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Finance-broski/README.md

FinanceBroski — Quant Developer

I build the tools systematic traders actually need — and I understand markets, not just code.

I specialize in trading bots, strategy backtesting, market dashboards, and financial-data pipelines. My focus is the difference between a backtest that looks good and one that's real — the strategies I build come with honest statistics, not curve-fit fantasies.

What I build

  • Trading bots & Expert Advisors (Python, MT5/MT4) with real risk controls — position sizing, daily-loss limits, prop-firm rules
  • Strategy backtesting — survivorship-free, realistic costs, deflated-Sharpe / walk-forward screening
  • Trading dashboards & data pipelines — Python · FastAPI · React
  • AI automation & LLM integration (GPT / Claude / Gemini APIs) — document pipelines, scheduled jobs, workflow automation with verification built in

Featured projects

  • pit-data-sample — a free, real-data sample of a survivorship-free, point-in-time NSE/BSE dataset: delisted names included, fundamentals stamped with their real announcement date, CA-adjusted total-return prices. Ships with a runnable notebook and a worked case study — the same vanilla value screen "earns" ~+4 pp/yr more once you let it cheat with survivorship + look-ahead bias. CC-BY 4.0.
  • nse-factor-backtest — research-grade factor backtester with a pre-registered, one-shot held-out test that rejected its own strategy. Most repos show winners; this one shows the machinery.
  • nse-fundamental-screener — ranks stocks by Magic Formula / Quality / Piotroski F-Score from primary balance-sheet data.
  • kite-execution-pipeline — SEBI-compliant Zerodha execution layer: target-diff orders, limit-only, human-in-the-loop, 11 tests.
  • quant-cockpit — real-time trading dashboard (live equity, positions, risk) in React + FastAPI.

The point-in-time reconstruction pipeline behind the data — bhavcopy + corporate actions + XBRL, ~29k filings across 1,300+ names — is proprietary. pit-data-sample is exactly what it produces; diff it against whatever you use today, or open an issue to have it measured on your own tickers.

Tech

Python · pandas · NumPy · SciPy · FastAPI · React · Vite · MQL5 · Pine Script

Work with me

I take freelance work in trading systems, backtesting, and market tooling. Tell me your strategy or idea and I'll tell you honestly whether it's buildable and how I'd test it.

Available for freelance work: Fiverr — AI automation, backtesting, and trading systems.

Pinned Loading

  1. pit-data-sample pit-data-sample Public

    A runnable notebook showing how survivorship + look-ahead bias inflate Indian equity backtests — and the point-in-time data that fixes it.

    Jupyter Notebook

  2. nse-fundamental-screener nse-fundamental-screener Public

    Ranks NSE stocks by Magic Formula, Quality & Piotroski F-Score from primary balance-sheet data.

    Python 2

  3. mql5-expert-advisors mql5-expert-advisors Public

    Clean, risk-managed MetaTrader 5 Expert Advisors — ATR stops, risk-% position sizing, daily-loss guards. MQL5 done properly, tested honestly.

    MQL5

  4. nse-factor-backtest nse-factor-backtest Public

    Research-grade NSE factor backtester — deflated Sharpe, survivorship-free, pre-registered.

    Python 1

  5. quant-cockpit quant-cockpit Public

    Real-time trading dashboard — live equity, positions, risk & research telemetry. React + FastAPI.

    JavaScript

  6. pykiteconnect pykiteconnect Public

    Forked from zerodha/pykiteconnect

    The official Python client library for the Kite Connect trading APIs

    Python