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Pure Python Pipeline

Welcome to the Pure Python Pipeline documentation. This pipeline provides a high-performance, deterministic alternative to AI-based portfolio analysis.

What is the Pure Python Pipeline?

The Pure Python Pipeline is a high-performance, deterministic alternative to AI-based portfolio analysis that delivers:

  • 10-20x faster execution
  • 100% cost reduction (zero LLM calls)
  • Deterministic results (same inputs = same outputs)
  • Full integration with deep analysis, A+ discovery, backtesting, and reporting

Components

The pipeline consists of four main components:

  1. Portfolio Deep Analyzer - Pure Python scoring engine
  2. A+ Discovery Integrator - Opportunity identification
  3. Backtesting Pipeline Connector - Performance validation
  4. Python Report Generator - Template-based reporting

View detailed component documentation →

Performance

Speed Comparison

Component AI-Based Python-Based Improvement
Deep Analysis (per holding) 30-60s <1s 30-60x faster
Total (5 holdings) 5-10 min 10-20s 15-30x faster

Cost Comparison

Component AI-Based Python-Based Savings
Deep Analysis (per holding) $0.05-0.10 $0.00 100%
Total (5 holdings) $0.65-1.35 $0.00 100%

View detailed performance metrics →

Quick Start

Python
from finwiz.scoring.portfolio_deep_analyzer import analyze_portfolio_with_python
from finwiz.integration.aplus_discovery_integrator import integrate_aplus_discovery_with_deep_analysis
from finwiz.integration.backtesting_pipeline_connector import connect_backtesting_to_discovery_results
from finwiz.reporting.python_report_generator import generate_python_report

# Run complete pipeline
analysis_results = analyze_portfolio_with_python(holdings, session_id)
discovery_results = integrate_aplus_discovery_with_deep_analysis(session_id)
backtesting_results = connect_backtesting_to_discovery_results(session_id)
report_path = generate_python_report(portfolio_review, analysis_results, session_id)

View complete usage guide →

Documentation Structure

Explanations (Understanding)

How-to Guides (Problem-solving)

Reference (Information)

Getting Started

  1. Read the overview to understand the pipeline
  2. Learn about components to see how it works
  3. Follow the how-to guide to implement it
  4. Review best practices to optimize your implementation