Backtesting on Antoine Boucher

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).