DEV Community: Aion Analytics (India)

aion-indian-market-intelligence: The India Contextual Intelligence Layer for Your LLMs and SaaS Products

AION ANALYTICS — Fri, 03 Jul 2026 17:38:33 +0000

This is not another Indian market model.

It is the missing regional causal intelligence layer you plug into your existing LLM, agent, or SaaS product.

You bring the headlines (from any sources or circulars you already use).

It returns structured, time-lagged, signed sector impact + stakeholder context specifically calibrated for Indian markets.

Important Disclaimer

This service provides structured market-interpretation infrastructure for informational purposes.

It is not investment advice, not a trading signal, and not a recommendation. All decisions and regulatory compliance (including SEBI) remain with you.

Core Value Proposition

Signed sector_vector (positive/negative exposure per sector)
Time-lagged effects (e.g. Construction negative at lag 0, positive at lag 90 after a cyclone)
Stakeholder views (banks, borrowers, government, exporters, etc.)
VIX regime context
High-confidence event typing

This is exactly the kind of structured, India-specific context that generic LLMs are weakest at providing.

How to Use It (for Developers & Vibe Coders)

1. Basic (your source → structured India context)

from aion_indian_market_intelligence import analyze

intel = analyze("RBI raises repo rate by 25 bps")
print(intel["sector_vector"])

2. LLM / RAG Augmentation

Store the structured output next to your headlines in your vector store. When the LLM retrieves a document, also retrieve the sector impact data.

enriched = f"Headline: {headline}
Sector impact: {intel['sector_vector']}
Stakeholders: {intel.get('stakeholder_views')}"
# embed or store as metadata

3. SaaS / Sales & Lead Intelligence (high-leverage pattern)

flowchart TD
    Event[Macro / micro event] --> IMI[analyze → sector impact + severity]
    IMI --> YourCRM[Your customer / prospect list]
    YourCRM --> LLM[LLM gets real Indian regional causal context]
    LLM --> Action[Prioritized leads / risk flags / personalized messaging]

Real example: A policy change or weather event lands. Instead of generic “this is negative”, your system knows:

Which specific sectors are affected
The severity and direction
The time-lagged second-order effects
How this maps to your actual customers in those sectors

This is extremely powerful for sales intelligence, account-based marketing, and risk products focused on India.

4. Batch from Your Existing Crawler

from aion_indian_market_intelligence import analyze

for item in my_news_feed:
    intel = analyze(item["headline"])
    item["imi_sector_vector"] = intel["sector_vector"]
    item["imi_top_positive"] = intel["top_positive_sectors"]
    # store or act

Mermaid Diagrams

Basic Integration

flowchart LR
    YourSources[Your news sources / circulars] --> Analyze[analyze()]
    Analyze --> AION[Hosted AION IMI]
    AION --> Structured[sector_vector + context]
    Structured --> YourLLM[Your LLM / Agent / Application]

SaaS Sales / CRM Intelligence

flowchart TD
    ExternalEvent[Policy / Weather / Geopolitics / Commodity shock] 
        --> IMI[analyze → impact per sector]
    IMI --> YourData[Your CRM / lead list]
    YourData --> EnrichedLLM[LLM now understands India-specific impact on *your* accounts]
    EnrichedLLM --> Actionable[Prioritized actions]

Sample Output (Realistic)

{
  "headline": "Severe cyclone makes coastal landfall...",
  "event": "weather_disaster",
  "confidence": 0.84,
  "sector_vector": {
    "Construction": { "lag_0": -0.50, "lag_90": 0.55 },
    "Agriculture": { "lag_0": -0.65, "lag_90": -0.65 }
  },
  "stakeholder_views": { ... }
}

Positioning for Different Users

Vibe coders / AI agents: Plug in via MCP (uvx aion-indian-market-intelligence-mcp) and give your agent real Indian market context.
SaaS builders: Add sector-impact awareness to any product that touches Indian companies or economy.
Internal tools: Enrich research, risk, or opportunity systems with calibrated causal vectors instead of raw sentiment.

