<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Trading on Antoine Boucher</title><link>https://antoineboucher.info/CV/blog/tags/trading/</link><description>Recent content in Trading on Antoine Boucher</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 13 Apr 2026 10:00:00 -0400</lastBuildDate><atom:link href="https://antoineboucher.info/CV/blog/tags/trading/index.xml" rel="self" type="application/rss+xml"/><item><title>Python library for MarketWatch virtual trading</title><link>https://antoineboucher.info/CV/blog/posts/marketwatch-python-trading/</link><pubDate>Mon, 13 Apr 2026 10:00:00 -0400</pubDate><guid>https://antoineboucher.info/CV/blog/posts/marketwatch-python-trading/</guid><description>&lt;p&gt;I published &lt;strong&gt;&lt;a href="https://pypi.org/project/marketwatch/"&gt;marketwatch&lt;/a&gt;&lt;/strong&gt; on PyPI: a small Python client for the &lt;a href="https://www.marketwatch.com"&gt;MarketWatch&lt;/a&gt; &lt;strong&gt;virtual stock game&lt;/strong&gt; (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.&lt;/p&gt;
&lt;h2 id="links"&gt;Links&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Package:&lt;/strong&gt; &lt;a href="https://pypi.org/project/marketwatch/"&gt;pypi.org/project/marketwatch&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Documentation:&lt;/strong&gt; &lt;a href="https://antoinebou12.github.io/marketwatch/"&gt;antoinebou12.github.io/marketwatch&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Source &amp;amp; issues:&lt;/strong&gt; &lt;a href="https://github.com/antoinebou12/marketwatch"&gt;github.com/antoinebou12/marketwatch&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="what-it-can-do"&gt;What it can do&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Create and manage &lt;strong&gt;watchlists&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;Read &lt;strong&gt;game&lt;/strong&gt; details and settings&lt;/li&gt;
&lt;li&gt;Inspect &lt;strong&gt;portfolio&lt;/strong&gt;, positions, and pending orders&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Buy&lt;/strong&gt; and &lt;strong&gt;sell&lt;/strong&gt; (in-game)&lt;/li&gt;
&lt;li&gt;Fetch the &lt;strong&gt;leaderboard&lt;/strong&gt; for a game&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Useful if you are exploring automated strategies or small bots &lt;strong&gt;inside the game’s rules&lt;/strong&gt;—see the docs for method names and return shapes.&lt;/p&gt;</description></item><item><title>Multiple Technical Indicators Backtesting on Multiple Tickers using Python</title><link>https://antoineboucher.info/CV/blog/posts/multiple-indicators-backtesting/</link><pubDate>Thu, 30 May 2024 15:00:00 -0400</pubDate><guid>https://antoineboucher.info/CV/blog/posts/multiple-indicators-backtesting/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;In this report, we present an experiment with technical indicators using the BatchBacktesting project available on GitHub at the following link: &lt;a href="https://github.com/AlgoETS/BatchBacktesting"&gt;BatchBacktesting&lt;/a&gt;.&lt;/p&gt;
&lt;h2 id="installing-dependencies"&gt;Installing Dependencies&lt;/h2&gt;
&lt;p&gt;To get started, install the necessary libraries:&lt;/p&gt;
&lt;p&gt;!pip install numpy httpx richp&lt;/p&gt;
&lt;h2 id="importing-modules"&gt;Importing Modules&lt;/h2&gt;
&lt;p&gt;Here are the modules to import for the script:&lt;/p&gt;
&lt;p&gt;import pandas as pd&lt;br&gt;
import numpy as np&lt;br&gt;
from datetime import datetime&lt;br&gt;
import httpx&lt;br&gt;
import concurrent.futures&lt;br&gt;
import glob&lt;br&gt;
import warnings&lt;br&gt;
from rich.progress import track&lt;/p&gt;</description></item><item><title>Experimenting with technical indicators using Python and backtesting</title><link>https://antoineboucher.info/CV/blog/posts/experimentation-indicateurs-backtesting/</link><pubDate>Tue, 14 May 2024 20:00:00 -0400</pubDate><guid>https://antoineboucher.info/CV/blog/posts/experimentation-indicateurs-backtesting/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;In this report, we present an experiment with technical indicators using the BatchBacktesting project available on GitHub at the following link: &lt;a href="https://github.com/AlgoETS/BatchBacktesting"&gt;BatchBacktesting&lt;/a&gt;.&lt;/p&gt;
&lt;h2 id="installing-dependencies"&gt;Installing Dependencies&lt;/h2&gt;
&lt;p&gt;To get started, install the necessary libraries:&lt;/p&gt;
&lt;p&gt;!pip install numpy httpx richp&lt;/p&gt;
&lt;h2 id="importing-modules"&gt;Importing Modules&lt;/h2&gt;
&lt;p&gt;Here are the modules to import for the script:&lt;/p&gt;
&lt;p&gt;import pandas as pd&lt;br&gt;
import numpy as np&lt;br&gt;
from datetime import datetime&lt;br&gt;
import httpx&lt;br&gt;
import concurrent.futures&lt;br&gt;
import glob&lt;br&gt;
import warnings&lt;br&gt;
from rich.progress import track&lt;/p&gt;</description></item></channel></rss>