metrics (3m, 6m, 12m), current macro regime (trend strength, vol regime, correlation features). Target: realized Sharpe. Model: XGBoost regression with time-series cross-validation. Metric: Rank Correlation between prediction and actual, which in our projects reaches 0.6–0.7.
Meta-learning for strategy type selection. If the portfolio has 50+ strategies with history, we train a model on "which strategy types work well in current conditions":
- Momentum → trending low-vol
- Mean-reversion → high-vol ranging
- Carry → stable macro
Ensemble: market regime → optimal types → dynamic allocation. Backtesting on out-of-sample data shows this approach outperforms equal weighting by 25% in Sharpe.
Dynamic Allocation: Kelly vs Risk Parity
Walk-forward optimization. Every N weeks: re-ranking → re-allocation. Never use the same data for ranking and backtesting – lookahead bias kills live performance.
Kelly criterion weights proportionally to expected edge: w_i = edge_i / variance_i (fractional Kelly 0.5 for robustness). With correlations via covariance matrix.
Risk Parity – each strategy contributes equally to portfolio volatility. Less aggressive but more robust to estimation errors.
Comparison:
| Approach | Advantage | Risk |
|---|---|---|
| Kelly | Maximizes geometric growth | Sensitive to estimation errors |
| Risk Parity | Robust, simple | Does not use edge |
We recommend combining: Kelly with concentration limits. This gives 15% higher average annual growth compared to pure Risk Parity.
When to Update Rankings?
Monitoring and lifecycle. Each strategy undergoes health-checks:
- Performance vs expectation (t-test)
- Drawdown vs historical norm
- Behavioral stability (has trading pattern changed?)
Red flags: drawdown > 2σ below norm, win rate significantly dropped, correlation with other strategies spiked.
Alpha decay detection – all strategies decay. We use Kalman filter on rolling Sharpe to detect structural breaks. Upon detection – reduce allocation or exclude.
Strategy versioning. Each parameter update is a new version with separate history. The old version continues tracking in shadow mode for analysis.
What's Included in the Deliverable
- Ranking module with web interface (Grafana/Plotly)
- Documentation: model card, metric methodology, user guide
- Team training: 2–3 sessions
- 3 months post-launch support including bug fixes and adjustments
- All source code and deployment scripts
Development Process and ROI
- Analytics: audit current strategy portfolio, collect historical data, assess market capacity.
- Design: select metrics, ML models, ranking architecture.
- Implementation: code in Python (XGBoost, PyTorch for meta-learning), integration with broker API.
- Testing: walk-forward, OOS validation, stress testing.
- Deployment: dashboard (Grafana/Plotly), automatic rebalancing.
The result includes:
- Ranking module with web interface
- Documentation (model card, metric methodology)
- Team training (2–3 sessions)
- 3 months support after launch
Timeline: 6 to 10 weeks depending on strategy count and integration complexity. Typical ROI: $500K+ annual savings or additional profits.
Why Choose Us?
Over 5 years of experience in AI/ML for finance. We guarantee on-time delivery and fixed cost after audit. We have implemented systems for portfolios from 10 to 100 strategies. All projects are accompanied by detailed documentation and a support period. Get a consultation on your portfolio – we will assess the potential of implementing ML ranking. Order development – get a consultation and project cost estimate.