MOIRA_COMPETITIVE_ANALYSIS_2026 05 25 - TheDaniel166/moira GitHub Wiki

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:

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.
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.
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:

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
REST API / Service Layer — No HTTP interface; still Python-library-only
Report Generation & Templating — No HTML/PDF report output
Research & Statistical Tools — No chart database, filtering, or aggregation layer
Relationship & Synastry Automation — No batch synastry or compatibility scoring
Chart Data Persistence — No storage layer or chart versioning
Interpretive Synthesis — No text interpretation layer (intentional; see positioning)

Moira's Computational Strengths

Astronomy Foundation (Unmatched)

JPL DE441 kernel with explicit light-time iteration, gravitational deflection, annual aberration
IAU 2006 frame bias, precession, nutation with full 1365 lunisolar + 687 planetary terms
Topocentric parallax (WGS-84) and atmospheric refraction
Validation against ERFA/SOFA with documented residuals
Python-native API with inspectable intermediate stages

C++ Native Acceleration Layer (New)

Hybrid Python/C++ architecture — Python surface backed by sovereign C++ computational core
AVX2/SIMD-accelerated Chebyshev evaluation (SPK Type 2/3 segments)
Lagrange interpolation in C++ (SPK Type 13 segments — comet and irregular body orbits)
Native NAIF DAF/SPK reader — Moira owns the file format parsing path
Native NAIF LSK parser and physical delta-T — sovereign time conversion chain
Thread-safe evaluator hierarchy with mutex-guarded single-element cache
Composite evaluator DAG (RelativeEvaluator, SumEvaluator, TopocentricEvaluator) — center-chaining and topocentric correction in C++
FixedStarEvaluator — proper motion, parallax, and radial velocity propagation in C++
Thread pool for parallel event search — search_pool.hpp
This architecture is superior to thin wrappers around an external C library (e.g., pyswisseph): Moira's Python API is backed by a fully sovereign C++ kernel, not delegated to a third-party binary

Planetary & Stellar Coverage

1,809 named fixed stars with proper motion, parallax, epoch propagation
Classical asteroids (Ceres, Pallas, Juno, Vesta) + Centaurs (Chiron, Pholus, Chariklo, Asbolus, Hylonome)
Trans-Neptunians (Ixion, Quaoar, Varuna, Orcus)
Uranian/Hamburg bodies (Cupido through Poseidon)
Lunar nodes, apsides, True/Mean Lilith
Variable stars (Algol phase engine)
Multiple star systems (Sirius AB, Alpha Centauri AB)
User-supplied .bsp kernels for 887,000+ numbered minor planets
Comets with native NAIF ID registry
Asteroid families with resonance network mapping

Chart Calculation

17 house systems (Placidus, Koch, Regiomontanus, Campanus, Morinus, Porphyry, Whole Sign, Equal, APC, Sunshine, etc.)
22 zodiacal aspects with applying/separating/stationary motion detection
Declination parallels & contra-parallels
Antiscia & contra-antiscia
21 aspect patterns (T-Square, Grand Trine, Yod, Kite, Mystic Rectangle, Stellium, etc.)
Full midpoint matrix with 90°/45°/22.5° dial projections
Traditional dignities (domicile, exaltation, triplicity, terms, face, sect, hayz, Almuten Figuris)
499 Arabic Parts with dependency graphs
36-decan research catalog with source-reconstructed names and planetary faces; the former ruling-star assignments fail closed and the unsupported Hermetic night-hour experiment was removed on 2026-07-26
Galactic house system with boundary profiles

Predictive Techniques

Progressions — 40 techniques covering secondary, tertiary, minor, solar arc, Naibod, ascendant arc, vertex arc, planetary arc, quotidian solar/lunar, plus full converse forms
Primary directions (Placidus semi-arc, mundane, speculum)
Returns (solar, lunar, planetary)
Time lords (profections, Firdaria, Zodiacal Releasing, Hyleg/Alcocoden)
Vedic techniques (Vimshottari Dasha, Ashtottari Dasha, Yogini Dasha, sidereal positions, 27 nakshatras, Varga charts)

