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MartinValachy/README.md

BA (Mathematical) Finance student at University of Zurich · concurrent mathematics at ETH Zürich · Zurich, Switzerland

One question through most of this: how do you tell a real edge from a lucky backtest?

Strategy-Zoo — 780 multi-asset configurations, enumerated and committed before any of them ran, then corrected for having tried all 780. Best Sharpe 0.67, deflated 0.639. Zero survive Bonferroni, Holm or BHY at 5%.

Volatility Risk Premium — Does the market overpay for insuring against turbulence? Confirmed on 84–86% of trading days across three measurement windows. A term-structure de-risking rule cut losses in the two real crashes tested (Feb 2018, Mar 2020) by roughly 70–76%, at no cost to everyday profit.

Equity Factor Timing — Can the value spread time HML? Four pre-registered rules on Kenneth French data 1983–2026, block-bootstrap CIs. One looked significant until it was corrected for the other three. Null.

Monte Carlo Option Pricing — European, American (Longstaff–Schwartz) and Asian options by Monte Carlo, with pathwise Greeks and variance reduction.

Sporitelna Deposit Forecasting — Retail loan and deposit forecasting for Slovak households, built in R at Slovenská sporiteľňa (Erste Group) and used internally for scenario planning.

The trial count gets written down before the first backtest runs, confidence intervals are block-bootstrapped, and null results stay published.

Python · R · LaTeX · SQL valachy.mar@gmail.com · LinkedIn

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  1. Sporitelna-Deposit-Forecasting Sporitelna-Deposit-Forecasting Public

    Retail loan and deposit forecasting for Slovak households: 10 macro drivers, lags to 12 months. Built in R at Slovenská sporiteľňa (Erste Group).

    R

  2. Equity-Factor-Timing Equity-Factor-Timing Public

    Testing whether the value spread times the HML(High minus Low) factor. Four pre-registered variants, none survive multiple-testing correction.

    Jupyter Notebook

  3. Monte-Carlo-Option-Pricing Monte-Carlo-Option-Pricing Public

    European, American (Longstaff-Schwartz) and Asian option pricing by Monte Carlo, with pathwise Greeks and variance reduction.

    Jupyter Notebook

  4. Strategy-Zoo Strategy-Zoo Public

    780 pre-registered multi-asset backtests, corrected for multiple testing. 0 survive at 5%.

    Python

  5. Volatility-Risk-Premium Volatility-Risk-Premium Public

    Jupyter Notebook