Source: https://dashboard.aiondashboard.site/models/indian-market-intelligence

PyPI: pip install aion-indian-market-intelligence

MCP: uvx aion-indian-market-intelligence-mcp

This is infrastructure. You decide what to do with the intelligence.

aion-indian-market-calendar: Accurate Multi-Session Indian Market Timing for Automated Systems

AION ANALYTICS — Fri, 03 Jul 2026 16:31:54 +0000

Indian trading systems often need to decide automatically whether to start, pause, or stop based on which exchange segment is actually open.

A single "is today a trading day?" check is not enough. NSE equity, CDS (currency), and MCX (commodities) have different hours. Some days are partial sessions (Muhurat trading, special closures). Hard-coding dates or relying on generic calendars leads to bots that either miss sessions or try to trade on holidays.

aion-indian-market-calendar solves exactly this problem.

Install

pip install aion-indian-market-calendar

Basic Showcase

from aion_indian_market_calendar import IndiaMarketCalendar
from datetime import datetime
import pytz

cal = IndiaMarketCalendar.bundled(2026)
ist = pytz.timezone("Asia/Kolkata")
now = datetime.now(ist)

print("NSE equity open?", cal.is_market_open(now, "NSE"))          # 9:00–15:30
print("CDS open?", cal.is_market_open(now, "CDS"))                 # 9:00–17:00
print("MCX open?", cal.is_market_open(now, "MCX"))                 # 9:00–23:30

session = cal.get_session(now, "MCX")
if session:
    for seg in session:
        print(seg.open, "→", seg.close)

It correctly handles:

Different close times (15:30 vs 17:00 vs 23:30)
Muhurat trading (short evening session, usually on Diwali)
Exchange-specific holidays
Live refresh (optional) for last-minute circular changes

Comparison with Common Alternatives

Tool / Approach	NSE Equity	CDS (to 5pm)	MCX Evening Session	Muhurat Trading	Partial Holidays	Notes
pandas_market_calendars (XNSE)	Partial	No	No	Treated as holiday	Poor	Generic calendar, no India evening sessions
Hard-coded holiday list	Fragile	Fragile	Fragile	Easy to miss	Very fragile	Breaks every year or on circular changes
Custom "if date in holidays"	Error-prone	Misses segments	Completely wrong	No	No	Common source of production incidents
aion-indian-market-calendar	Yes	Yes	Yes	Yes (special session)	Yes (per segment)	Segment-aware, bundled data + live refresh

Generic calendars fail because Indian exchanges do not share one uniform schedule.

Real Use Case: Auto-Start Systems on Partial Working Days

Many developers run data pipelines, scanners, or execution engines that should only be active when a market segment is open.

Instead of one cron job that always runs 9 AM–11:30 PM, you can poll at fixed intervals and decide per segment.

Example: Polling to Auto-Start/Stop for Different Sessions

import time
from datetime import datetime
import pytz
from aion_indian_market_calendar import IndiaMarketCalendar

cal = IndiaMarketCalendar.bundled(2026)
ist = pytz.timezone("Asia/Kolkata")

def should_run_for_segment(segment: str) -> bool:
    now = datetime.now(ist)
    return cal.is_market_open(now, segment)

# Simple polling loop (run this in a supervisor or systemd timer)
while True:
    now = datetime.now(ist)

    equity_open = should_run_for_segment("NSE") or should_run_for_segment("BSE")
    cds_open    = should_run_for_segment("CDS")
    mcx_morning = should_run_for_segment("MCX")  # first session
    mcx_evening = cal.is_market_open(now, "MCX") and now.hour >= 17

    if equity_open:
        # start or keep alive equity data ingestion / scanners
        print(f"{now} — Equity session active (NSE/BSE)")
    else:
        # gracefully stop equity components
        pass

    if cds_open:
        print(f"{now} — CDS session active")

    if mcx_morning or mcx_evening:
        print(f"{now} — MCX session active (morning or evening)")

    time.sleep(60)  # poll every minute; adjust as needed

Why This Matters on Partial Holidays

Muhurat Trading Day: Equity has a very short evening session (often 1 hour). Generic calendars mark the whole day as holiday. Your polling will correctly see the special session and can start the equity engine only for that window.
Exchange-specific closures: One segment may be closed while another is open.
MCX second session: Even on normal days, the evening session (roughly 5 PM – 11:30 PM) is separate. Systems that only watch "9 AM – 3:30 PM" will miss commodity opportunities or data.

By checking is_market_open("SEGMENT") or get_session(...) you get the precise truth for that segment at that moment.

