moira

Moira Computational API: Competitive Analysis & Gap Assessment

Point-in-time audit. This analysis reflects the repository state recorded on 2026-05-25. Current code, public exports, and registered REST routes govern present capability claims.

Date: May 25, 2026 (updated from April 28, 2026) Purpose: Identify gaps, overlaps, and strategic opportunities for Moira as a computational core offering

What Changed Since April 2026

Three major developments close or substantially narrow the gaps identified in the prior analysis:

  1. Batch Operations API implemented — moira/batch.py ships 8 batch functions covering charts, events, transits, returns, progressions (40 techniques), ingresses, void-of-course, and time series. Gap #1 is closed.
  2. C++ native evaluation layer added — A hybrid Python/C++ architecture now underpins SPK polynomial evaluation, fixed star propagation, topocentric correction, and event search, with AVX2 SIMD acceleration and thread-safe caching. This creates a new competitive advantage and changes the “Pure Python” positioning.
  3. Vedic depth expanded substantially — Full Shadbala, Ashtakavarga, Panchanga, Jaimini Karakas, Vedic dignities, Ashtottari Dasha, Yogini Dasha, and a serious Varshaphal/Tajika annual layer are now implemented. The remaining Vedic gap is now concentrated in specialized doctrine breadth and workflow layers such as KP, natal yoga catalogs, Jaimini Chara Dasha, Muhurta workflows, and rectification.

Executive Summary

Moira is astronomically comprehensive, computationally transparent, and now batch-capable in ways that professional astrology software is not. The remaining strategic gaps are:

  1. Vedic Completeness — Core Jyotish infrastructure is strong, and Varshaphal/Tajika is now substantially present; the remaining Vedic gap is concentrated in KP, Jaimini Chara Dasha, natal yoga catalogs, Muhurta workflows, and birth time rectification
  2. REST API / Service Layer — No HTTP interface; still Python-library-only
  3. Report Generation & Templating — No HTML/PDF report output
  4. Research & Statistical Tools — No chart database, filtering, or aggregation layer
  5. Relationship & Synastry Automation — No batch synastry or compatibility scoring
  6. Chart Data Persistence — No storage layer or chart versioning
  7. Interpretive Synthesis — No text interpretation layer (intentional; see positioning)

Moira’s Computational Strengths

Astronomy Foundation (Unmatched)

C++ Native Acceleration Layer (New)

Planetary & Stellar Coverage

Chart Calculation

Predictive Techniques

Vedic Depth (Substantially Expanded)

Batch & Automation Operations (New — Gap #1 Closed)

Observational & Practical Phenomena (Significantly Improved)

Advanced Analytical Capabilities (New / Unique)

Professional Astrology Software Landscape

Tier 1: Desktop Calculation Engines

Swiss Ephemeris (Astro.com)

Solar Fire (Alabe Software)

Kepler (Astro Software)

Astro Gold (Solar Fire for Mac/iOS)

Tier 2: Vedic Astrology Specialists

Parashara’s Light

Jagannatha Hora

Tier 3: API/Service Layer

AstroVisor API

Immanuel (Python)

Kerykeion (Python)

Gap Analysis: Current State

1. Batch & Automation Operations — CLOSED ✓

Status: Fully implemented in moira/batch.py

Remaining: No macro recording or task automation (not needed for a library); no chart filtering or research queries (see Gap #6)

2. Interpretive Synthesis (MEDIUM PRIORITY — intentionally deferred)

Professional Need: Astrologers want computed facts + interpretive guidance

Moira Status: Pure computation; no interpretation

Note: This is the most natural application of an LLM layer over Moira’s output. A moira-interpret plugin consuming the structured API would be a strong product rather than an engine feature.

3. Vedic Technique Depth (LOW PRIORITY — substantially reduced)

Professional Need: Vedic practitioners need specialized tools

Moira Status: Deep Vedic core coverage; Varshaphal/Tajika is now a serious annual-return subsystem, and the remaining gap is primarily breadth, workflow, and synthesis rather than missing foundation

Gap remaining: Moira can now claim substantial Jyotish infrastructure and real annual-return support, but it cannot yet claim parity with Parashara’s Light or Sirius across the full practitioner workflow. The clearest remaining gaps are KP, Jaimini Chara Dasha, natal yoga catalogs, Muhurta workflow, rectification, and additional long-tail refinement within Tajika rather than missing annual architecture.

4. Observational/Practical Phenomena — SUBSTANTIALLY IMPROVED

Professional Need: Simple, clean event-search APIs for common practical uses

Moira Status: Now largely complete; specific naming conventions could improve discoverability

Opportunity: If needed, add a thin next_visible_window() or similar alias over the existing heliacal event search for the remaining discoverability gap.

5. Relationship & Synastry Automation (MEDIUM PRIORITY)

Professional Need: Batch synastry, composite, Davison for multiple chart pairs

Moira Status: Single-pair focus

Opportunity: batch_synastry(pairs, techniques) analogous to batch_progressions() — low implementation cost given the batch pattern is already established.

