This website showcases how AI and Quant can deliver exceptional results in active asset management by overperforming the benchmark.
Yes, using pyramiding techniques to allocate asset weights. Real-money results are published on LinkedIn.
It ranges from 4 days to 5 weeks on average.
Possible, but entry and exit are different based on your risk appetite as detailed in User Guide.
TensorFlow (AI framework), PostgreSQL (database), Railway (deployment platform), Supabase (backend-as-a-service), and DeepSeek (LLM for news extraction).
PhD in Mathematics, Financial Risk Manager (FRM) certification, Chartered Market Technician (CMT) Level 1, and TensorFlow developer certificate.
The AI model is trained on historical price movements and market-switching regimes to predict future stock movement, determining if it is above or below the money market threshold. The AI model returns a probability that is plugged into another model to generate the signal measure.
The Quant uses advanced mathematical and noise filtration techniques to produce an output that is passed to another model, along with the AI model output, to produce the signal line.
The signal is a number ranging from -100 to +100 that describes the stock movement as: Trending Up, Slowing Down, Trending Down, or Recovering.
Monitor the signal line and consider buying when the signal becomes positive if you are risk-averse. If you are willing to take higher risk, you may buy the stock when it shows a Recovery signal.
Only market data (OHLCV) is used, as no other data is available.
As we use only market data (OHLCV), models are trained to project one week ahead. If fundamental data (Earnings Reports) and alternative data are included, projections could extend to around 3 months and beyond.
Initial analysis shows promising results for applying AI to analyze company reports, but it is not included, as it requires more storage and processing power, which is outside the allocated budget for launching this website.
Because data for other markets is either expensive to access, not in a standard format, or of poor quality, requiring extensive cleansing.
Because servers are not free!