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暴躁祖安人
暴躁祖安人

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GridSense: Real-Time Stock Snapshot Grid Trading Agent (n8n + Bright Data)

#devchallenge #n8nbrightdatachallenge #ai #webdev

n8n and Bright Challenge: Unstoppable Workflow

This is a submission for the AI Agents Challenge powered by n8n and Bright Data

What I Built

GridSense is a real-time AI trading assistant that ingests live stock/ETF/index snapshots and fresh headlines via Bright Data, then recommends grid trading actions (buy/hold/sell) with clear price bands, position sizing, and risk limits.
It’s designed for liquid tickers (e.g., QQQ, ^NDX, NKY, 510880) and supports:

  • Auto-grid construction (center = last price; step = ATR- or %-based; levels up/down configurable).
  • Dynamic re-centering when volatility/price regimes shift.
  • Capital allocation & max-exposure guardrails (per-level budget, cumulative cap, soft stop).
  • Explainable outputs: the agent shows why it recommends a level (snapshot + recent news shocks + volatility filter).

Demo

Public Chat: https://your-public-chat.example.com
Video Walkthrough (alt): https://your-demo-video.example.com

Usage hints in chat:

  • “analyze TSLA with 1% grid, 10 levels”
  • “recenter grid on QQQ if 30m ATR > yesterday ATR”
  • “pause buys if negative news surprise > medium”

n8n Workflow

Workflow JSON (Gist): https://gist.github.com/your-handle/gridsense-workflow.json

High-level flow

  1. Chat Trigger / Example Chat Window →
  2. AI Agent (OpenAI) with tools →
  3. Bright Data Verified Node (snapshot/news) →
  4. Function/Code (normalize quotes, compute ATR/step) →
  5. Decision Block (grid build/recenter) →
  6. Formatter (human output + CSV/JSON) →
  7. (Optional) Webhook to downstream execution simulator

Technical Implementation

System Instructions (excerpt used in AI Agent node) 

You are a quantitative trading assistant. Always:
1) First call the Bright Data tool to fetch the latest price snapshot (and, if available, recent headlines) for the user’s tickers.
2) If data is sparse or delayed, state assumptions and proceed.
3) Build a symmetric grid around last price:
   - step: percentage or ATR-based (select the tighter of user-provided step or ATR-derived step)
   - levels: N up and N down
   - capital: allocate per-level, enforce max exposure cap
4) Output: 
   a) Buy levels (price, qty, rationale)
   b) Sell levels (price, qty, rationale)
   c) Recenter/disable rules if volatility regime changes
   d) Clear final verdict: BUY / HOLD / TRIM with confidence.
Never provide financial advice; provide educational, model-based signals only.
Enter fullscreen mode Exit fullscreen mode

Model Choice

  • LLM: OpenAI GPT-4o-mini (cost-efficient, strong tool-use).
  • Latency controls: short context + compact JSON schema for tool outputs.
  • Determinism: temperature=0.2 for decisions, 0.5 for explanations.

Memory

  • Session memory for user preferences (default step %, number of levels, max exposure, favored tickers).
  • Stateless option for compliance: memory can be disabled per session.

Tools Used

  • Bright Data Verified Node — primary real-time data source for:

    • Quotes snapshot from public finance pages.
    • Recent headlines to de-risk buys during adverse news.

  • Function/Code node — grid math:

    • Step size = max(user_step%, min_cap, ATR_k * 100%)) with guards.
    • Position sizing = geometric or equal-weight per grid line.
    • Recenter rule = trigger if price drifts > X steps or ATR regime switches.

  • IF / Merge / Switch — route when snapshot is stale, retry or fall back.

Bright Data Verified Node

How it’s wired

  • Operation: “Scrape / Collect by URL (snapshot)” for designated quote/news URLs.
  • Inputs: ticker, market, urls[], optional dataset tag for run labeling.
  • Flow:
  1. Trigger collection (snapshot).
  2. Poll status until ready.
  3. Download structured payload (price, change, ts, headline items).
    • Error/Anti-bot handling: rely on Bright Data’s infra for CAPTCHAs/rate limits; implement exponential backoff + max retry 2 in n8n.
    • Normalization: Map fields → {symbol, last, ts, prev_close, day_chg, day_chg_pct, headlines[]}; validate timestamps and drop outliers.

Why Bright Data helps the grid

  • Freshness: grid levels use current last price and volatility proxy; stale quotes would misplace bands.
  • Context: headlines act as binary filters (e.g., “negative surprise” → reduce buys / widen step).

Journey

Problem & approach. Retail-friendly grid strategies often fail in live use because step sizes and centers are static and blind to regime shifts. I used Bright Data to ground the agent in fresh quotes and contextual headlines, and n8n to orchestrate a robust, inspectable pipeline.

What was hard.

  • Snapshot timing: aligning tool polling with LLM tool-use; solved with a status-poll sub-flow and strict timeouts.
  • Volatility-aware grids: ATR from short windows can be noisy; I capped min/max steps and required consensus across 15m/60m lookbacks.
  • Explainability: traders want why; I structured outputs into level tables with per-level rationales.

What I learned.

  • Real-time web data unlocks actionable agent behavior if you normalize and guard for staleness.
  • n8n’s AI Agent + Verified Node combo keeps the system low-code but still auditable.
  • Good defaults matter: conservative exposure caps and recenter rules prevent most “marching losses.”

Compliance & disclaimer.
This project analyzes public information for educational purposes only. It does not provide financial advice or execute trades. Respect each site’s terms of use and local regulations when collecting data.

Cover Image idea: A minimal grid-ladder chart overlaying live price ticks.
Tags: devchallenge, n8nbrightdatachallenge, ai, webdev

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