6. Research & Statistical Tools (MEDIUM PRIORITY)

Professional Need: Filter, aggregate, and analyze chart collections

Moira Status: No research layer

Opportunity: A moira-research module — even a simple in-memory chart collection with filter predicates — would unlock research workflows. The batch API is the computational foundation; persistence and filtering are the missing layer.

7. Report Generation & Templating (MEDIUM PRIORITY)

Professional Need: Generate client-facing reports in multiple formats

Moira Status: No report layer

Opportunity: moira-reports module with Jinja2 templates for HTML output; PDF via wkhtmltopdf or weasyprint.

8. REST API / Service Layer (HIGH PRIORITY)

Professional Need: Integrate Moira into web apps, mobile apps, third-party services

Moira Status: Python library only

Note: The C++ native layer makes this more viable: the hot computation paths are now fast enough for production HTTP latency budgets. A FastAPI server is the natural next step.

Opportunity: moira-server — FastAPI app with endpoints for chart, batch, event, and progression operations; Docker image; OpenAPI schema.

9. Mobile/Lightweight Interface (LOW PRIORITY)

Professional Need: Astrologers want mobile access

Moira Status: Python library; not mobile-friendly

Note: This is a downstream product of the REST API; build the server first.

10. Chart Data Persistence (MEDIUM PRIORITY)

Professional Need: Store, retrieve, and manage chart collections

Moira Status: No persistence layer

Opportunity: Define a canonical chart JSON schema; create moira-storage with SQLite backend.

11. NEW: Concurrency & Thread Safety at Scale (MEDIUM PRIORITY — newly identified)

Professional Need: Multi-threaded servers and parallel chart processing

Moira Status: Substantially addressed; closure work is now documentation and audit completion

Opportunity: Treat this as a production-guidance and server-pattern concern rather than a missing substrate concern; prefer a documented stable-reader deployment model and process-worker isolation where needed.

12. NEW: Lunar Phase API — CLOSED ✓

Professional Need: Simple find_lunar_phases() call for phase calendars

Moira Status: Surfaced on the predictive/transit layer

Impact: Practitioner discoverability gap is closed.

13. NEW: Chart Visualization / SVG Output (LOW PRIORITY — newly identified)

Professional Need: Visual chart wheel output

Moira Status: No visualization layer

Note: Moira’s positioning as a computation core makes this a plugin concern, but chart SVG output would significantly broaden its appeal for developers.

Moira’s Unique Competitive Advantages

1. Hybrid Python/C++ Architecture (New)

2. Astronomical Transparency

3. Modern Astronomical Standards

4. Comprehensive Vedic Coverage

5. Sovereign Fixed Star Registry

6. Generalized Visibility Engine (V5 — Unique)

7. Deep Dispositorship & Condition Profiling (Unique)

8. Harmogram Analysis (Unique)

9. Explicit Policy Control

Strategic Recommendations

Tier 1: High-Impact Gaps (Implement Next)

  1. REST API Service Layer
    • FastAPI server with endpoints for all major Moira functions
    • OpenAPI schema for discoverability
    • Docker container for easy deployment
    • Consume the documented stable-reader deployment model during service packaging
    • Impact: Opens Moira to web/mobile developers; enables hosted service revenue
    • Effort: Medium (3-4 weeks given C++ layer already handles hot path)
  2. Batch Synastry
    • batch_synastry(pairs, techniques) following the established batch pattern
    • Synastry filtering helper
    • Impact: Closes a notable gap vs. Solar Fire batch workflows
    • Effort: Low (1 week — batch infrastructure already in place)
  3. Thread Safety Documentation & GIL Release Audit
    • Public threading contract, native GIL audit, rebuild proof, runtime verification, and adversarial verification are now in place
    • Remaining work is not engine closure; it is correct adoption of the documented deployment model in future REST/service packaging
    • Impact: Important for production operations, but no longer an open engine substrate gap
    • Effort: Low

Tier 2: Vedic & Professional Workflow (Implement Second)

  1. Varshaphal (Annual Charts)
    • Remaining work is refinement: additional long-tail Tajika yogas, house-based/monthly/daily period variants, and stronger interpretive/report synthesis
    • Impact: No longer a foundation gap; now a completion and practitioner-polish track
    • Effort: Medium-high (incremental doctrine work, not a missing base implementation)
  2. Chart Persistence & Storage
    • Canonical chart JSON schema
    • SQLite backend with moira-storage module
    • Impact: Enables research and client management workflows
    • Effort: Medium (2-3 weeks)
  3. Muhurta (Electional Astrology)
    • Integrate Panchanga + planetary conditions into electional scoring
    • Impact: Closes an important practitioner workflow gap, but does not by itself complete Vedic parity without KP, Tajika depth, and yoga catalog work
    • Effort: High (4-6 weeks; domain-specific logic)
  4. Research & Filtering Tools
    • In-memory chart collection with filter predicates
    • Statistical aggregation helpers
    • Impact: Enables research workflows on top of batch API
    • Effort: Medium (2-3 weeks)