Other Useful Methods for Automation

cal.trading_days("NSE_EQUITY", 2026)      # list of actual trading dates
cal.holidays("NSE_EQUITY", 2026)          # holidays for a segment
cal.events_on("2026-11-08", "NSE")        # detects Muhurat etc.

You can also enable live refresh against the official AION endpoint so last-minute circular changes are picked up without rebuilding the package.

Bottom Line for Developers

If you are building anything that needs to run autonomously around Indian market hours — data collectors, risk engines, execution schedulers, scanners — you need segment-aware, session-aware calendar logic.

aion-indian-market-calendar gives you that in one small, MIT-licensed package with no external dependencies beyond pytz/tzdata.

pip install aion-indian-market-calendar

Full documentation and vs-pandas comparison:

https://dashboard.aiondashboard.site/open-source/indian-market-calendar

Use it to poll, decide, and auto-start only when the right session is actually open — including the tricky partial and evening sessions that trip up most systems.

Source: AION Analytics (India) dashboard

Mirrored for discoverability. aion-indian-market-calendar is MIT licensed and available on PyPI.

AION Research Notes — Complete Index

AION ANALYTICS — Fri, 03 Jul 2026 16:29:29 +0000

AION Research Notes — Full Collection

Mirrored from the AION Analytics (India) dashboard for wider reach.

Originals & latest: https://dashboard.aiondashboard.site/research

Notes (in reverse chrono on this org)

The 15-Minute Opening Window Rule
The Dollar-Metals Dynamic
The Great Indian Illusion, Part 2 (Gold)
Gold-Silver Ratio as a Risk Regime Signal
Your Trading Car
Who Lost in the Crash
The Great Indian Illusion (Part 1)
Indian Market Intelligence: Macro Event to Sector Impact
SEBI Governance Blind Spot
Crash Cycles
Options Greeks Through the Taylor Series
Systemic Risk Model: Graph Theory
The Bear Trap
Euler Equation and Why Financial Markets Love It
The Bull Trap
Veritas: From Bachelier to Black-Scholes
The High VIX Trap
OpenAlgo symtoken: 31 Brokers...
The FII-DII Headline Is Lagging...

Papers

AION Indian Market Intelligence: Causal Incidence Modeling
Asymmetric Multipolarity (SSRN)
Bridging the Gap (SSRN)

All original posts include links back to the dashboard.

Bridging the Gap: Blending Human Insights and Digital Tools to Master Emerging Markets (SSRN)

AION ANALYTICS — Fri, 03 Jul 2026 16:29:03 +0000

Bridging the Gap: Blending Human Insights and Digital Tools to Master Emerging Markets.

This paper explores the intersection of human judgment and digital/AI tools for better understanding and navigating emerging markets, with a focus on India.

Key themes include combining traditional analysis with data-driven methods, the role of local context, and practical frameworks for market participants and researchers in complex environments.

Full paper on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5140987

Emerging markets India Digital tools Human insights AI Research paper

Mirrored from AION Analytics (India) / SSRN. Full paper

The 15-Minute Opening Window Rule: Market Structure Before Entry

AION ANALYTICS — Fri, 03 Jul 2026 16:28:39 +0000

The first 15 minutes of an NSE trading session are the noisiest. Order imbalances from overnight and pre-market carry settle. Institutional positioning adjusts. Retail participants react to opening gaps. Entering a trade before the opening range is defined is gambling on resolution, not reading structure. Wait for the opening high, low, and VWAP to establish — then act on the evidence.

Why the opening 15 minutes are structurally different

When the NSE opens at 9:15 AM, several forces arrive simultaneously:

Pre-market auction orders that set the opening price for liquid stocks and indices
Overnight position adjustments from institutional participants reacting to global markets, futures premiums, and news
FII order execution that may be spread across the opening period
Retail reaction to the opening gap (up or down) driven by pre-market commentary and sentiment

The result is high-volume, high-volatility price discovery that frequently overshoots in both directions. The opening candle is almost never the day's true direction. It is the market clearing its overnight imbalances.