Vedic Depth (Substantially Expanded)

Vimshottari Dasha with nakshatra balance
Ashtottari Dasha and Yogini Dasha (alternate systems)
Ashtakavarga — Bhinnashtakavarga (per-planet) and Sarvashtakavarga (chart-wide); sign strength profiles; transit strength
Shadbala — full six-strength computation (Sthana Bala, Dig Bala, Kala Bala, Chesta Bala, Naisargika Bala, Drig Bala) with tier classification
Jaimini Karakas — full 7-planet and 8-planet schemes with atmakaraka, karaka pairs, condition profiles
Panchanga — Tithi, Yoga, Karana, Vara with condition profiles and policy control
Vedic Dignities — exaltation, debilitation, Mula Trikona, own signs, natural friendship/enmity, compound relationships
Nakshatras — 27-nakshatra system with nakshatra_of() and all_nakshatras_at()
Divisional charts (Varga) via sidereal module

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

batch_charts() — multi-chart assembly across dates, locations, body sets
batch_events() — general event search (transits, aspect transits, declination transits, ingresses, stations)
batch_transits() — multi-body longitude transit search
batch_returns() — solar, lunar, planetary return JDs in bulk
batch_progressions() — 40 progression techniques in batch with per-request failure isolation
find_all_ingresses() — chronologically merged ingresses for many bodies
void_periods_all_planets() — void-of-course windows for any moving body across a range
planet_time_series() — ephemeris sampling for one body across many epochs
All batch functions use per-request BatchFailure isolation: one failed chart does not abort the run

Observational & Practical Phenomena (Significantly Improved)

Eclipses (NASA-canon contact solver, Saros series, local circumstance)
Heliacal phenomena V5 — generalized visibility engine: multi-criterion (naked eye/binoculars/telescope), light pollution classes (Bortle scale), atmospheric extinction models, moonlight policy, twilight model, lunar crescent visibility
Parans (paranatellonta field analysis)
Occultations (lunar occultation of stars/planets)
Retrograde stations — find_stations() exposed in batch
Astrocartography (ACG lines for all planets)
Local Space charts
Gauquelin sectors
Galactic coordinates (equatorial-to-galactic transform)
Void-of-course Moon — void_of_course_window(), is_void_of_course(), next_void_of_course(), void_periods_in_range(); generalized to any body via void_periods_all_planets()
Sign ingresses — find_ingresses() and find_all_ingresses() (batch)
Proximity/conjunction events — proximity_events_in_range() and solar_condition_events_in_range()
Planetary phenomena — planet_phenomena_at(), PlanetPhenomena
Keplerian orbital elements — orbital_elements_at(), distance_extremes_at()
Planetary nodes — geometric orbital nodes via planetary_node(), all_planetary_nodes()

Advanced Analytical Capabilities (New / Unique)

Harmogram analysis — harmogram traces, intensity function spectra, zero-aries parts harmonic vectors, harmogram strength projection; no commercial competitor exposes this in an API
Dispositorship system — full dispositorship chain calculation, condition network profiles, subsystem profiles, comparison bundles; research-grade analysis unavailable in any Python astrology library
Triplicity scoring — configurable triplicity doctrine with participation rules
Asteroid family resonance — family identification, resonant aspect detection, resonance network mapping

Professional Astrology Software Landscape

Tier 1: Desktop Calculation Engines

Swiss Ephemeris (Astro.com)

Strength: Industry standard; used by most professional software
API: C/Fortran library; thin Python wrappers available (pyswisseph)
Coverage: Planets, asteroids, fixed stars, nodes, Lilith
Techniques: Basic aspects, houses, progressions, returns
Weakness: Closed-source; limited transparency; no Vedic depth; Python wrappers are thin bindings to a black box
Market Position: De facto standard for Western astrology

