Pulse Trading, Fintech and trading
Exchange data, signals, and AI analysis on one research screen
An AIOVIX trading research workspace that puts exchange inputs, technical signals, and AI interpretation together.
Built for: AIOVIX's own trading research product for crypto markets.
- exchanges connected
- 2
- exchanges connected
- context views: funding, liquidation, wallet
- 3
- context views: funding, liquidation, wallet
- analysis beside every chart
- AI
- analysis beside every chart
First agent live in one week. A short call. Your first agent, a fixed quote, and the timeline. No obligation.
Sound familiar?
- “We flip between exchange feeds, charts, and wallet trackers to check one idea.”
- “A signal tells us the direction but not the market context behind it.”
- “We can't tell where the calculated data ends and the AI's opinion begins.”
How the system works.
- Step 1
Exchange market views
Separate Binance and Hyperliquid routes for market data, candles, and signals, with instrument detail pages and filters.
- Step 2
Technical signals and charts
Python modules generate indicator-based signals shown beside TradingView charts and signal badges.
- Step 3
Market context panels
Funding, liquidation, wallet activity, and live-price context let a user investigate a signal beyond its direction.
- Step 4
Instrument-specific AI analysis
An analysis request builds a prompt from the available data and shows the model's written view next to the calculated indicators.
- Step 5
Paper-trading research engines
Python paper-trading and adaptive-strategy modules test ideas without risking real money.
What changed.
- 2 exchanges, Binance and Hyperliquid, in one research workspace.
- AI interpretation sits beside calculated signals instead of replacing them.
- Funding, liquidation, and wallet context available for every instrument.
- Adaptive strategies tested through paper trading with dedicated signal tests.
The AI inside.
OpenAI
Writes instrument-specific analysis from market data, with a configurable model.
The full stack.
Frontend
- Next.js
- React
- TradingView charting
Backend
- Next.js API routes
- Python strategy modules
- Paper-trading engines
AI and automation
- OpenAI analysis route
- Adaptive-signal modules
Integrations
- Binance
- Hyperliquid
- Live-price feeds
This fits you if...
- Your analysts pull data from several platforms before they can review one idea.
- You want AI commentary, but kept clearly separate from calculated figures.
- You need to test strategies safely before any capital is involved.
Start with a free Agent Audit. First agent live in one week
The Finance Operations Agent brings this to your team.
Close the month in days and get paid on time.
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Project overview
Exchange data, technical signals, and AI analysis on one screen
Services: AI Fintech ; AI Workflow Automation
Delivery: AIOVIX-owned trading research product.
Project Scope
- 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.
The Problem
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.
What We Delivered
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.
Engineering Decisions
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.
Results
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.
Technology stack
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.
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Want this for your team?
Book a short audit call and show us the workflow. You leave with the first agent we would build, a fixed quote, and the timeline.