╔════════════════════════════════════════════════════════════════════╗
                    ║                 VIKKRANT POL · OPTIMUS QUANTA                      ║
                    ║        PRIVATE PRODUCTION AUTONOMOUS TRADING SOFTWARE              ║
                    ║                    INDIA · UNITED STATES                           ║
                    ╚════════════════════════════════════════════════════════════════════╝

"Markets are information. Infrastructure is alpha."

I'm Vikkrant Pol, an IIT Goa graduate and the founder-builder of Optimus Quanta, a private production autonomous trading software platform operating through independently deployed Indian and US market environments.

I build systems for systematic market research, market scanning, backtesting, Monte Carlo simulation, walk-forward validation, live options forward simulation, broker-connected workflows, risk analytics, dashboards, reconciliation, AI-assisted research agents, deployment controls, and live-operations infrastructure across Indian equities, NSE F&O, MCX commodities, US equities, and selected multi-market workflows.

My work is domain-led and system-focused: translating market research questions into repeatable data workflows, validation tooling, monitoring surfaces, risk controls, execution-aware processes, and operational systems.

AI serves as a research and development accelerator. Market judgment, system architecture, validation standards, risk decisions, execution authorization, and operational responsibility remain human-governed.

A private production-grade trading software platform designed around:

• systematic research
• market-universe construction
• liquidity and security-type controls
• market scanning and candidate ranking
• backtesting
• Monte Carlo analysis
• walk-forward validation
• live options forward simulation
• broker workflows
• portfolio risk
• execution-state management
• broker reconciliation
• operational alerting
• AI research agents
• operator-supervised execution

Optimus Quanta now operates through two separate market environments:

Optimus India now also exposes an isolated Optimus Options Lab through the production dashboard. The lab consumes live read-only NIFTY options data and runs a dedicated Live Forward Simulation environment with simulated execution and no broker order route. Its research surface includes multi-expiry capture and replay, strategy preview and comparison, portfolio Greeks, mark-to-market P&L, drawdown and risk alerts, regime diagnostics, scenario analysis, forward-simulation performance reporting, and deterministic readiness/review gates.

The US platform is fully production-deployed on AWS EC2 with live market data, official XNYS session scheduling, broker-backed order-state workflows, durable tracking, monitoring, alerting, and recovery controls.

It is currently operating through a controlled forward-validation authorization phase before live-capital activation. The present execution boundary uses Alpaca's non-capital environment; the surrounding infrastructure is operated as a production system.

The two environments maintain separate:

• market calendars
• time zones
• market-session rules
• symbol universes
• broker integrations
• provider controls
• scheduled jobs
• runtime services
• deployment artifacts
• state boundaries
• failure boundaries
• rollback and recovery paths

Autonomous components can perform scheduled scans, structured research, candidate review, portfolio supervision, reporting, monitoring, and operational analysis.

Execution remains explicitly:

• risk-gated
• broker-state-aware
• persistence-aware
• fail-closed where required
• operator-supervised

Fresh filesystem inventories were completed for both production environments on August 4, 2026.

Audit-scope clarification: The India figure is a broad online production-root inventory that includes the main application, scanner, operational scripts, configuration, and documentation. The US figure is a broad source-tree inventory that includes application source, tests, documentation, templates, static assets, fixtures, scanner data, and some retained support or historical files. These figures are not claims that every counted line is active runtime code, Git-tracked application code, or installed in an EC2 deployment payload.

Proprietary strategy logic, account data, broker credentials, private execution details, and alpha-generating thresholds are intentionally not exposed publicly.

Selected validation evidence for Optimus Quanta is published on optimusquanta.com as operational evidence of infrastructure behaviour under controlled forward-simulation or pilot conditions.

Detailed validation material includes:

• master equity-curve review
• benchmark comparison
• drawdown analysis
• stress-window evidence
• silver-crash review
• geopolitical-shock review
• operational observations
• validation methodology
• limitations and interpretation notes

The complete evidence package is maintained in PERFORMANCE_VALIDATION_APPENDIX.md.

These figures are presented as historical software and infrastructure validation evidence—not investment advice, projected returns, audited fund performance, or a guarantee of future performance.

flowchart LR

    subgraph INDIA["India Production Environment"]
        direction LR
        IM["India Markets<br/>NSE Equities · F&O · MCX"]
        HI["Optimus India Runtime"]
        IB["Indian Broker Workflows"]
        IC["India Risk · State<br/>Reconciliation · Alerts"]
        IO["India Monitoring<br/>Audit · Recovery"]
        OL["Optimus Options Lab<br/>Live Forward Simulation"]
        OA["Live NIFTY Data · Greeks · Scenarios<br/>Forward-Sim P&L · Risk · Readiness"]