The opening range: high, low, and VWAP

By the end of the first 15 minutes — typically the 9:15-9:30 opening session — three reference levels are established:

Opening High: The highest price reached in the first 15 minutes. A break above this level with volume confirmation is a bullish structure signal.
Opening Low: The lowest price reached in the first 15 minutes. A break below this level with volume confirmation is a bearish structure signal.
VWAP (Volume-Weighted Average Price): The average price weighted by volume transacted. Price above VWAP means buyers have been in control of the volume during the session. Price below means sellers have.

These three levels together form the structural map for the trading session. They are not forecasts — they are the raw evidence the market has provided about its own direction and conviction.

The entry framework

Setup: Let the first 15 minutes complete without placing any intraday directional trade. Mark the opening high, opening low, and VWAP on your chart.

Bullish breakout entry:

1) Price closes above the opening high on the 15-minute chart
2) Volume on the breakout candle is above average (1.5× or higher is a stronger signal)
3) Price is above VWAP
4) Price retests the breakout level and holds (optional but stronger signal)

Bearish breakdown entry: symmetric.

What to do when price chops inside the opening range

The opening range is frequently not broken for the first 30-60 minutes of trading. This is not a setup failure — it is the market telling you there is no clear structural conviction yet. The correct response is to wait, not to create a trade by picking a direction arbitrarily.

Midpoint entries inside the opening range have poor risk/reward because both the high and low are nearby. The breakout or breakdown gives you a structural entry with a clear invalidation level (the breakout/breakdown level itself) and room to the next reference point (often the prior day's high/low or a daily VWAP level).

Why this approach works structurally

Institutional order flow is not random. Large participants are executing into the opening liquidity to build or adjust positions. Their activity creates the opening range. When price breaks out of that range with volume, it is evidence that institutional order flow is directional in that session — not merely reactive.

Retail participants who enter during the first candle are entering before that evidence exists. They are making a directional bet before the market has revealed its structure. The 15-minute rule is simply the practice of waiting for the evidence before acting on it.

Opening range VWAP Volume confirmation NSE Execution discipline Intraday Market structure

Mirrored from AION Analytics (India) dashboard. Read original

The Dollar-Metals Dynamic: A Factual Framework

AION ANALYTICS — Fri, 03 Jul 2026 16:28:04 +0000

The historical inverse relationship between the US Dollar Index and precious metals is one of the most studied correlations. In early 2026, that correlation reached -96% — a level observed only seven times since 1990. This note explains the mechanics, the conditions that produce extreme readings, and what historical patterns suggest follows.

The core relationship

Gold is priced globally in US dollars. When the dollar weakens, the same ounce of gold costs more dollars. When the dollar strengthens, it costs fewer. This mechanical relationship is the floor of the inverse correlation.

Beyond mechanical, when the dollar weakens because of US fiscal stress, monetary policy uncertainty, or geopolitical reserve diversification, the same conditions drive demand for gold as an alternative store of value.

The 2026 reading

When DXY-gold correlation reaches -96%, ~96% of daily variance in gold prices is explained by the dollar. Historical analysis of prior occasions shows extreme reading followed within 3-6 months by correlation normalisation. During normalisation, silver tends to outperform gold by 15-25% on relative basis because of silver's industrial demand component.

Why India is specifically exposed

India's exposure runs through:

Current account: second-largest gold importer. Rising gold price in USD + depreciating rupee widens CAD.
Monetary policy: rising gold import costs reduce RBI room for rate cuts.
Household savings: significant allocation to physical gold. Strong gold price encourages allocation away from formal banking.
Silver industrial demand: growing solar manufacturing capacity uses silver.

The geopolitical dimension

The 2026 extreme is occurring alongside reserve currency diversification by BRICS+ economies, US fiscal deficit expansion, and restructuring of global trade settlement. When reserve managers diversify into gold, they weaken the dollar and strengthen gold demand simultaneously. Structural reserve diversification shifts persist over years.

Gold Silver DXY US Dollar Precious metals Macro correlation Geopolitics

Mirrored from AION Analytics (India) dashboard. Read original

The Great Indian Illusion, Part 2: When the Framework Made a Call

AION ANALYTICS — Fri, 03 Jul 2026 16:27:28 +0000

Forty days after publishing the Great Indian Illusion framework, every structural crack we identified had worsened — and the Prime Minister publicly asked citizens not to buy gold. This note shows what the framework called, what happened, and why the gold signal is the canary in the coal mine for the rupee and domestic savings.

