Solution · AI-Powered QIS
AI-Powered QIS Index Platform
Custom, factor-based Quantitative Investment Strategy indices -- engineered with multi-factor models, deep learning, and regime-aware AI, and benchmarked continuously after they go live.
Multi-Factor
Value, momentum, quality & more
Regime-Aware
AI that adapts to markets
Backtested
Evidence before deployment
Institutional
Built for institutional buyers
What it includes
Factor Discovery & Signal Generation
Finding what actually predicts returns
Multi-factor models sift through market data to surface signals with genuine predictive power, combined with deep learning approaches for patterns that classical factor models miss.
Backtesting & Index Construction
Proving it before it goes live
Every candidate strategy is rigorously backtested across market regimes before construction into an index, so performance claims are grounded in historical evidence, not hope.
Institutional Deployment & Benchmarking
Live, and held to account
Once deployed, every index is continuously benchmarked against its stated objective, with regime-aware AI models adapting as market conditions shift rather than drifting silently out of date.
Not A Mockup
See factor discovery run, live
24 real large-cap U.S. equities, 5 years of actual daily price history, three classic factors computed from scratch below — and their real, measured predictive power. No fitted curve, no cherry-picked window.
Launch the live engine →Latest cross-section
Methodology: momentum is the standard academic 12-month-return-excluding-most-recent-month definition; low-volatility is the negative of trailing 6-month realized monthly-return volatility; reversal is the negative of trailing 1-month return. Signal quality is the cross-sectional Spearman rank Information Coefficient against each stock's real following-month return, averaged across every rolling month in the sample. An IC near zero or negative isn't hidden here — it's the honest result for that factor over this universe and period.
What's actually running behind this page
The pipeline, on demand
- 1. Fetch — pulls 5 years of real daily prices for 24 large-cap U.S. equities directly from Yahoo Finance, live, when you ask it to.
- 2. Compute — derives 12–1 momentum, trailing 6-month low-volatility, and 1-month reversal for every stock at every month-end, from scratch, every run.
- 3. Measure — scores each factor's real predictive power with the cross-sectional Spearman rank Information Coefficient against next-month returns.
- 4. Report — averages IC and hit rate across every rolling month in the sample and reports it plainly, including when a factor's signal is weak or negative.
Then: build your own index
Past the factor readout, the live engine lets you set your own weighting across momentum, low-volatility, and reversal, then genuinely backtests a top-8 portfolio built from that blend against the same 5-year history — benchmarked against simply holding the full 24-stock universe equal-weighted. CAGR, annualized volatility, Sharpe ratio, and max drawdown come back for both, side by side.
This is the "Backtesting & Index Construction" step described above, made interactive — so you can see how a factor-weighting choice actually moves the numbers before anything is proposed as a real mandate.
Opens in a new tab — runs on TeleCanor's own quant infrastructure, not a mockup.
Capabilities
Multi-Factor Models
Value, momentum, quality, and volatility factors combined systematically rather than picked ad hoc.
Regime-Aware AI
Models that recognize when market conditions have shifted and adjust rather than keep extrapolating.
Transparent Methodology
Documented, explainable construction rules -- institutional buyers can see how the index is built, not just what it returns.
Continuous Benchmarking
Live performance tracked against its stated objective on an ongoing basis, not just at launch.
Considering a custom QIS index?
Let's talk about what a factor-based, AI-engineered index could look like for your strategy.