Solar Fire (Alabe Software)

Strength: Calculation-first; research-grade accuracy; batch automation
Features:
- Animated charts (real-time clock, time-step animation)
- Astrologer's Assistant (task automation/macro recording)
- Astro-Locality Mapping (eclipse paths, local space lines)
- Batch processing for transits, progressions, returns
- 1000+ asteroids
- Extensive report generation
Weakness: Proprietary; Windows-first; no API
Market Position: Professional astrologer standard for research & batch work

Kepler (Astro Software)

Strength: Research-grade; statistical analysis; AstroMaps
Features:
- AstroMaps (Treasure Maps, local space, horizon, geodetic, zodiac sign maps)
- 90° dials (regular, biwheel, triwheel, quadwheel)
- Antiscia/contra-antiscia charts
- Arabic Parts wheels
- Arc Transform charts
- Asteroids (4 major + Chiron in wheel; 1000 in list; 38,000 optional)
- Aspect listings with VOC
- Assumptionless research tools
- Batch processing
Weakness: Proprietary; expensive; limited Vedic
Market Position: Research & statistical analysis

Astro Gold (Solar Fire for Mac/iOS)

Strength: Mobile-first; elegant UI; file compatibility with Solar Fire
Features:
- Transit reports with custom date conditions
- Progressions, synastry, composite charts
- Customizable reports
- iOS/macOS native
Weakness: Limited to Apple ecosystem; less powerful than Solar Fire desktop
Market Position: Professional astrologers on Mac/iPad

Tier 2: Vedic Astrology Specialists

Parashara's Light

Strength: Comprehensive Vedic suite; accurate calculations
Features:
- Vimshottari Dasha with nakshatra balance
- Varshaphal (annual charts)
- Ashtakavarga
- Divisional charts (Varga)
- KP astrology
- Muhurta (electional astrology)
- Birth time rectification
- Database/research tools
- Customizable reports
Weakness: Proprietary; limited Western techniques; no API
Market Position: Vedic practitioners & researchers

Jagannatha Hora

Strength: Free; Vedic-focused; active development
Features:
- Vimshottari Dasha
- Varshaphal
- Ashtakavarga
- Divisional charts
- KP astrology
- Muhurta
- Nakshatra analysis
Weakness: Limited Western techniques; no API; smaller user base
Market Position: Budget-conscious Vedic practitioners

Tier 3: API/Service Layer

AstroVisor API

Strength: Professional API; Swiss Ephemeris engine; 95+ tools
Features:
- Natal charts, BaZi, Jyotish
- REST API
- Batch processing
- Custom calculations
Weakness: Proprietary; closed-source; pricing model
Market Position: Developers integrating astrology into apps

Immanuel (Python)

Strength: Open-source Python wrapper; Swiss Ephemeris + Astro.com data
Features:
- JSON-formatted chart data
- Human-readable output
- Python-native
Weakness: Limited to Swiss Ephemeris; no Vedic; minimal techniques; thin wrapper with no sovereign computation
Market Position: Python developers wanting quick chart generation

Kerykeion (Python)

Strength: Open-source; SVG chart generation; Python-native
Features:
- Birth charts, composite, transit charts
- SVG output
- Data extraction
Weakness: Limited techniques; no Vedic; minimal validation; no batch or predictive API
Market Position: Python developers wanting chart visualization

Gap Analysis: Current State

1. Batch & Automation Operations — CLOSED ✓

Status: Fully implemented in moira/batch.py

✓ batch_charts() — multi-chart assembly
✓ batch_events() — transits, aspect transits, declination transits, ingresses, stations
✓ batch_transits() — longitude transit search
✓ batch_returns() — solar/lunar/planet returns
✓ batch_progressions() — 40 progression techniques with full converse forms
✓ find_all_ingresses() — multi-body ingress merge
✓ void_periods_all_planets() — VOC windows for any body
✓ planet_time_series() — ephemeris time series sampling
✓ Per-request BatchFailure isolation — one failure does not abort the run

