Local TradingView chart analysis via OpenCV + Tesseract OCR — no API keys, no GPU, runs fully offline.
Why · How It Works · vs Vision LLMs · MCP Integration · Hybrid Approach · Limitations
AI agents that analyze TradingView charts typically rely on one of three approaches — each with serious tradeoffs:
| Approach | Cost | Speed | Privacy | Quality |
|---|---|---|---|---|
| GPT-4 Vision / Gemini | $$$ per API call | ~3-10s | Your chart data leaves your machine | High — understands context |
| Local VLM (LLaVA, Qwen-VL) | Free (GPU required) | ~5-30s | Fully local | Medium — small models hallucinate |
| OpenCV + OCR (this project) | Free | < 1s | Fully local | Medium — structural, not semantic |
Chart Vision MCP occupies a unique niche: it's the only option that is simultaneously free, instant, and fully offline. It doesn't "understand" charts the way an LLM does — but for structural analysis (candle colors, level lines, zone boundaries, volume) it's often more reliable than a vision LLM that might hallucinate price levels.
┌─────────────────┐ screenshot ┌──────────────────────┐
│ Screen / TV │ ───────────────► │ OpenCV Analysis │
│ │ │ │
└─────────────────┘ │ Step 1: Grab screen │
│ Step 2: Color mask │
┌─────────────────┐ │ Step 3: Hough lines │
│ Tesseract OCR │ ◄─────────────── │ Step 4: Contours │
│ │ image regions │ Step 5: OCR crop │
└─────────────────┘ │ │
└──────────┬───────────┘
│ JSON result
▼
┌──────────────────────┐
│ MCP Response │
│ { │
│ candle_bias, │
│ levels: [...] │
│ zones: [...], │
│ labels: [...], │
│ volume_rel, │
│ trend │
│ } │
└──────────────────────┘
| Step | Method | What it finds | Why it matters |
|---|---|---|---|
| 1. Candle colors | HSV color masking | Green/red pixel ratio per region | Bullish vs bearish bias for the visible period |
| 2. Level lines | Probabilistic Hough Transform | Horizontal lines matching known colors | Support/resistance levels drawn on the chart |
| 3. Zones | Contour analysis on color masks | Filled rectangles (FVG zones, order blocks) | Key areas where price might react |
| 4. Volume | Bar height measurement | Relative volume comparison across visible bars | Confirms whether moves have conviction |
| 5. OCR | Tesseract + preprocessing | Price labels, indicator values, annotations | Extracts actual numbers from the image |
| 6. Trend | Price position algorithm | Up / Down / Sideways classification | Overall market direction from the visible range |
{
"candle_bias": "bullish",
"green_pct": 58.3,
"red_pct": 41.7,
"levels": [
{"price": 19250, "color": "red", "type": "horizontal"},
{"price": 19300, "color": "blue", "type": "horizontal"}
],
"zones": [
{"color": "yellow", "bounds": {"x1": 100, "y1": 200, "x2": 300, "y2": 250}}
],
"labels": ["19250.00", "19300.00", "RSI: 62.4"],
"volume_rel": "increasing",
"trend": "up"
}Vision LLMs are getting better at reading charts, but they have fundamental weaknesses that OpenCV doesn't:
| Scenario | OpenCV | Vision LLM |
|---|---|---|
| Color accuracy | Exact HSV matching | May confuse similar colors |
| Line position | Pixel-exact coordinates | Approximate spatial reasoning |
| Consistency | Deterministic — same input, same output | Stochastic — different analysis each time |
| Speed | < 1 second | 3-30 seconds |
| Cost | $0 | $0.01-$0.10 per call |
| Offline | Yes | No (cloud models) |
| Scenario | OpenCV | Vision LLM |
|---|---|---|
| Chart patterns | Can't detect head-and-shoulders | Can reason about patterns |
| Context understanding | "Many green candles = bullish" | "This looks like a breakout from consolidation" |
| Complex indicators | Can only see colored lines | Can read indicator values and interpret |
| Ambiguity | Fails on unclear visuals | Makes educated guesses |
Use Chart Vision MCP for the structural analysis (what colors, lines, and zones are on screen), and feed those results into an LLM for semantic interpretation. This gives you both speed (structural data is instant) and understanding (LLM reasons about the structured data).
This project is a Model Context Protocol (MCP) server, which means it integrates natively with AI agents like Hermes, Claude Code, or any MCP-compatible host.
{
"mcpServers": {
"chart-vision": {
"command": "python3",
"args": ["/path/to/chart_vision_mcp_server.py"],
"env": {}
}
}
}| Tool | Description | Parameters |
|---|---|---|
chart_vision_analyze |
Screenshot + full analysis (OpenCV + OCR) | region: "full" or "chart" |
chart_vision_screenshot |
Screenshot only, no analysis | (none) |
Both tools work without any API key or internet connection.
Chart Vision MCP is most powerful when combined with real TradingView data. The vision layer gives you visual structure; the TV data layer gives you exact numeric values:
1. mcp_tv_chart_get_state → what symbol, timeframe, indicators
2. mcp_tv_data_get_ohlcv → last 20 bars (exact prices)
3. mcp_tv_data_get_pine_lines → exact horizontal level prices
4. mcp_tv_data_get_pine_labels → FVG / Order Block zones (exact boundaries)
5. mcp_tv_data_get_study_values → RSI, MACD, Bollinger Bands (exact values)
6. mcp_chart_vision_analyze → visual structure (colors, candle bias)
The TV MCP tools give you ground truth (exact prices, indicator values). Chart Vision gives you the visual gestalt (is it bullish or bearish-looking?). Together they're stronger than either alone.
git clone https://github.com/nessos666/chart-vision-mcp.git
cd chart-vision-mcp
pip install opencv-python pillow numpy pytesseract
sudo apt install tesseract-ocr # Linux
# Run standalone analysis
python chart_vision.py --debug
# Or use the MCP server
python chart_vision_mcp_server.py| File | Purpose | Lines |
|---|---|---|
chart_vision.py |
Standalone OpenCV analysis | 458 |
chart_vision_mcp_server.py |
MCP server (v2.0, stdio JSON-RPC) | 258 |
chart_vision_gemini.py |
Gemini Vision integration (optional, requires API key) | ~150 |
This is not an ML model. It doesn't "understand" charts the way a human or LLM does. It provides a deterministic, color-based structural analysis of what's visible on screen. It will not:
- Detect complex patterns (head-and-shoulders, wedges, triangles)
- Interpret candlestick patterns (doji, engulfing, harami)
- Read every indicator value reliably (OCR accuracy depends on font size and contrast)
- Understand market context or news
Use it for what it's good at: fast, reliable, zero-cost structural chart analysis that never hallucinates a price level.
opencv-python>=4.8
pillow>=10.0
numpy>=1.26
pytesseract>=0.3
Plus system package: tesseract-ocr
# Syntax check
python3 -m py_compile chart_vision.py
python3 -m py_compile chart_vision_mcp_server.py
# Run with debug (shows intermediate steps)
python chart_vision.py --debug- nq-strategy-builder — Full NQ futures backtesting framework
- tv-watch-agent — CDP-based TradingView automation
- tv-mcp-server — TradingView MCP server (68 tools) — use alongside Chart Vision for the hybrid approach
MIT — free to use, modify, and integrate.
Built for local, offline, zero-cost TradingView chart analysis.
The only chart vision MCP server that works without internet.
github.com/nessos666