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CN2.DEVBUKYA NARESH

CV / 01 — HEADER

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CN2 / QUANTITATIVE DEVELOPER

Research · Markets · Data · Engineering

02

PROFILE

CN2.dev — the digital headquarters of CN2.DEV. Quantitative development through research, markets, data and engineering: deterministic systems for understanding markets.

STATUS — OPEN TO SELECTED WORK · RESEARCH · COLLABORATION

03

WHAT I BUILD

  • QUANTITATIVE ENGINES

    Multi-strategy trading engines with deterministic backtesting, explicit cost modelling and paper execution — 11.9M+ bars, 5,867 completed trades.

    REPO / apex-quant-engine

  • EXECUTION ARCHITECTURES

    Multi-timeframe trading pipelines with structural risk controls — 1% risk cap, 1:4 R:R floor, cost-budget filtering, profit lock, giveback limit.

    REPO / automated_trading_os

  • MARKET-DATA INFRASTRUCTURE

    Point-in-time safe data platforms: ingestion, validation, quarantine, deterministic replay, nanosecond timestamps, exact decimal arithmetic.

    REPO / quant-market-data-replay

  • RESEARCH GOVERNANCE

    Experimentation platforms governing hypothesis → verification → promotion with 352 tests on deterministic fixtures.

    REPO / qrsip

  • PERFORMANCE SYSTEMS

    Zero-dependency C11 data engines at bare-metal throughput — 487,421 records/sec, 31.89 MB/s.

    REPO / billing-data-gateway

04

EXPERIENCE

  1. APEX QUANT ENGINE

    Sole architect and engineer

    Complete research-to-execution pipeline: data engine, strategy engine, risk engine, portfolio allocation, deterministic backtesting, paper execution.

    BACKTEST / SIMULATION — SEE CASE STUDY+570.18% net simulated ROI (master-fund backtest) · 5,867 trades · 44.50s backtest
    CASE STUDY — cn2-identity-hub.vercel.app/work/apex-quant-engine

  2. AUTOMATED TRADING OS

    Sole architect and engineer

    Multi-timeframe execution architecture with verified structural controls from HTF context to order construction.

    SIMULATION — STRUCTURAL CONTROLS1% risk cap · 1:4 R:R floor · full cost modelling
    CASE STUDY — cn2-identity-hub.vercel.app/work/automated-trading-os

  3. QUANT MARKET DATA REPLAY

    Sole architect and engineer

    Point-in-time safe market-data infrastructure with deterministic replay and adversarial testing.

    TEST SUITE — DETERMINISM9-stage pipeline · nanosecond timestamps · SQLite WAL
    CASE STUDY — cn2-identity-hub.vercel.app/work/market-data-replay

  4. QRSIP

    Sole architect and engineer

    Research governance platform: hypothesis → experiment → verification → artifact → report → promotion.

    TEST SUITE — 352 TESTS352 tests · deterministic fixtures · CI security validation
    CASE STUDY — cn2-identity-hub.vercel.app/work/qrsip

  5. BILLING DATA GATEWAY

    Sole engineer

    Zero-dependency C11 utility normalizing multi-cloud cost exports into an intermediate financial model.

    SELF-BENCHMARK — SELF-MEASURED THROUGHPUT487,421 rec/s · 31.89 MB/s · 2,051 ns per record
    REPOSITORY — github.com/CloudOps-Financial-Platform/billing-data-gateway
05

CAPABILITIES

  • QUANTITATIVE RESEARCH

    Forming falsifiable market hypotheses and testing them with deterministic experiments — evidence before conclusions.

    • Hypothesis workflow
    • Statistical evaluation
    • Deterministic fixtures

  • MARKET DATA

    Point-in-time safe market-data infrastructure: ingestion, normalization, validation and deterministic replay.

    • SQLite WAL
    • Nanosecond timestamps
    • Exact decimals

  • SYSTEMATIC TRADING

    Rule-based multi-strategy trading architectures where entries, exits and sizing are code, not discretion.

    • Multi-strategy engines
    • Signal contracts
    • Paper execution

  • BACKTESTING

    Deterministic simulation over historical bars with explicit fees and slippage — reproducible run to run.

    • Deterministic replay
    • Cost modelling
    • 11.9M+ bar harness

  • RISK

    Risk-first position sizing and structural controls: caps, R:R floors, profit locks and giveback limits.

    • 1% risk cap
    • 1:4 R:R floor
    • Cost-budget filtering

  • DATA INFRASTRUCTURE

    High-throughput data pipelines: ingestion, transformation and query layers built for scale and exactness.

    • 487,421 rec/s pipeline
    • POSIX mmap
    • Zero-dependency C11
    • NSDC Data Standards

  • PERFORMANCE ENGINEERING

    Low-latency systems work — memory-mapped I/O, serialization elimination and bare-metal throughput.

    • C11
    • POSIX API
    • -O3 optimization
    • Zero allocations on hot path

  • AI & COMPUTATIONAL INTELLIGENCE

    Applying computational methods and generative pipelines where they carry their weight — classification, pattern detection, automation.

    • Python
    • Automation pipelines
    • Generative AI
    • NSQF Level 7 AI/ML

  • SYSTEMS PROGRAMMING & MEMORY

    Low-level memory management, pointer arithmetic, custom allocators, and hardware-close systems engineering in C and C++.

    • C
    • C++
    • Memory Layout
    • Pointers & Heap Management
    • Valgrind
    • POSIX

  • ALGORITHMS & DATA STRUCTURES

    Rigorous computational complexity analysis, Big O optimization, custom trees, hash tables, tries, and graph algorithms.

    • Big O Analysis
    • Hash Tables
    • Binary Trees & Tries
    • Sorting & Search
    • Graph Traversal

  • CLOUD & DEVOPS INFRASTRUCTURE

    Containerized orchestration, CI/CD pipelines, cloud deployment, and scalable infrastructure across multi-cloud environments.

    • Docker
    • Kubernetes
    • AWS
    • GCP
    • Azure
    • GitHub Actions
    • GitLab CI

  • FULL-STACK TOOLING & SCIENTIFIC UI

    Interactive quantitative research terminals, real-time telemetry dashboards, and high-performance web applications.

    • TypeScript
    • Next.js
    • React
    • FastAPI
    • Tailwind CSS
    • Canvas / WebGL
    • WebSockets
06

STUDY FOCUS

  • 01Lookahead bias and point-in-time correctness in market data
  • 02Deterministic replay and reproducibility as research infrastructure
  • 03Regime dependence in multi-strategy portfolio performance
  • 04Risk-first position sizing and structural trade admission
  • 05Multi-timeframe information flow in systematic trading
  • 06Exact arithmetic and representation error in financial computation
07

CONTRIBUTION

  • RESEARCH ENGINEERING

    Turning market questions into deterministic, reproducible experiments with verifiable evidence.

  • DATA-INTENSIVE SYSTEMS

    Ingestion pipelines, normalization layers and query infrastructure built for exactness and scale.

  • RISK-DISCIPLINED DESIGN

    Architectures where risk controls are structural invariants rather than advisory conventions.

  • PERFORMANCE WORK

    Throughput and latency engineering where measurement context is part of the deliverable.

08

EVIDENCE

Results above are labelled by method — BACKTEST, SIMULATION, SELF-BENCHMARK or TEST SUITE.