About StockVisionz
STOCKVISIONS | support@stockvisionz.com
What Is StockVisionz?
StockVisionz is a user-driven algorithmic trading research platform built for US equity markets. It combines traditional rule-based backtesting with a full suite of machine learning models, giving traders and researchers the tools to explore, test, and compare strategies on any US equity ticker — all from a single dashboard.
The platform is designed to serve two audiences: technical users who want full control over ML model configurations and experiment reproducibility, and non-technical retail traders who want clear, visual insights without needing to write code.
The Core Interaction Model
The platform is built around a simple but powerful interaction model: you search any US equity ticker, and StockVisionz handles the rest. If the ticker is not yet in the database, the platform automatically fetches historical price data, computes technical indicators, and makes the ticker ready for analysis. You then trigger backtests or ML model runs on demand, with full results persisted and comparable across sessions.
What You Can Do
- Run on-demand backtests against rule-based strategies (SMA crossover, mean reversion, momentum) for any US equity ticker
- Train and compare machine learning models including Logistic Regression, XGBoost, Random Forest, LSTM, and Reinforcement Learning agents (DQN, PPO, A2C)
- Evaluate models using walk-forward validation to approximate real-world deployment conditions
- Apply meta-labeling to use ML as a dynamic signal filter on top of rule-based strategies
- Track experiment reproducibility with full configuration logging, versioned model weights, and side-by-side comparison views
- View full performance tearsheets including equity curves, Sharpe ratio, CAGR, max drawdown, and monthly return heatmaps
The Technology
- Time-series database: PostgreSQL with TimescaleDB for optimized historical data storage and ML feature extraction
- Backtesting: vectorbt for vectorized, high-performance strategy evaluation
- Machine learning: scikit-learn, XGBoost (GPU-accelerated), PyTorch, and stable-baselines3
- Data pipeline: vendor-agnostic adapter pattern enabling clean provider swaps without pipeline changes
- Frontend: Next.js with TypeScript, Tailwind CSS, and Recharts for a responsive, modern dashboard
Built With Rigor
StockVisionz is built with a baseline-first ML discipline: simpler models run first, and more complex models must demonstrably outperform them to justify the added complexity. Walk-forward validation is non-negotiable across all model families. Data leakage prevention is enforced through a multi-check validation layer. Backtest results are clearly labeled with their limitations, including survivorship bias disclosures tied to the underlying data source.
The goal is not to promise alpha. The goal is to give you the infrastructure to rigorously evaluate whether any strategy or model actually has it.
Who Built This?
StockVisionz is developed and operated by STOCKVISIONS. For questions, feedback, or support, reach us at support@stockvisionz.com.
StockVisionz is a research platform. Nothing on this platform constitutes investment advice. See our full Legal Disclaimer for details.