Tableau de Bord Indicateurs Techniques

VérifiéSûr

Crée un tableau de bord interactif pour l'analyse technique avec Plotly Dash ou Streamlit. Supporte les configurations mono-symbole, multi-symbole et multi-timeframe avec actualisation en temps réel. Utile pour visualiser des indicateurs et surveiller les marchés financiers en temps réel.

Spar Skills Guide Bot
Data & IAIntermédiaire
4002/06/2026
Claude Code
#dashboard#technical-analysis#plotly-dash#streamlit#finance

Recommandé pour

Notre avis

Crée un tableau de bord web interactif pour l'analyse technique d'indicateurs boursiers en utilisant Plotly Dash ou Streamlit.

Points forts

  • Support de plusieurs types de tableaux de bord (mono-symbole, multi-symbole, multi-timeframe, scanner)
  • Thème sombre intégré et sélecteurs de symboles, d'échange et d'intervalle
  • Auto‑rafraîchissement en temps réel avec dcc.Interval ou st.rerun()
  • Utilisation de templates prédéfinis pour accélérer le développement

Limites

  • Nécessite une configuration préalable des règles de dépendances
  • Les tableaux de bord ne sont pas déployés automatiquement
  • Limitée aux indicateurs techniques de base sans personnalisation avancée
Quand l'utiliser

Utilisez cette compétence lorsque vous avez besoin d'un tableau de bord d'analyse technique visuel et interactif pour un ou plusieurs symboles boursiers avec des indicateurs courants.

Quand l'éviter

Évitez cette compétence si vous avez besoin d'une analyse quantitative complexe ou d'un système de trading automatisé sans interface utilisateur.

Analyse de sécurité

Sûr
Score qualité85/100

The skill creates a local web dashboard for technical analysis using Plotly Dash or Streamlit. It involves file creation, running a local Python server, and loading environment variables from .env, but there are no instructions for data exfiltration, system destruction, or any dangerous operations. The Bash usage is limited to starting the application locally, which is standard and safe.

Aucun point d'attention détecté

Exemples

Single symbol Dash dashboard
Build a single symbol dashboard for SBIN using Dash with EMA and RSI indicators.
Multi-timeframe Streamlit dashboard
Create a multi-timeframe Streamlit dashboard for RELIANCE with 5m, 15m, 1h, and D timeframes.
Live scanner dashboard
Create a scanner-dashboard type in Dash for a watchlist of 10+ NSE symbols.

name: indicator-dashboard description: Build a web dashboard for technical indicator analysis using Plotly Dash or Streamlit. Supports single-symbol, multi-symbol, and multi-timeframe layouts with real-time refresh. argument-hint: "[type] [symbol]" allowed-tools: Read, Write, Edit, Bash, Glob, Grep, AskUserQuestion

Create a web dashboard for interactive technical analysis using Plotly Dash or Streamlit.

Arguments

Parse $ARGUMENTS as: type symbol

  • $0 = dashboard type. Default: single
    • Dash types: single, multi-symbol, multi-timeframe, scanner-dashboard
    • Streamlit types: streamlit-single, streamlit-multi, streamlit-scanner
  • $1 = symbol (e.g., SBIN, RELIANCE). Default: SBIN

If no arguments, ask the user what kind of dashboard they want and whether they prefer Dash or Streamlit.

Instructions

  1. Read the indicator-expert rules, especially:
    • rules/dashboard-patterns.md — Dash app patterns
    • rules/streamlit-patterns.md — Streamlit app patterns
    • rules/plotting.md — Chart patterns
    • rules/data-fetching.md — Data loading
  2. Create dashboards/{dashboard_name}/ directory (on-demand)
  3. Create app.py in dashboards/{dashboard_name}/
  4. Use the matching template from rules/assets/

Dashboard Requirements

All dashboards must include:

  • Dark theme: Dash uses dbc.themes.DARKLY; Streamlit uses [theme] base = "dark" or CSS injection
  • Symbol input: Text input or dropdown for symbol selection
  • Exchange selector: NSE, BSE, NFO, NSE_INDEX
  • Interval selector: 1m, 5m, 15m, 1h, D
  • Indicator selectors: Checkboxes/multiselect for overlay and subplot indicators
  • Interactive chart: Plotly chart with template="plotly_dark", xaxis_type="category"
  • Stats display: Key metrics (LTP, Change, Volume, indicator values)
  • Auto-refresh: Dash uses dcc.Interval; Streamlit uses st.rerun() with time.sleep()
  • Load .env from project root via find_dotenv()

Dash Dashboard Types

single — Single Symbol Dashboard (Dash)

  • One symbol with configurable indicators
  • Overlays: EMA, SMA, Bollinger, Supertrend, Ichimoku (checkboxes)
  • Subplots: RSI, MACD, Stochastic, Volume, ADX, OBV (checkboxes)
  • Stats panel: LTP, day change, volume, selected indicator values
  • Template: rules/assets/dashboard_basic/app.py

multi-symbol — Multi-Symbol Watchlist (Dash)

  • 4-6 symbols in a grid layout
  • Each cell shows candlestick + one overlay indicator
  • Bottom row: RSI comparison across all symbols
  • Symbol list editable via input

multi-timeframe — MTF Analysis (Dash)

  • 4-panel grid: 5m, 15m, 1h, D for same symbol
  • Same indicators computed on each timeframe
  • Confluence summary: "3/4 timeframes bullish"
  • Template: rules/assets/dashboard_multi/app.py

scanner-dashboard — Live Scanner (Dash)

  • Watchlist of 10+ symbols
  • Table showing: Symbol, LTP, RSI, EMA trend, Signal
  • Color-coded rows (green=bullish, red=bearish)
  • Click symbol to show detailed chart
  • Auto-refresh every 30 seconds

Streamlit Dashboard Types

streamlit-single — Single Symbol Dashboard (Streamlit)

  • Sidebar: symbol, exchange, interval, overlay/subplot multiselect
  • st.plotly_chart() for interactive charts
  • st.metric() for LTP, Change, RSI, EMA stats
  • Auto-refresh via checkbox + st.rerun()
  • Template: rules/assets/streamlit_basic/app.py

streamlit-multi — MTF Analysis (Streamlit)

  • 2x2 grid via st.columns(2) for 4 timeframes
  • Candlestick + EMA overlay per timeframe
  • Confluence summary with st.success()/st.error()/st.warning()
  • st.metric() cards for each timeframe trend
  • Template: rules/assets/streamlit_multi/app.py

streamlit-scanner — Scanner Dashboard (Streamlit)

  • Sidebar: scan type selector, run button
  • st.progress() during scan
  • st.dataframe() for results table
  • st.download_button() for CSV export

Running the Dashboard

After creating the app, provide instructions:

Dash:

cd dashboards/{dashboard_name}
python app.py
# Open http://127.0.0.1:8050 in browser

Streamlit:

cd dashboards/{dashboard_name}
streamlit run app.py
# Open http://localhost:8501 in browser

Example Usage

/indicator-dashboard single SBIN /indicator-dashboard multi-timeframe RELIANCE /indicator-dashboard scanner-dashboard /indicator-dashboard streamlit-single SBIN /indicator-dashboard streamlit-multi RELIANCE /indicator-dashboard streamlit-scanner

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