QuanterLab is a multi-module trading research platform. It combines indicator-based technical analysis, fundamental screening, machine learning, portfolio construction, and live paper trading into one connected system. This guide introduces each major area and shows you how to navigate between them.
The Dashboard
After logging in you land on the Home Dashboard. The top row shows the three primary research pillars:
- Indicator Strategies — Technical analysis using price-based indicators (RSI, MACD, Bollinger Bands, ADX, etc.) organized into four strategy families: Mean Reversion, Momentum, Crossover, and Breakout
- Fundamental Analysis — Financial statement analysis, multi-factor screening, portfolio optimization, and risk modeling
- ML Strategies — Train machine learning models (Random Forest, XGBoost, LightGBM, CatBoost) on market data to generate buy/sell predictions
Below that, the Execution row contains:
- Indicator Paper Trading — Deploy saved strategy configs to run in real-time with virtual capital (Classic for Strategy Builder configs, Advanced for Overlay/Builder configs)
- Portfolio Visualizer — Build and track real portfolios with up to 20 positions per tab, including buy/sell/short actions and live P&L
- ML Paper Trading — Forward-test trained ML models with live market data
The typical workflow moves left to right: Research (scanners and indicators) → Build (strategy or model) → Test (backtest or walk-forward) → Execute (paper trading). My Projects ties it all together.
Navigation: The Ribbon and Sidebar
QuanterLab has two navigation systems that work together:
The Control Panel Ribbon
The ribbon bar at the top of every page has four tabs:
- Strategies — Quick-launch links to every indicator strategy module. Dropdown menus are grouped by strategy family (Mean Reversion, Momentum, Crossover, Breakout) with sub-items for Scanners, Signal Overlays, and Strategy Builders. The Advanced section contains the universal Overlay/Builder tool and the Portfolio Backtester
- Fundamental — Links to the multi-factor screener, portfolio optimization tools (MVO, HRP, Inverse Volatility), risk modules (Monte Carlo, VaR), and all deep-dive research units
- Execution — Direct links to Paper Trading modules and saved strategy configs. This is where you deploy and monitor live strategies
- Knowledge Base — Educational articles organized by topic (you are reading one now). Covers trading theory, indicator guides, and platform workflows
The Sidebar
The left sidebar shows your live activity. Running paper trading instances appear with their current status (SCANNING, LONG, SHORT, P&L). Portfolio Visualizer shows asset counts and total value. ML Trader shows active model instances. At the bottom you will find a Research Library link and your account information.
Every module page has a Read Me button in the top-right corner. Click it for module-specific documentation explaining exactly what the module does and how to use it step by step.
The Five Major Areas
1. Indicator Strategies
Four strategy families, each with three tools: a Scanner (find candidates across global indexes), a Signal Overlay (visualize indicators on a price chart with 30% data holdout), and a Strategy Builder (configure entry/exit conditions and backtest). The Advanced section adds the Overlay/Builder (50+ indicators, entry + confirmation + exit layers, in-sample/out-of-sample testing) and the Portfolio Backtester (test up to 20 saved configs together with shared capital).
2. Fundamental Analysis
A full fundamental research suite. Start with the Multi-Factor Screener to rank stocks by Value, Quality, Momentum, and Growth across global indexes. Use Portfolio Optimization (MVO, HRP, Inverse Volatility) to determine allocation weights. Evaluate risk with Monte Carlo Simulation and Value at Risk. Dive deeper with eight Research Units covering profitability, cash flow, earnings, capital allocation, Piotroski F-Score, valuation multiples, intrinsic value, and macroeconomic overlay.
3. ML Strategies
Train classification or regression models on any ticker. Four algorithms are available (Random Forest, XGBoost, LightGBM, CatBoost). The ML Training module handles feature engineering, train/test splitting, hyperparameter configuration, and walk-forward validation. Trained models can be deployed to ML Paper Trading for live forward-testing.
4. Paper Trading
Two indicator paper trading modules: Classic (for Strategy Builder configs — imports from the ribbon, scans in real time, and auto-executes trades with virtual capital) and Advanced (for Overlay/Builder configs — supports all 50+ indicators with entry, confirmation, and exit layers). ML Paper Trading runs trained models against live data. All instances appear in the sidebar with live status.
5. My Projects
The organizational hub. Create projects to group related strategy configs, portfolios, and ML models together. Each project gets a timeline for planning research phases, a notes editor for trade evaluation, and a report attachment system. Items are dragged in from the right sidebar which shows all your saved trading configs, portfolios, and trained models.
Suggested First Steps
- Explore a Scanner — Go to Indicator Strategies → Mean Reversion → Scanner and run the S&P 500 scan to see stocks ranked by statistical mean reversion potential
- Try a Signal Overlay — Pick a stock from the scanner results, open the Signal Overlay, and experiment with RSI or Bollinger Bands on the price chart
- Build your first strategy — Open the Strategy Builder or Overlay/Builder, set an entry indicator, add an exit condition, and run a backtest
- Save and deploy — Save your config from the backtest results, then open Paper Trading from the Execution ribbon to deploy it with virtual capital
- Screen fundamentals — Switch to Fundamental Analysis and run the Multi-Factor Screener on the S&P 500 to see how stocks rank on financial quality metrics