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

✗ No interpretive text generation
✗ No technique synthesis (e.g., "Mars in 8th + Pluto aspects = X")
✗ No report templates
✗ No client-facing output formatting

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

✓ Vimshottari Dasha
✓ Ashtottari Dasha
✓ Yogini Dasha
✓ Ashtakavarga (Bhinna and Sarva)
✓ Shadbala (full six-strength: Sthana, Dig, Kala, Chesta, Naisargika, Drig)
✓ Jaimini Karakas (7 and 8-planet)
✓ Panchanga (Tithi, Yoga, Karana, Vara)
✓ Vedic dignities, compound relationships
✓ Nakshatras (27 lunar mansions)
✓ Divisional charts (Varga; Shodashvarga depth is present)
✓ Tajika / Varshaphal annual-return doctrine — sidereal annual return timing, annual chart vessel, Muntha, Sahams, Tajika aspects/yogas, Varshesha, Tajika strength, Mudda Dasha, Tāsīra timing, annual judgement, and topic-specific channels are present
✗ KP astrology (Krishnamurti Paddhati) — no KP cusp workflow, star-lord / sub-lord chain, ruling planets, or significator logic
✗ Jaimini Chara Dasha — Karakas are implemented, but the time-lord system itself is absent
✗ Natal yoga catalog — Panchanga nitya yogas exist, but named chart-yoga families (Raja, Dhana, Nabhasa, etc.) are not surfaced
✗ Muhurta (electional astrology) — no electional scoring or practitioner workflow layer over Panchanga and planetary conditions
✗ Birth time rectification — not implemented
✗ Bhava Chalit — not implemented

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

✓ Eclipses (present)
✓ Heliacal phenomena V5 with full visibility policy (light pollution, extinction, moonlight, observer conditions)
✓ Occultations (present)
✓ Retrograde stations via find_stations() in batch
✓ Sign ingresses via find_ingresses() / find_all_ingresses()
✓ Proximity/conjunction events via proximity_events_in_range()
✓ Void-of-course Moon with dedicated API
✓ Planetary phenomena via planet_phenomena_at()
✓ Lunar phases — exposed via find_lunar_phases(jd_start, jd_end) on the predictive/transit surface
✓ Planetary visibility convenience — visibility_tonight() and is_visible_tonight() now expose the single-epoch practitioner-facing verdict directly
✗ Visibility window search convenience — there is still no thin next_visible_window()-style helper over the full heliacal search subsystem

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

✓ Synastry (present)
✓ Composite (present)
✓ Davison (present)
✗ Batch synastry — not implemented
✗ Synastry filtering (e.g., "show all Mars-Venus aspects across 100 pairs")
✗ Compatibility scoring — not implemented

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

✗ No chart database
✗ No filtering (e.g., "all charts with Sun in 8th")
✗ No statistical aggregation
✗ No correlation analysis
✗ No chart comparison tools

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

✗ No HTML/PDF report generation
✗ No report templates
✗ No customizable output formatting
✗ No multi-chart report layouts (triwheel, quadwheel)

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

✗ No REST API
✗ No Docker container
✗ No cloud deployment
✗ No rate limiting / auth
✗ No async/concurrent request handling

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

✗ No mobile app
✗ No lightweight web UI
✗ No offline-capable mobile library

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

✗ No chart storage format
✗ No database schema
✗ No import/export (except manual)
✗ No chart versioning

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

✓ C++ evaluator layer uses mutex-guarded caching — thread-safe per evaluator
✓ search_pool.hpp — native thread pool for event searches
✓ SpkReader and reader singleton lifecycle now carry explicit concurrent-read and serialized-lifecycle contracts
✓ Long-running pure-native C++ entry points have explicit GIL-release coverage on the major search, payload, kernel-handle, and bulk-compute paths
✗ Public deployment guidance for threaded services still needs to be consumed by downstream REST/server work

