[ LOCAL DESKTOP APPLICATION ]

Stock Analysis Pro

Precision Analysis. Assured Valuation.

Eliminate retail bias and trade with confidence. Stock Analysis Pro is a local, high-performance financial modeling suite combining composite discounted cash flow (DCF) intrinsic valuation, analyst targets, dynamic standard/streaming ML predictions, and institutional risk management frameworks.

Stock Analysis Pro — NVIDIA Corporation dashboard showing LT Score 1.9 Strong Buy, candlestick chart with indicators, DCF valuation panel, and analyst price targets
PLATFORM CAPABILITIES

Eliminate Market Noise.

Stock Analysis Pro deploys robust quantitative models, background-tracked data pipelines, and responsive visualizations to elevate your market analysis workflow.

01 / CHARTING

Interactive Lightweight Charts

TradingView candlestick integrations featuring live tracking intervals, volume histograms, overlays of technical indicators (RSI, Bollinger Bands, and MACD), and period/horizon zoom tools.

02 / PREDICTIONS

Dynamic ML Consensus

Standard ML direction predictions (1d, 3d, 7d, 1mo) paired with Streaming ML analysis running background ticks over short horizons (10m to 4h) paired with sqlite DB caching for instant hydration.

03 / VALUATION

Intrinsic DCF Valuation

Reverse-DCF logic modeling the growth rates implied by current pricing, cross-analyzed against peer multiples (P/E, P/S, EV/EBITDA) and institutional consensus analyst ratings.

04 / RISK MANAGEMENT

Kelly Criteria & VaR

Professional risk controls including Kelly Criterion position sizing, Value at Risk, Volatility Adjustment, and Risk-Adjusted Return calculations to protect capital at every entry.

05 / WATCHLIST

Smart Watchlist

Persist your tracked positions with LT Scores, valuation metrics, and ML consensus pre-loaded from SQLite cache — zero-latency portfolio overview on every launch.

06 / MARKET REGIME

Sector & Market Regime

Track best and worst performing sectors, see live market regime analysis, and compare multiple stocks and indices overlaid on a single chart view.

07 / ENTRY / EXIT

Entry & Exit Verdict

Precision trade timing powered by the composite LT Score engine. Generates actionable BUY, HOLD, and SELL verdicts with defined entry zones, re-assess thresholds, and staged exit targets — removing emotion from every decision.

08 / SPINE

Price Spine Indicator

A proprietary support-resistance backbone that dynamically maps the structural spine of a stock's price action. Visualizes key price levels, trend pivots, and accumulation zones directly on the candlestick chart for high-precision positioning.

COMPOSITE SCORING ENGINE

Algorithmic Valuation. Unified.

The LT Score combines Discounted Cash Flow modeling, Peer Multiples, Technical RSI boundaries, and Analyst upside targets to form an emotion-free, quantitative BUY, HOLD, or SELL rating. Adjust the parameters below to preview the composite rating logic.

-20%
Negative deviation indicates the stock is trading below its intrinsic discounted cash flow value (undervalued).
35
Relative Strength Index. Values under 30 show extreme oversold sentiment (bullish indicator for value plays).
+25%
Average high-tier institutional analyst price targets relative to current market price.
8.2 LT Score
Buy
Valuation Grade Undervalued
Momentum Multiplier Neutral Sentiment
Institutional Consensus Moderate Upside
SPECIFICATIONS & REQUIREMENTS

Local First.
Extremely Reliable.

SYSTEM ARCHITECTURE

Python Flask Backend & Vue.js Frontend

Built using modern Vue 3 Composition APIs and PyInstaller packaging, running lightweight SQLite persistent storage and optional Redis caches.

PIPELINE RELIABILITY

yFinance Live Sync with Alpha Vantage Fallbacks

Data pipelines lazily hydrate stock details, tracking 24/7 background indicators for crypto indices (BTC-USD, ETH-USD) without API rate limit penalties.

RISK MANAGEMENT FRAMEWORK

Kelly Criteria & Value at Risk (VaR)

Built-in risk calculators evaluate position sizes using quantitative parameters like Value at Risk, Volatility Adjustment, and Risk Adjusted Returns.

# Clone and navigate to the project directory
git clone https://github.com/cjguitar/stock_analysis_app.git
cd stock_analysis_app

# Create and activate Python virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate

# Install backend and machine learning dependencies
pip install -r requirements.txt

# Launch the local service host
python run_backend.py
* Running on http://127.0.0.1:5005 (Press CTRL+C to quit)
* SQLite cache hydrated successfully (BTC-USD status: OK)