Python on Antoine Boucher

Exploring movie similarities with vector search algorithms

Mon, 13 Apr 2026 12:00:00 -0400

This is a single walkthrough of a movie similarity thread: Part 1 stores embeddings in PostgreSQL + pgvector and runs nearest-neighbor search in SQL; Part 2 uses Qdrant with MovieLens (dense text vectors for semantic search and sparse rating vectors for collaborative-style recommendations); Part 3 turns the same pgvector-backed catalog into the retrieval layer for a small RAG pipeline with LangChain and Ollama. Below are short GIFs from that work (movie-similarities-1.gif … 3.gif in this page bundle).

Python library for MarketWatch virtual trading

Mon, 13 Apr 2026 10:00:00 -0400

I published marketwatch on PyPI: a small Python client for the MarketWatch virtual stock game (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.

What it can do

Create and manage watchlists
Read game details and settings
Inspect portfolio, positions, and pending orders
Buy and sell (in-game)
Fetch the leaderboard for a game

Useful if you are exploring automated strategies or small bots inside the game’s rules—see the docs for method names and return shapes.

A Method to Install Python Packages for Add-ons & Plugins in Blender (Windows, Blender 4.2+)

Sat, 08 Feb 2025 12:00:00 -0400

Introduction

Blender is a powerhouse for 3D creation, offering a Python API that allows users to extend its functionality with scripts, add-ons, and plugins. However, one challenge developers face is installing external Python packages within Blender’s isolated Python environment.

Unlike system-wide Python installations, Blender bundles its own Python interpreter, making standard package installations tricky. This article presents a more general and robust method to install Python dependencies for Blender add-ons and plugins — ensuring a smooth workflow across different versions.

ChatGPT plugin with FastAPI — implementation outline

Sat, 01 Jun 2024 10:00:00 -0400

OpenAI-style plugins expose an HTTP API described by an OpenAPI document so ChatGPT can call your tools safely. FastAPI generates OpenAPI for you, which fits this model well.

1. Define the API in FastAPI

Routes return JSON with stable shapes (no ambiguous free text where structure matters).
Add summaries and descriptions on paths and fields — they help the model choose the right tool.

2. Publish `openapi.json`

FastAPI serves /openapi.json by default; the plugin manifest points at this URL (or a static copy you version).
Keep schemas tight: enums, required fields, and examples reduce bad calls.

3. Plugin manifest

Host ai-plugin.json (or the format required by the current OpenAI developer docs) over HTTPS.
Manifest references your API base URL and OpenAPI location.

4. Auth

Prefer OAuth or API keys as documented for your integration; never commit secrets.
Validate tokens inside FastAPI dependencies or middleware.

5. Deploy

HTTPS endpoint reachable from OpenAI’s servers.
Logging and idempotency for side-effecting routes.

6. Test manually

Call routes with curl or HTTPie using the same payloads the model will send.
Iterate on descriptions and constraints before exposing wide traffic.

Details change with OpenAI’s platform updates — always follow the latest plugin / tools / actions documentation when wiring production apps.

Multiple Technical Indicators Backtesting on Multiple Tickers using Python

Thu, 30 May 2024 15:00:00 -0400

Introduction

In this report, we present an experiment with technical indicators using the BatchBacktesting project available on GitHub at the following link: BatchBacktesting.

Installing Dependencies

To get started, install the necessary libraries:

!pip install numpy httpx richp

Importing Modules

Here are the modules to import for the script:

import pandas as pd
import numpy as np
from datetime import datetime
import httpx
import concurrent.futures
import glob
import warnings
from rich.progress import track

Experimenting with technical indicators using Python and backtesting

Tue, 14 May 2024 20:00:00 -0400

Introduction

In this report, we present an experiment with technical indicators using the BatchBacktesting project available on GitHub at the following link: BatchBacktesting.

Installing Dependencies

To get started, install the necessary libraries:

!pip install numpy httpx richp

Importing Modules

Here are the modules to import for the script:

import pandas as pd
import numpy as np
from datetime import datetime
import httpx
import concurrent.futures
import glob
import warnings
from rich.progress import track

Predicting Stock Prices with Monte Carlo Simulations

Tue, 14 May 2024 09:00:00 -0400

Introduction

In finance, decisions are rarely about a single “forecast” price: they are about ranges, tail risk, and how wrong simple models can be. This article walks through a Monte Carlo path simulation 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 distribution—the right object for risk-style questions (bands, percentiles, coverage against a hold-out period).

Python on Antoine Boucher

Exploring movie similarities with vector search algorithms

Python library for MarketWatch virtual trading

Links

What it can do

A Method to Install Python Packages for Add-ons & Plugins in Blender (Windows, Blender 4.2+)

Introduction

ChatGPT plugin with FastAPI — implementation outline

1. Define the API in FastAPI

2. Publish openapi.json

3. Plugin manifest

4. Auth

5. Deploy

6. Test manually

Multiple Technical Indicators Backtesting on Multiple Tickers using Python

Introduction

Installing Dependencies

Importing Modules

Experimenting with technical indicators using Python and backtesting

Introduction

Installing Dependencies

Importing Modules

Predicting Stock Prices with Monte Carlo Simulations

Introduction

2. Publish `openapi.json`