Pulse Trading

Exchange data, technical signals, and AI analysis on one screen

Services: AI Fintech ; AI Workflow Automation
Delivery: AIOVIX-owned trading research product.

• Binance and Hyperliquid market views.
• Technical signals, instrument charts, and indicator panels.
• Wallet tracking, funding, liquidation, and market-context views.
• Python paper-trading engines and adaptive-strategy modules.
• Instrument-specific AI analysis.

A signal gives the trader only part of the picture. They still need the chart, market conditions, funding information, and a way to inspect the reasoning behind an idea.

Pulse combines those inputs in a research workspace. The product separates calculated market information from model-generated interpretation so the user can examine both.

The dashboard includes exchange-specific market pages, instrument detail views, charts, filters, signal badges, and analytics.

Python modules generate technical signals and run paper-trading workflows. Next.js routes expose market, candle, signal, wallet, and broader context data.

An AI-analysis request prepares an instrument-specific prompt from the available information. The answer appears alongside the research views rather than replacing the chart or calculated indicators.

Keep the exchange paths explicit. Binance and Hyperliquid have separate routes for market data, candles, and signals.

Distinguish calculation from interpretation. Indicator-based signal logic is separate from the language model's written analysis.

Provide a research environment. Paper-trading code lets strategies be investigated without representing simulated returns as money earned.

Keep context visible. Funding, liquidation, and wallet views let the user investigate a signal beyond its direction label.

Pulse provides an internal workspace for reviewing exchange data and AI analysis together. The source includes adaptive-signal tests and paper-trading engines, giving the research logic a separate place to be exercised.

No live-return or predictive-accuracy figure is supported by the reviewed material. The case study concerns the product and research infrastructure, not investment performance.

Next.js, React; Python strategy and paper-trading modules; Binance and Hyperliquid integrations; TradingView charting assets; live-price context; OpenAI analysis route with a configurable model.

Market inputs beside the AI interpretation.
Pulse combines exchange data, technical signals, charts, wallet activity, and instrument-specific AI analysis. Its Python research modules include adaptive strategies and paper trading.

Pulse is our own trading research product. We connected exchange feeds and technical signals to charts and AI analysis, while keeping the model's interpretation separate from calculated data and paper-trading results.