What the framework said — and what happened

The five structural cracks:

Consumption without income growth
Jobs that do not look like jobs
Inflation that compounds faster than wages
Rising household debt funding the appearance of demand
Rupee depreciation passing through to imported inflation

Forty days later, data confirmed every crack. Urban consumption growth fell. Real salary growth fell. Food inflation elevated. Household savings compressed. Rupee depreciation continued.

Why the government discouraged gold purchases

When a government's leadership publicly asks citizens not to buy gold, it is a macro signal. Gold demand puts upward pressure on the current account deficit through imports. India imports 800–900 tonnes per year. At elevated prices, the import bill is material.

The appeal is a current account management intervention.

What gold purchasing actually signals

Indian households increasing gold allocation are expressing:

Real deposit rates are negative or near-zero
Trust in the rupee's long-term stability is declining
Equity risk is elevated

These are rational responses to the structural conditions.

The framework's forward implication

If the cracks persist, the gold demand signal will persist. The current account pressure will continue. The rupee will face structural depreciation. The gold signal is one of the clearest leading indicators of that divergence widening.

Political economy Gold Macro risk India Rupee Household savings Fiscal policy

Mirrored from AION Analytics (India) dashboard. Read original

Gold-Silver Ratio as a Risk Regime Signal

AION ANALYTICS — Fri, 03 Jul 2026 16:27:05 +0000

The gold-silver ratio as a risk regime signal. Not a trading rule. A structural indicator of when fear or liquidity stress is dominating precious metals pricing.

Mirrored from AION Analytics (India) dashboard. Read original

AION Indian Market Intelligence: Causal Incidence Modeling for Indian Market Events

AION ANALYTICS — Fri, 03 Jul 2026 16:26:48 +0000

Market events are redistributive graphs, not scalar sentiment objects. A policy change, oil shock, monsoon failure, rate move, or currency event does not become simply positive, negative, or neutral. It reallocates margin, liquidity, inventory value, tax burden, subsidy exposure, pricing power, and risk across sectors and stakeholders over time.

AION Indian Market Intelligence models Indian financial headlines as structured causal incidence objects: event class, primitive pressure vector, sector redistribution vector, stakeholder incidence views, volatility-regime context, and audit metadata.

This paper frames the system as a causal incidence and redistribution engine for emerging-market financial events. It explains why scalar sentiment is structurally insufficient, formalizes the primitive-to-sector-to-stakeholder computation, documents the architecture, and records the empirical corrections that moved the system away from shallow sentiment and keyword matching.

Full draft available in AION research materials. The operational object is a compressed causal briefing for analysts, developers, and executives.

Market intelligence Causal modeling Sector impact Redistribution India finance Research paper

Mirrored from AION Analytics (India) dashboard. Read original

Your Trading Car: A Minimalist Dashboard for Intraday and Equity Decisions

AION ANALYTICS — Fri, 03 Jul 2026 16:26:28 +0000

Reduces chart clutter into four gauges: price/trend, volume/participation, momentum, and key levels. Uses VWAP/MA as the speedometer for direction. Explains why conflicting gauges often mean no trade is the cleanest decision.

Mirrored from AION Analytics (India) dashboard. Read original

Asymmetric Multipolarity: Financial, Industrial, Resource, and Strategic Power in the Post-Unipolar Global Order (SSRN)

AION ANALYTICS — Fri, 03 Jul 2026 16:26:07 +0000

A geoeconomic framework for reading the post-unipolar order through four power vectors: financial depth, industrial capacity, resource security, and strategic autonomy.

Positions AION as infrastructure research.
Connects macro capital flows, supply chains, resource chokepoints, and state capacity to technology architecture.
Useful for model assumptions where markets react to geopolitical and industrial regime shifts.

Full paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6449739

Asymmetric Multipolarity Geopolitics India Economy Research

Mirrored from AION Analytics (India) dashboard. Read original

Who Lost in the Crash — Anatomy of Losses in the 2025 Correction

AION ANALYTICS — Fri, 03 Jul 2026 16:25:52 +0000

Who lost in the 2025 correction: the anatomy of losses. Not just 'the market fell.' Specific participants, specific leverage structures, specific timing errors. The distribution of pain is never uniform.

Mirrored from AION Analytics (India) dashboard. Read original