SharpEdge Systems

SharpEdge Systems is a systematic market data and regime analysis engine focused on clarity, discipline, and decision-quality over noise.

Built from a background in precision craft, this project applies the same principles of sharp tools, clean structure, and deliberate execution to financial data:

• Automated multi-source market data ingestion (Alpaca, FINRA, FRED)
• Feature engineering and liquidity/regime classification
• Backtested signal generation with calibrated options execution logic
• Deterministic trade cards designed to reduce emotional decision-making

The goal is simple:

Create a reliable edge through structure, not speculation.

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Architecture Overview

Pipeline layers:

1. Truth Layer – Raw market data ingestion and normalization
2. Feature Layer – Derived signals, regimes, and structural context
3. Decision Layer – Backtested rules, calibrated DTE selection, and trade plans

All processes are automated via scheduled workflows and reproducible SQLite state.

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Purpose

SharpEdge Systems is both:

• A personal discipline framework for systematic trading
• A production-style data engineering portfolio project

It represents the transition from physical precision craft → data system design.

Local Quality Gate

Ruff may require a Rust build on Android/Termux, so this repo includes a stdlib-only fallback quality gate:

python scripts/utils/lint_python.py scripts

Optional stricter style audit, currently advisory while old debt is cleaned up:

python scripts/utils/lint_python.py scripts --strict-style

FINRA Runtime Control

The FINRA darkpool overlay uses persisted ats_weekly state. Routine runs rebuild daily overlays from SQLite and skip FINRA network calls while the cache is fresh.

Useful overrides:

FINRA_FORCE_REFRESH=1 python scripts/ingest_finra_darkpool_overlay.py
FINRA_CACHE_TTL_HOURS=24 python scripts/ingest_finra_darkpool_overlay.py
FINRA_REFRESH_LOOKBACK_WEEKS=8 python scripts/ingest_finra_darkpool_overlay.py

Layer 1 Cache + State Controls

Layer 1 ingestion emits state breadcrumbs under outputs/health/*_state.json. Routine runs now avoid unnecessary network or recompute work when persisted state is fresh.

Useful overrides:

DAILY_FORCE_REFRESH=1 python scripts/ingest_spy_daily.py
DAILY_CACHE_TTL_HOURS=6 python scripts/ingest_spy_daily.py
DAILY_INCREMENTAL_PERIOD=30d python scripts/ingest_spy_daily.py

FRED_FORCE_REFRESH=1 python scripts/ingest_fred_overlays.py
FRED_MAX_LAG_DAYS=2 python scripts/ingest_fred_overlays.py

OPTIONS_POSITIONING_FORCE_REBUILD=1 DTE_MIN=0 DTE_MAX=1 \
  python scripts/aggregate_options_positioning_metrics.py

Operator Brief MVP

The operator brief is a thin compression layer over the existing local-only artifacts. It gives one fast stand-down / monitor / review summary without changing the safety contract.

python scripts/agents/operator_brief.py

Outputs:

• outputs/operator_brief.json
• outputs/operator_brief.txt
• outputs/operator_watchlist.json
• outputs/operator_journal_append.jsonl
• outputs/operator_session_review.json
• outputs/operator_session_review.txt
• outputs/morning_open_dashboard.json
• outputs/morning_open_dashboard.txt
• outputs/robinhood_beta_execution.json
• outputs/robinhood_beta_execution.txt

Extra operator artifacts:

python scripts/agents/operator_session_review.py
python scripts/agents/morning_open_dashboard.py
python scripts/agents/robinhood_beta_execution.py

Design notes:

• docs/operator_breadcrumbs.md
• docs/robinhood_beta_execution.md

Results Summary

Metric	Value
Signals Generated	TBD
Win Rate	TBD
Avg Expectancy (R)	TBD
Max Drawdown	TBD
Latest Data Date	2026-02-06