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

✓ find_lunar_phases(jd_start, jd_end) convenience function
✓ Lightweight phase calendar output with phase type + JD
✓ Backed by the existing eight-phase Moon phase engine rather than duplicated search math

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

✗ No SVG or chart wheel output
Competitors like Kerykeion (SVG) and Astro Gold (native iOS graphics) have visual output

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)

Sovereign C++ core with Python-native API — not a thin wrapper around an external binary
AVX2-accelerated Chebyshev polynomial evaluation (outperforms pyswisseph hot path)
Composable native evaluator DAG (RelativeEvaluator, SumEvaluator, TopocentricEvaluator)
Native DAF/SPK reader — Moira owns the ephemeris file-format chain
Thread pool for parallel event search
Performance competitive with compiled libraries while maintaining Python transparency

2. Astronomical Transparency

Explicit computational pipeline with inspectable intermediate stages
Documented residuals against ERFA/SOFA
No hidden black boxes or undocumented corrections
Machine contract governance (MACHINE_CONTRACT v1 blocks) enforced by test suite

3. Modern Astronomical Standards

JPL DE441 (not older DE430)
IAU 2006 (not IAU 1976)
Full nutation model (1365 lunisolar + 687 planetary terms)
Gravitational deflection (Sun, Jupiter, Saturn, Earth)
IERS delta-T with physical-model fallback

4. Comprehensive Vedic Coverage

Vimshottari + Ashtottari + Yogini dasha systems
Shadbala (full six-strength with tier classification)
Ashtakavarga (Bhinna + Sarva + transit strength)
Jaimini Karakas (7 and 8-planet schemes)
Panchanga (Tithi, Yoga, Karana, Vara with condition profiles)
Multiple ayanamshas with century-drift characterization

5. Sovereign Fixed Star Registry

1,809 named stars with proper motion, parallax, epoch propagation
Evaluated in native C++ (FixedStarEvaluator)
Audited against SOFA/ERFA (0.00048 arcseconds residual)
License-independent

6. Generalized Visibility Engine (V5 — Unique)

Multi-criterion observing conditions: naked eye, binoculars, telescope
Bortle-scale light pollution classes
Atmospheric extinction model
Moonlight interference policy
Twilight model selection
Lunar crescent visibility criteria with IOTA-oriented authority
No commercial Python library matches this depth

7. Deep Dispositorship & Condition Profiling (Unique)

Full dispositorship chain calculation
Condition network profiles across a chart
Subsystem profiles and comparison bundles
Research-grade dignities analysis not available in any other Python library

8. Harmogram Analysis (Unique)

Harmogram traces with configurable domains
Intensity function spectra
Zero-aries parts harmonic vectors
No commercial competitor exposes harmonic analysis at this level in an API

9. Explicit Policy Control

Every correction stage can be toggled independently
Delta-T policy choices (IERS, polynomial, hybrid)
Ayanamsa selection
Topocentric parallax optional
Atmospheric refraction optional
Dignity doctrine selection

Strategic Recommendations

Tier 1: High-Impact Gaps (Implement Next)

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)
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)
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)

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)
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)
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)
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)

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)
Report Generation
- Jinja2 template system for HTML/PDF
- Impact: Enables client-facing deliverables
- Effort: Medium (3-4 weeks)
Chart Visualization / SVG Output
- Plugin module with wheel rendering
- Impact: Widens developer appeal
- Effort: Medium (3-4 weeks)
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.

Recommended Positions

"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)
"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
"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:

REST API — transforms Moira from a library into a platform
Batch synastry — low-effort gap closure using existing batch infrastructure
Muhurta / practitioner workflow — now the clearer Vedic workflow gap after the Varshaphal annual layer
Chart persistence + research tools — unlocks the research and data workflows that Solar Fire and Kepler own today
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.