[Vibe Coding Guide] How to Generate a Perfect Blueprint for Your AI Trading Bot with Gemini

by

Intro “Vibe Coding” with AI is fast, but without a solid plan, it’s chaotic. Especially for financial applications like a US Market Pulse Dashboard, you need a concrete architectural blueprint before writing a single line of code.

Today, I’ll show you how to use Gemini (or any advanced LLM) to generate a professional-grade Project Blueprint that aligns perfectly with the Antigravity Protocol (our safety-first architecture).

Step 1: The Context Setup

First, you need to tell Gemini how to think. We are not building a toy app; we are building a “Fortress.”

Prompt to Gemini:

“Act as a Technical Project Manager and System Architect for ‘Project Antigravity’. We are building a US Market Pulse Dashboard.

Core Philosophy:

  1. Safety First: No accidental API calls.
  2. Cost-Efficiency: Mock data first, real API later.
  3. Survival: Anti-ban strategies (Jitter, Circuit Breakers) are mandatory.

Based on this, I need a detailed blueprint covering Frontend, Backend, Data Sources, Analysis Methods, Reliability, and User Needs.”

Step 2: Defining the 6 Pillars

Ask Gemini to break down the blueprint into 6 specific areas. This ensures no aspect of the system is overlooked.

The 6 Pillars we need:

  1. Frontend: Low-latency, “Cockpit” style UI with safety switches.
  2. Backend: “Fortress” architecture with quota management.
  3. Data Sources: A hybrid of free (dev) and paid (prod) sources.
  4. Analysis: Evidence-based sentiment & technical analysis.
  5. Reliability: Confidence scores and outlier detection.
  6. User Needs: What do US investors actually care about in 2026?

Step 3: The “One-Click” Generation

Instead of chatting back and forth for hours, use the Master Prompt below to get everything at once.

Copy & Paste this into Gemini:

“Generate a comprehensive Project Blueprint for the US Market Pulse Dashboard. Please create 6 separate markdown documents detailed below:

  1. frontend.md: UI/UX specs focusing on the ‘Simulation Mode’ switch and passive data refreshing.
  2. backend.md: Tech stack and ‘Fortress Architecture’ details (Quota Manager, Circuit Breaker).
  3. data_sources.md: Strategy for using Mock Data vs. Real APIs (yfinance, FRED, etc.).
  4. analysis_methodology.md: How we combine Technical indicators with AI Sentiment analysis (FinBERT).
  5. reliability_metrics.md: How to calculate and display ‘AI Confidence Scores’ and detect outliers.
  6. us_investor_needs.md: Key market trends and data points US investors demand right now (e.g., Fed rates, Sector Rotation).

Constraint: Write in professional technical English, suitable for a GitHub Wiki or System Documentation.”

# 🎨 Frontend Architecture: Resource-Conscious Cockpit UI

## 1. Design Philosophy
* **Concept:** "Deep Immersion Cockpit"
* **Goal:** Minimize cognitive load and prevent accidental API usage.
* **Theme:** Dark Mode (`#0F1115`) for long-term monitoring comfort.

## 2. Core Safety Features (Antigravity Mandates)
* **Safety Switch (Critical):**
    * **Location:** Top-right header.
    * **Function:** Toggles between `Simulation Mode` (Green, Mock Data) and `Live Mode` (Red, Real API).
    * **Logic:** Switching to 'Live' triggers a confirmation modal.
* **Passive Refresh:**
    * **Rule:** NO auto-refreshing by default.
    * **Action:** Users must click a **"Scan Market (Cost: 1 Credit)"** button to update data.
* **Freshness Indicator:**
    * Display "Last updated: [Time] ago" prominently to reduce compulsive clicking.

## 3. Layout Strategy (Streamlit/Web)
* **Sidebar:**
    * Asset Selection (Tickers)
    * Timeframe Settings (1D, 1W, 1M)
    * Technical Indicator Toggles (RSI, MACD, Bollinger Bands)
* **Main Dashboard:**
    * **Top:** Real-time Ticker Tape (S&P 500, Nasdaq 100).
    * **Center:** Interactive Candle Chart with Overlay Indicators.
    * **Bottom:** Sentiment Heatmap & AI News Summary.
# 🧠 Backend Architecture: The Fortress

## 1. Core Principles
* **API Last:** External calls are the absolute last step in any logic chain.
* **Local First:** Always prioritize cached or mock data.

## 2. Technology Stack
* **Language:** Python 3.11+
* **Framework:** Streamlit (for rapid UI) or FastAPI (for scalable API).
* **Data Processing:** Pandas, NumPy.
* **Technical Analysis:** TA-Lib.
* **AI/ML:** PyTorch, FinBERT (Sentiment Analysis).
* **Storage:** SQLite (Local Caching), JSON (Mock Data).

## 3. Defense Mechanisms
* **Quota Manager:**
    * Singleton service that tracks daily API usage.
    * **Action:** Blocks requests if `daily_count >= limit`.
* **Circuit Breaker:**
    * **Trigger:** 429 (Too Many Requests) or 403 (Forbidden) errors.
    * **Action:** Locks the specific module for 1 hour to prevent IP bans.
* **Jitter Strategy:**
    * Adds random delays (`random.uniform(1.5, 4.0)s`) to all scraping/polling loops.
# 📡 Data Sourcing Strategy: Hybrid Approach

## 1. Phase Strategy
* **Phase 1-3 (Development):** strict use of **Mock Data** only.
* **Phase 4 (Production):** Gradual rollout of Real API connections.