        IM --> HI
        HI --> IB
        HI --> IC
        IC --> IO
        IM --> OL
        OL --> OA
        HI -. dashboard navigation / isolated runtime .-> OL
    end

    subgraph USENV["US Production Environment"]
        direction LR
        UM["US Markets<br/>XNYS · US Equities"]
        OU["Optimus US Runtime"]
        AP["US Broker Workflows"]
        UC["US Risk · State<br/>Reconciliation Boundaries · Alerts"]
        UO["US Monitoring<br/>Audit · Recovery"]

        UM --> OU
        OU --> AP
        OU --> UC
        UC --> UO
    end

    PRINCIPLES["Shared Engineering Principles<br/>Risk-Gated · Broker-Aware · Fail-Closed"]

    PRINCIPLES -.-> IC
    PRINCIPLES -.-> UC

The US system is not a renamed copy of the India platform. It is an independently deployed production environment engineered for US market structure, XNYS sessions, Eastern-time scheduling, daylight-saving transitions, early closes, US symbol and liquidity controls, Alpaca order-state semantics, continuous market-data tracking, and isolated operational boundaries.

The current phase is live forward validation under controlled execution authorization. Live-capital activation remains a deliberate operational gate, not a missing engineering capability.

Its market-specific engineering includes:

• official XNYS-session resolution
• US Eastern-time scheduling and display
• daylight-saving-aware behaviour
• US holiday and early-close handling
• pre-market, regular-session, and post-market classification
• close-relative scheduled workflows
• US trade-date normalization
• Alpaca broker order IDs, statuses, and confirmed fill semantics
• broker-confirmed average fill prices
• first-message readiness and stale-feed monitoring
• US-specific symbol canonicalization
• liquidity-aware universe controls
• ETF, ADR, unit, warrant, and unsupported-security classification
• separate deployment, state, credential, and failure boundaries

Production updates follow a controlled artifact-deployment process.

Reviewed source change
        ↓
Focused automated validation
        ↓
Allowlisted runtime payload
        ↓
Manifest and digest verification
        ↓
Remote staging
        ↓
Backup of exact live files
        ↓
Installation of reviewed artifacts
        ↓
Restart of only affected services
        ↓
Health and state verification
        ↓
Documented rollback path

Production controls include:

Normal production deployment does not depend on uncontrolled:

• git pull
• production branch switching
• git reset --hard
• git clean
• copying credentials into Git
• copying runtime databases or logs into source control
• bulk replacement of an EC2 application tree

Python FastAPI NIFTY Options FYERS Read-Only Black-76
Live Market Data Live Forward Simulation Greeks Scenario Analysis Risk Gates

Optimus Options Lab is the isolated options research and Live Forward Simulation environment integrated with the Optimus India / Helios Online production dashboard. It is designed for continuous live-market observation, simulated strategy execution, and advanced options validation using live NIFTY market data.

LIVE NIFTY OPTION CHAIN
        ↓
Multi-expiry capture and replay-ready history
        ↓
IV / smile / term-structure / surface diagnostics
        ↓
Defined-risk strategy preview and comparison
        ↓
Payoff, PoP, Greeks and spot/IV scenario analysis
        ↓
Forward-simulation account marking and trade lifecycle
        ↓
Equity curve · realised/unrealised P&L · drawdown
        ↓
Portfolio Greeks · alerts · regime diagnostics
        ↓
Readiness gates and auditable human review

The Options Lab is intentionally isolated from the Equity runtime: separate application state, database, services, and forward-simulation controls. The public production dashboard provides navigation into the lab without merging its execution or persistence boundaries into the equity system.

React Vite FastAPI Black-Scholes-Merton Fyers V3
Implied Volatility Options Greeks Risk Controls AI Strategy Engineering

24Options is a private full-stack options intelligence, strategy-construction, risk, and execution-oriented research platform for NIFTY and BANKNIFTY derivatives workflows.

24OPTIONS  ·  NSE F&O OPTIONS INTELLIGENCE

> Pricing Engine             > Greeks Dashboard
> Strategy Workflows         > AI-assisted Trade Structuring
> Implied Volatility         > Portfolio Risk Controls
> Strike Selection           > Execution-oriented Workflow
> Position Sizing            > Multi-leg Payoff Analysis

Live option chain
        ↓
Pricing and implied-volatility calculation
        ↓
Greeks across strikes and expiries
        ↓
Market-regime classification
        ↓
Twenty-four-strategy scoring
        ↓
Strike and leg optimisation
        ↓
Portfolio-risk validation
        ↓
Position sizing
        ↓
Human review and execution-oriented staging

24Options is kept private because it contains proprietary product logic, broker-integrated workflows, and protected implementation details.

Formerly developed under the US_IN_NEW_SCANNER_NEW_FEATURES workspace name.

