https://antoineboucher.info/CV/blog/tags/finance/Recent content in Finance on Antoine BoucherHugoen-usMon, 13 Apr 2026 10:00:00 -0400https://antoineboucher.info/CV/blog/posts/marketwatch-python-trading/Mon, 13 Apr 2026 10:00:00 -0400https://antoineboucher.info/CV/blog/posts/marketwatch-python-trading/<p>I published <strong><a href="https://pypi.org/project/marketwatch/">marketwatch</a></strong> on PyPI: a small Python client for the <a href="https://www.marketwatch.com">MarketWatch</a> <strong>virtual stock game</strong> (paper trading), not live brokerage access. If you want to script watchlists, pull game or portfolio data, or experiment with automation against the game, it wraps the flows in a straightforward API.</p> <h2 id="links">Links</h2> <ul> <li><strong>Package:</strong> <a href="https://pypi.org/project/marketwatch/">pypi.org/project/marketwatch</a></li> <li><strong>Documentation:</strong> <a href="https://antoinebou12.github.io/marketwatch/">antoinebou12.github.io/marketwatch</a></li> <li><strong>Source &amp; issues:</strong> <a href="https://github.com/antoinebou12/marketwatch">github.com/antoinebou12/marketwatch</a></li> </ul> <h2 id="what-it-can-do">What it can do</h2> <ul> <li>Create and manage <strong>watchlists</strong></li> <li>Read <strong>game</strong> details and settings</li> <li>Inspect <strong>portfolio</strong>, positions, and pending orders</li> <li><strong>Buy</strong> and <strong>sell</strong> (in-game)</li> <li>Fetch the <strong>leaderboard</strong> for a game</li> </ul> <p>Useful if you are exploring automated strategies or small bots <strong>inside the game’s rules</strong>—see the docs for method names and return shapes.</p>https://antoineboucher.info/CV/blog/posts/predicting-stock-prices-monte-carlo/Tue, 14 May 2024 09:00:00 -0400https://antoineboucher.info/CV/blog/posts/predicting-stock-prices-monte-carlo/<h2 id="introduction">Introduction</h2> <p>In finance, decisions are rarely about a single “forecast” price: they are about <strong>ranges</strong>, <strong>tail risk</strong>, and <strong>how wrong</strong> simple models can be. This article walks through a <strong>Monte Carlo path simulation</strong> in Python: we estimate drift and volatility from historical closes, simulate many future price paths (a geometric Brownian–style discrete step), and summarize the result as a <strong>distribution</strong>—the right object for risk-style questions (bands, percentiles, coverage against a hold-out period).</p>