Case study 02
The Nexalyze
AI-powered trading analytics and automation
Portfolio index: AI-powered trading automation.
TheNexalyze scans charts, detects patterns in live market data, and turns those signals into automated trade setups. Signal automation, a backtesting engine, and risk management sit in one system so a strategy can be tested before it runs live.
I · The obstacle
Charts move faster than a manual review can follow
II · The action
Chart scanning that detects patterns in live market data
III · The outcome
40% Faster business reporting
- 40%
- Faster business reporting
- 60%
- Decision automation
- <2s
- Insight generation
- 85%
- Reduced manual analysis
- Signal automation
- Backtesting engine
- No-code builder
- Algo intelligence layer
The problem
- Charts move faster than a manual review can follow
- Patterns in live market data are easy to miss
- Signals are hard to trust without a backtest
- Trade setups stall when execution and risk are handled by hand
What we built
- Chart scanning that detects patterns in live market data
- Signal generation from technical inputs, including RSI and MACD
- Automated trade setups and execution from those signals
- A backtesting framework before a strategy goes live
- Risk management around open positions
Key features
- Chart scanning
- Pattern detection
- Signal generation
- RSI and MACD logic
- Backtesting
- Automated trade execution
- Risk management
- Live trading integration
Architectural flow
01
Market scan
Read live market data and scan charts for setups.
02
Signal generation
Detect patterns and build signals from technical inputs such as RSI and MACD.
03
Trade setup
Turn signals into automated trade setups, then backtest and apply risk controls before live execution.
Tech stack
- Next.js
- Docker
- Redis
- AWS
- GPT-4 / Claude
- Python
- FastAPI
- PostgreSQL
- React
- Open API