chart-vision

name	chart-vision
description	Complete chart vision pipeline — render charts, preprocess images, detect candlestick patterns, detect classical chart patterns, detect trendlines and S/R levels, and annotate results back onto charts. Use this skill for ANY chart image task: "render a chart", "create chart image", "screenshot chart", "generate candlestick chart", "draw chart with indicators", "produce chart for analysis", "make a chart PNG", "render OHLCV", "chart to image", "visual chart output", "clean chart image", "preprocess chart", "enhance chart", "remove grid from chart", "prepare chart for analysis", "normalize chart image", "read candles from image", "what candlestick pattern is this", "analyze candles in screenshot", "identify candle pattern from chart", "visual candle detection", "find patterns in chart image", "what chart pattern is this", "pattern recognition from screenshot", "detect triangle from chart image", "visual pattern scan", "AI chart pattern detection", "head and shoulders from image", "detect double top from screenshot", "identify engulfing candle", "doji", "hammer", "pin bar", "morning star", "evening star", "three soldiers", "ascending triangle", "descending triangle", "flag", "wedge", "cup and handle", "find support resistance from image", "detect trendlines", "draw trendlines on chart", "key levels from screenshot", "S/R detection", "find channels", "horizontal levels", "automatic trendline detection", "annotate chart", "draw levels on chart", "mark patterns on image", "highlight trade setup", "overlay analysis on screenshot", "visual markup", "label candles on chart", "draw buy/sell on chart", "head and shoulders", "double top", "double bottom", "triangle", "channel", "pennant", "pattern scan", "chart patterns", "find patterns", "bull flag", "bear flag", "AI signal aggregation", "combine strategy signals", "weighted vote", "meta-model", "trading data science", "feature engineering", "ML model", "anomaly detection", "mplfinance chart", "publication-quality chart PNG", "base64 chart output", "indicator overlay", "multi-signal AI analysis", or any chart rendering, image preprocessing, visual pattern detection, chart annotation, or AI signal aggregation task. Full pipeline: render → preprocess → analyze → annotate → aggregate.
kind	reference
category	trading/analysis
status	active
tags	["analysis","chart","grid-trading","trading","vision"]
related_skills	["analyze"]

Chart Vision — Complete Pipeline

Overview

Unified skill covering the entire chart image pipeline in one place:

1. Rendering — produce high-quality candlestick chart images from OHLCV data
2. Preprocessing — clean, denoise, and normalize raw chart screenshots
3. Candlestick Vision — detect single and multi-candle patterns from images
4. Chart Pattern Vision — detect H&S, triangles, wedges, double tops from images
5. Pattern Scanner — algorithmic classical pattern detection from price data
6. Trendline & S/R Vision — detect trendlines, channels, S/R zones from images
7. Annotation Overlay — draw all analysis results back onto the chart image

Pipeline Order

chart-vision-renderer       → render OHLCV to PNG
chart-image-preprocessor    → clean, denoise, extract ROI
chart-pattern-vision        → detect candle + chart patterns from image
trendline-sr-vision         → detect S/R and trendlines from image
chart-pattern-scanner       → detect patterns from price data (no image needed)
chart-annotation-overlay    → draw everything back onto chart  ← final step

Reference Files

File	Contents
references/rendering.md	Chart renderer: mplfinance + matplotlib, indicators, dark/light themes
references/preprocessing.md	Image preprocessing: denoise, ROI extract, grid removal, contrast, color analysis
references/pattern-vision.md	CV candlestick detection + classical chart pattern detection from images
references/trendline-sr.md	Hough transforms, LSD, horizontal projection, S/R clustering, channels
references/scanner-and-annotation.md	Price-data pattern scanner (swing-based) + annotation overlay drawing engine

Stack

• mplfinance — candlestick rendering
• matplotlib 3.10 — rendering engine
• OpenCV 4.13 — all CV operations (Hough, LSD, morphology, edge detection, drawing)
• scikit-image 0.26 — region props, probabilistic Hough, restoration
• scipy 1.17 — peak finding, signal processing, clustering
• Pillow 12.1 — text rendering, image post-processing
• numpy 2.4 — array operations
• sklearn — RandomForest, GradientBoosting, calibration, TimeSeriesSplit (AI signal aggregation & ML)
• statsmodels — ADF, Granger causality (statistical analysis)

Quick Usage Examples

Render a chart from OHLCV data

import mplfinance as mpf
import pandas as pd

df = pd.read_csv("ohlcv.csv", index_col="date", parse_dates=True)
mpf.plot(df, type="candle", style="charles", volume=True,
         mav=(20, 50), savefig="chart.png", figsize=(14, 8))

Preprocess a chart screenshot

import cv2
import numpy as np

img = cv2.imread("screenshot.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Remove grid lines
denoised = cv2.fastNlMeansDenoising(gray, h=15)
# Enhance edges for pattern detection
edges = cv2.Canny(denoised, 50, 150)
# Extract ROI (crop to chart area)
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
largest = max(contours, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(largest)
chart_roi = img[y:y+h, x:x+w]

Detect support/resistance from image

from scipy.signal import find_peaks

# Project pixel intensities horizontally to find price levels
projection = np.mean(gray, axis=1)
peaks, props = find_peaks(-projection, distance=20, prominence=10)
sr_levels = peaks  # pixel y-coordinates of S/R lines

# Draw detected levels
for level in sr_levels:
    cv2.line(img, (0, level), (img.shape[1], level), (0, 255, 0), 1)

Annotate chart with analysis

from PIL import Image, ImageDraw, ImageFont

img = Image.open("chart.png")
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("arial.ttf", 14)

# Draw buy signal
draw.text((entry_x, entry_y - 20), "BUY", fill="green", font=font)
draw.rectangle([sl_x-2, sl_y-2, sl_x+2, sl_y+2], fill="red")
draw.text((sl_x + 5, sl_y), f"SL: {sl_price:.5f}", fill="red", font=font)
draw.text((tp_x + 5, tp_y), f"TP: {tp_price:.5f}", fill="green", font=font)
img.save("annotated_chart.png")