dsm@terminal:~/portfolio/specs

$ ./technical_specifications

comprehensive system specifications & technical details

$ ./specs --asset_screener
=== ASSET SCREENER TECHNICAL SPECS ===
system requirements:
├── hardware: 4GB RAM, 2GB storage
├── software: python 3.8+, cross-platform (Linux, macOS, Windows)
└── network: yahoo finance api access
performance metrics:
├── processing: 500+ assets in <40 min
├── accuracy: TBD
├── memory: <500MB for full analysis
└── scalability: potentially supports 2000+ assets
ml capabilities:
├── models: linear regression, xgboost, random forest
├── algorithms: monte carlo, technical analysis
├── indicators: 50+ technical indicators
└── ensemble: risk-weighted forecasts
data integration:
├── sources: yahoo finance, alpha vantage
├── formats: csv, json, database output
└── assets: 9 classes (stocks, bonds, commodities, crypto, forex, indexes, futures, real estate, alternative investments)
$ ./specs --portfolio_analyzer
=== PORTFOLIO ANALYZER TECHNICAL SPECS ===
system requirements:
├── hardware: 8GB RAM, 5GB storage
├── software: python 3.8+, cross-platform (Linux, macOS, Windows)
└── network: yahoo finance api access
performance metrics:
├── processing: 5-year analysis in <30 minutes
├── data points: 1200+ trading days minimum
├── memory: <1GB for portfolio analysis
└── scalability: limited only by available memory
risk capabilities:
├── metrics: VaR, CVaR, Sharpe, Sortino, beta, alpha
├── benchmarks: 25+ market indices and alternatives
├── scenarios: stress testing and monte carlo
└── timeframes: 5-year analysis (1200+ trading days)
analysis algorithms:
├── time series: pct_change, cumprod, rolling windows
├── statistics: correlation, beta calculation, volatility
└── optimization: portfolio theory, diversification metrics
$ ./specs --forecasting_suite
=== FORECASTING SUITE TECHNICAL SPECS ===
system requirements:
├── hardware: 16GB RAM, 10GB storage
├── software: python 3.8+, cross-platform (Linux, macOS, Windows)
└── network: multi-api integration
performance metrics:
├── processing: complex forecast in <15 min (each)
├── accuracy: TBD
├── memory: <2GB for full model training
└── scalability: supports 1000+ time series
ml capabilities:
├── models: 10+ (LSTM, Prophet, XGBoost, SVM)
├── indicators: 200+ technical indicators
├── methods: Holt-Winters, arima, garch
└── validation: backtesting framework
forecasting algorithms:
├── time series: exponential smoothing, arima
├── machine learning: neural networks, ensemble methods
├── sentiment: nlp processing, web scraping
└── statistical: pattern recognition, anomaly detection
$ ./specs --business_intelligencer
=== BUSINESS INTELLIGENCER TECHNICAL SPECS ===
system requirements:
├── hardware: 32GB RAM, 50GB storage
├── software: python 3.8+, cross-platform (Linux, macOS, Windows)
└── network: api access for data sources
performance metrics:
├── processing: e.g. 99K transactions in <10 min (data layer); full 72-module run: ~30–45 min per month
├── data enrichment: e.g. 159MB processed dataset
├── memory: <8GB for full analysis
└── scalability: supports 1M+ transactions
analysis capabilities:
├── modules: 72 automated analysis types
├── segmentation: multi-factor customer clustering
├── metrics: unit economics, operational efficiency
└── validation: 95% synthetic data accuracy
data processing:
├── ingestion: multi-format file processing
├── enrichment: synthetic data generation
├── validation: mathematical impossibility detection
└── algorithms: cohort analysis, trend detection
$ ./specs --business_analyzer
=== BUSINESS ANALYZER TECHNICAL SPECS ===
system requirements:
├── hardware: 24GB RAM, 20GB storage
├── software: python 3.8+, cross-platform (Linux, macOS, Windows)
└── network: multiple financial data apis
performance metrics:
├── processing: complex analysis in ~15–30 min (full pipeline)
├── validation: multi-layer data validation
├── memory: <4GB for full workflow
└── scalability: enterprise-scale processing
analysis capabilities:
├── modules: 18+ specialized financial scripts
├── ratios: 50+ calculated financial metrics
├── models: dcf, valuation, forecasting
└── reporting: professional pdf/excel outputs
8-stage pipeline:
├── stage 1: data preparation & validation
├── stage 2: orchestration & analysis engine
├── stage 3: financial analysis execution
├── stage 4: intelligence processing
├── stage 5: ml forecasting & benchmarking
├── stage 6: strategic intelligence synthesis
├── stage 7: output orchestration
└── stage 8: delivery & packaging
$ ./specs --project_evaluator
=== PROJECT EVALUATOR TECHNICAL SPECS ===
system requirements:
├── hardware: 16GB RAM, 15GB storage
├── software: python 3.8+, cross-platform (Linux, macOS, Windows)
└── network: api access for llms and research
performance metrics:
├── processing: full evaluation ~15–25 min (fast mode); ~30–75 min (standard/high-reliability)
├── reliability: 70% consensus validation
├── memory: <3GB for evaluation workflow
└── scalability: enterprise-scale problems
ai capabilities:
├── models: 5 LLMs (Gemini, Llama, Claude, Chatgpt, Samos)
├── validation: multi-run consensus checking
├── research: tavily api integration
└── analysis: 6-stage decision pipeline
6-stage pipeline:
├── stage 1: problem analysis
├── stage 2: solution generation & LLM weighted consensus
├── stage 3: financial enrichment & npv/irr
├── stage 4: strategic planning & context
├── stage 5: task sequencing & automation
└── stage 6: automated execution & hand-off
$ ./specs --technical_stack
=== TECHNICAL STACK OVERVIEW ===
core technologies:
├── backend: python 3.8+, fastapi, flask
├── data processing: pandas, numpy, scipy
├── machine learning: scikit-learn, xgboost, TensorFlow (incl. Keras)
├── deep learning: pytorch
├── databases: sqlalchemy, postgresql
└── apis: restful, websocket
data sources:
├── financial: yahoo finance, alpha vantage, bloomberg
├── alternative data: news apis, social media
├── economic: fred, world bank, oecd
└── real-time: streaming apis, websocket connections
development tools:
├── version control: git, github
├── ci/cd: github actions, docker
├── monitoring: logging, performance metrics
└── testing: unit tests, integration tests
$ ./specs --deployment
=== DEPLOYMENT & SCALABILITY ===
infrastructure:
├── cloud: aws, google cloud, azure
├── containers: docker, kubernetes
├── orchestration: kubernetes, docker swarm
└── serverless: lambda, cloud functions
scalability features:
├── horizontal scaling: auto-scaling groups
├── load balancing: nginx, haproxy
├── caching: redis, memcached
└── cdn: cloudflare, aws cloudfront
security:
├── authentication: oauth2, jwt
├── encryption: ssl/tls, data encryption
├── compliance: gdpr, soc2
└── monitoring: security logging, intrusion detection
$ ./specs --api_specs
=== API SPECIFICATIONS ===
rest api endpoints:
├── GET /api/v1/assets - retrieve asset data
├── POST /api/v1/analyze - submit analysis request
├── GET /api/v1/portfolio - portfolio analysis
├── GET /api/v1/forecast - market forecasts
└── GET /api/v1/reports - generated reports
response formats:
├── json: standard api responses
├── csv: data export format
├── pdf: professional reports
└── excel: spreadsheet exports
rate limiting:
├── api key authentication
├── rate limits per endpoint
└── usage tracking and monitoring
$ ./specs --benchmarks
=== PERFORMANCE BENCHMARKS ===
processing speed:
├── asset screening: 500+ assets in <40 min
├── portfolio analysis: e.g. 5-year analysis in ~15–30 min
├── ml training: model updates in <30 min
└── report generation: pdf creation in <1 min
accuracy metrics:
├── prediction accuracy: TBD
├── risk assessment: TBD
├── market timing: TBD
└── portfolio optimization: TBD
system reliability:
├── uptime: 99.9% sla
├── error rate: <0.1%
├── data freshness: real-time updates
└── backup: daily automated backups
$ ./specs --contact
=== CONTACT & SUPPORT ===
support channels:
├── email: dsanchom@protonmail.com
├── documentation: comprehensive API docs (TBD)
├── GitHub issues: bug reports and feature requests (TBD)
└── slack channel: real-time support (TBD)
enterprise support:
├── dedicated account manager (TBD)
├── 24/7 priority support (TBD)
├── custom development (TBD)
└── training and onboarding (TBD)
enterprise licensing:
├── flexible licensing models (TBD)
├── volume discounts (TBD)
├── custom integrations (TBD)
└── white-label solutions (TBD)
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