## 2. Data Providers
### A. Market Data (Price/Volume)
* **Development:** `yfinance` (Free, heavily rate-limited).
* **Production:** `Alpha Vantage` or `Polygon.io` (Stable, API Key required).
* **Backup:** Local `market_mock.json` (for offline dev).

### B. Macroeconomics
* **Source:** **FRED (Federal Reserve Economic Data) API**.
* **Key Metrics:**
    * CPI (Consumer Price Index)
    * 10-Year Treasury Yield
    * Unemployment Rate
    * Fed Funds Rate

### C. News & Sentiment
* **Source:** NewsAPI, RSS Feeds (CNBC, Reuters, Bloomberg).
* **Constraint:** Text-only fetching to minimize bandwidth.
# 📊 Analysis Methodology: Evidence-Based Logic

## 1. Technical Analysis (Quantitative)
* **Trend:** Moving Averages (SMA/EMA 20, 50, 200) alignment.
* **Momentum:** RSI (Relative Strength Index) < 30 (Oversold) or > 70 (Overbought).
* **Volatility:** Bollinger Band Width expansion/contraction.

## 2. Sentiment Analysis (Qualitative)
* **Model:** **FinBERT** (Financial Bidirectional Encoder Representations from Transformers).
* **Logic:**
    * Ingest last 50 news headlines for a ticker.
    * Classify each as `Positive`, `Negative`, or `Neutral`.
    * Calculate a weighted "Sentiment Score" (-1.0 to +1.0).
* **Context:** Cross-reference Sentiment Score with Price Action (e.g., High Sentiment + Price Drop = Divergence).

## 3. Macro Correlation
* **Logic:** Monitor the correlation coefficient between **10Y Treasury Yield** and **Nasdaq 100**.
* **Signal:** High negative correlation implies rate-sensitive market conditions.
# 🛡️ Reliability & Accuracy Standards

## 1. Trust Indicators
* **Source Transparency:**
    * Every chart must display: *"Source: [Provider Name], Delayed [15] min"*.
* **AI Confidence Score:**
    * When FinBERT analyzes sentiment, display the model's certainty.
    * **UI:** *"AI Confidence: 92%"* (Green) vs *"AI Confidence: 45%"* (Amber).
    * **Rule:** If Confidence < 60%, label result as "Inconclusive".

## 2. Data Integrity Checks
* **Outlier Detection:**
    * **Logic:** If `Current Price` deviates > 10% from `5-min Moving Average` without news volume spike -> Flag as "Potential Data Error".
* **Staleness Check:**
    * If data timestamp > 1 hour during market hours -> Overlay "Data Stale" warning.
# 🇺🇸 US Investor Needs & Market Trends (2025/2026)

## 1. Macro Sensitivity
* **The "Fed Watch":** Investors are hyper-focused on interest rate cut/hike probabilities.
* **Inflation:** Real-time tracking of CPI/PCE releases vs. expectations.

## 2. Sector Rotation
* **Visual:** **Sector Heatmap** is essential.
* **Trend:** Tracking capital flow from **Growth (Tech)** to **Value (Energy/Finance)** and vice versa.

## 3. The "AI Trade"
* **Focus:** Momentum tracking for "Magnificent 7" and AI-adjacent stocks (Semiconductors, Data Centers).
* **Metric:** Social volume and news mention frequency for AI keywords.

## 4. Fear & Greed
* **Psychology:** Investors want to know the market's emotional state, not just price.
* **Implementation:** A proprietary "Fear & Greed Index" combining VIX (Volatility), Put/Call Ratio, and Momentum.

Step 4: Apply to Your Project

Once Gemini generates these 6 blueprint files, save them into your project’s docs/ or planning/ folder. These will serve as the “Source of Truth” for your Antigravity Agent (Cursor/Windsurf).

Now, don’t just start coding immediately. Engage in a dialogue with your Agent to refine the plan using the prompts below.

1. First Interaction: Analysis & Feedback Ask the Agent to review the blueprints and offer its professional opinion.

Copy & Paste this into your Agent (Cursor/Windsurf):

“Read the 6 blueprint documents I just added. Analyze the content and provide your opinion on any necessary modifications, additions, or improvements.”

  • Action: Read the Agent’s feedback carefully. If the Agent suggests better libraries or points out a logic gap, discuss it and decide whether to adopt the advice.

2. Second Interaction: Execution Planning Once you agree on the direction, lock in the plan and start the step-by-step implementation.

Copy & Paste this into your Agent:

“Refine the plan based on the points we discussed (specifically regarding [Insert Topic if needed]), and break down the execution plan into granular steps. Then, let’s proceed by discussing one step at a time.”

Now, you are ready to code with a crystal-clear roadmap. 🚀

 Disclaimer: All content in this article is for educational purposes only and does not constitute financial advice. Algorithmic trading involves a high level of risk, and you may lose your entire investment. The author and Vibe Algo Lab are not responsible for any financial losses incurred from using the code or strategies provided herein.

Post Views: 12

Leave a Comment