Python FastAPI SQLite Bash US Equities NSE

A private scanner and signal-research laboratory for US equities and Indian-market workflows.

Core work includes:

• multi-market scan automation
• liquidity-aware US universe construction
• deterministic sampling
• persistent scan state and history
• security-type classification
• symbol canonicalization
• ETF and ADR controls
• provider-quality metadata
• cache and retry controls
• browser-based monitoring
• scoring and filtering workflows
• persisted reports and diagnostics
• exported outputs for trading workflows

Universe construction
        ↓
Symbol canonicalization
        ↓
Eligibility and security-type controls
        ↓
Liquidity qualification
        ↓
Market and provider metadata
        ↓
Candidate scoring and ranking
        ↓
Persisted scan evidence

Python Offline Evaluation Security Deterministic Testing Agentic Coding

A private, production-oriented benchmark program for evaluating coding agents under realistic repository, security, review, and execution constraints.

The program is designed around:

• isolated workspaces
• trusted execution boundaries
• deterministic evaluation
• private and hidden test material
• filesystem restrictions
• network and subprocess controls
• timeout and resource limits
• anti-gaming protections
• fresh workspaces per evaluation
• multi-hunk tasks
• alternative-correct solutions
• reviewer-quality governance
• evidence provenance
• public/private benchmark separation
• reproducible statistical rankings

Phase 0 governance and operational closure is complete. Later benchmark-construction and evaluation phases remain active work.

Python FastAPI React Vite SQLite pandas NumPy

A full-stack, multi-market, multi-asset strategy research platform for equities and cryptocurrency workflows.

Key capabilities include:

• Indian-equity workflows
• US-equity workflows
• cryptocurrency workflows
• configurable backtests
• custom date ranges and timeframes
• EMA, RSI, breakout, and MACD strategies
• position-sizing models
• slippage and commission assumptions
• stop-loss support
• persistent SQLite-backed research history
• equity-curve and drawdown analysis
• performance and risk statistics
• broker-aware data workflows
• API-backed job progress
• saved report history
• JSON result export
• automated backend regression coverage

Python Visualisation Backtesting

Backtesting and visualisation framework for inside-bar-based systematic trade setups with protected core logic.

Python pandas NumPy

Cross-exchange arbitrage research focused on spread detection, fee- and slippage-aware evaluation, and performance testing.

Python pandas matplotlib

Backtesting framework for an EMA-based systematic strategy with trade analysis and performance visualisation.

Android Java Risk Management

A mobile-first position-sizing tool built around account capital, entry price, stop-loss distance, and percentage risk.

Some projects intentionally omit proprietary or alpha-generating logic. Public code is intended to showcase architecture, research processes, backtesting design, quantitative tooling, risk systems, and workflow engineering without exposing the exact edge.

Indian Equities   ████████████████████  Scanning, execution, portfolio, reconciliation
NSE F&O           ████████████████████  Index and stock options, derivatives workflows
MCX Commodities   ██████████████████░░  Crude oil, gold, silver research and infrastructure
US Equities       ██████████████████░░  Liquidity scanning, XNYS scheduling, broker-backed forward operations
Options Theory    ████████████████████  Pricing, Greeks, IV, GEX, Max Pain, PCR
Risk Management   ███████████████████░  Position sizing, exposure, stops, drawdown controls
Production Ops    ███████████████████░  EC2 services, deployment, monitoring, recovery

AI supports the research, engineering, and operational-review process where it improves speed, structure, and iteration quality.

Typical uses include:

• scheduled research scans
• market and candidate review
• backtest and validation analysis
• structured signal explanations
• portfolio and journal queries
• database-assisted research
• report generation
• workflow assistance
• multi-file software development
• regression-test generation
• documentation and audit review
• controlled multi-agent research
• benchmark and evaluation development

The broader approach remains domain-first:

Market knowledge leads
        ↓
System design constrains
        ↓
Validation challenges
        ↓
Risk controls authorize
        ↓
AI accelerates selected work

Market research, validation discipline, risk thinking, and reliable infrastructure lead. AI accelerates selected research and engineering workflows.

Role-scoped AI employees and research agents are not independently authorized to:

• place broker orders
• change strategies
• modify position sizing
• alter stop-loss rules
• change portfolio-risk limits
• activate live trading
• modify production execution state

Current work includes:

• operating and hardening the India production environment
• operating and validating the US production environment through live forward operation
• improving US liquidity-aware universe construction
• strengthening security-type classification
• improving provider-quality and conflict evidence
• enforcing broker-confirmed order and fill truth
• strengthening execution-state persistence
• improving reconciliation boundaries
• expanding production monitoring and recovery
• maintaining reproducible validation evidence
• developing the Optimus Quanta software-engineering benchmark
• separating public evidence from proprietary system internals