Tier 3: Optional Enhancements (Implement Third)

  1. KP Astrology (Krishnamurti Paddhati)
    • Sub-lord system, KP significators
    • Impact: Specialized Vedic niche; not the primary gap
    • Effort: High (4-6 weeks; distinct sub-lord calculation chain)
  2. Report Generation
    • Jinja2 template system for HTML/PDF
    • Impact: Enables client-facing deliverables
    • Effort: Medium (3-4 weeks)
  3. Chart Visualization / SVG Output
    • Plugin module with wheel rendering
    • Impact: Widens developer appeal
    • Effort: Medium (3-4 weeks)
  4. Interpretive Synthesis Layer (optional plugin)
    • LLM-backed technique synthesis over structured Moira output
    • Impact: Differentiates from pure computation; large practitioner market
    • Effort: High (8-12 weeks); best implemented as moira-interpret using Claude API

Market Positioning Strategy

Current Position

Moira is a transparent, auditable, high-performance computational engine with a Python-native API, sovereign C++ core, and comprehensive coverage of both Western and Vedic techniques. The batch API is now production-capable.

  1. “The Computational Core for Professional Astrology”
    • Offer Moira as a service layer (REST API) for app developers
    • Compete with Swiss Ephemeris on transparency + modern standards + Python-native accessibility
    • Compete with Solar Fire on batch automation (batch API is now equivalent in coverage if not UX polish)
  2. “The Unified Western + Vedic Engine with Performance”
    • Deepest Vedic coverage of any Python library (Shadbala, Ashtakavarga, Jaimini, Panchanga, Ashtottari, Yogini)
    • Full Western technique suite in the same library
    • C++ core means it is not a convenience trade-off
  3. “The Auditable Alternative to Proprietary Software”
    • Emphasize sovereign Python/C++ architecture, inspectable code, documented residuals
    • Machine contract governance for AI-assisted development
    • Target researchers, educators, and practitioners who value transparency
    • Open-source core + optional commercial services (hosting, support, moira-interpret)

Competitive Comparison Matrix

Feature Moira Swiss Ephemeris Solar Fire Kepler Parashara’s Light
Astronomy ★★★★★ ★★★★☆ ★★★★☆ ★★★★☆ ★★★☆☆
Transparency ★★★★★ ★☆☆☆☆ ★★☆☆☆ ★★☆☆☆ ★★☆☆☆
Performance (C++ core) ★★★★☆ ★★★★★ ★★★★☆ ★★★★☆ ★★★☆☆
Python API ★★★★★ ★★☆☆☆ ☆☆☆☆☆ ☆☆☆☆☆ ☆☆☆☆☆
Western Techniques ★★★★★ ★★★★☆ ★★★★★ ★★★★★ ★★☆☆☆
Vedic Techniques ★★★★☆ ★★☆☆☆ ★★☆☆☆ ★★☆☆☆ ★★★★★
Batch Operations ★★★★☆ ★★☆☆☆ ★★★★☆ ★★★★☆ ★★★☆☆
Observational Phenomena ★★★★☆ ★★★☆☆ ★★★☆☆ ★★★☆☆ ★★☆☆☆
Report Generation ☆☆☆☆☆ ★★☆☆☆ ★★★★☆ ★★★★☆ ★★★☆☆
REST API ☆☆☆☆☆ ★★★☆☆ ☆☆☆☆☆ ☆☆☆☆☆ ☆☆☆☆☆
Research Tools ★☆☆☆☆ ★★☆☆☆ ★★★★☆ ★★★★★ ★★★☆☆
Harmogram / Advanced Analysis ★★★★★ ☆☆☆☆☆ ★★☆☆☆ ★★★☆☆ ☆☆☆☆☆
Visibility Engine ★★★★★ ★★☆☆☆ ★★☆☆☆ ★★☆☆☆ ☆☆☆☆☆

Note: “Performance (C++ core)” reflects computational throughput on SPK evaluation. Moira’s Python surface adds overhead vs. direct C library calls, but the native evaluation layer is now competitive for hot-path operations.

Conclusion

Moira’s foundation has materially strengthened since April 2026. The batch operations gap is closed. The Vedic depth gap is substantially reduced. The C++ native layer adds a competitive performance dimension that no other Python-native astrology library possesses.

The remaining strategic priorities in order are:

  1. REST API — transforms Moira from a library into a platform
  2. Batch synastry — low-effort gap closure using existing batch infrastructure
  3. Muhurta / practitioner workflow — now the clearer Vedic workflow gap after the Varshaphal annual layer
  4. Chart persistence + research tools — unlocks the research and data workflows that Solar Fire and Kepler own today
  5. REST deployment packaging discipline — apply the documented stable-reader and no-hot-swap threading model when the server layer is built

The opportunity is no longer solely to offer Moira as a Python library. The C++ layer, batch API, and comprehensive technique coverage now support positioning Moira as a production computational platform that application developers, researchers, and Vedic/Western practitioners can build on top of — either as a library, a REST service, or a hosted API.

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