Only one in five day traders is profitable. Algorithmic trading improves these odds through better strategy design, testing, and execution. In this post, I’m going to discuss what it took for me to become a successful retail algorithmic trader. I …
Look-ahead bias occurs by using information not available or known in the analysis period for a study or simulation, leading to inaccurate results. An often-overlooked point regarding look-ahead bias is that it isn’t just about when a data observation becomes …
A time series is a sequence of moments-in-time observations. The sequence of data is either uniformly spaced at a specific frequency such as hourly or sporadically spaced in the case of a phone call log. Having an expert understanding of …
Pandas was developed at hedge fund AQR by Wes McKinney to enable quick analysis of financial data. Pandas is an extension of NumPy that supports vectorized operations enabling fast manipulation of financial information. I’ll be using company data provided by an Intrinio developer …
Rapid increases in technology availability have put systematic and algorithmic trading within reach for the retail trader. Below you’ll find a curated list of trading platforms and frameworks, broker-dealers, data providers, and other helpful trading libraries for aspiring Python traders …
Backtrader is an open-source python framework for trading and backtesting. Backtrader allows you to focus on writing reusable trading strategies, indicators, and analyzers instead of having to spend time building infrastructure. I think of Backtrader as a Swiss Army Knife for …
We’re going to be populating our equity backtesting database with stock market data from Intrinio. Intrinio provides access to its data through both CSV bulk downloads and APIs. In this article, I’m going to cover importing the data using the Intrinio …
There’s a lot of misinformation on the types of traders and trading styles. I’m here to clear the confusion. A trading type depends on the trader’s activity discretion, concentration, data used, position quantity, and trade frequency. Type Determining Factor Active …
Survivorship bias is the risk of analyzing investment performance using only “surviving” constituents of a selected investment universe instead of incorporating all current and historical constituents over the performance period. Survivorship bias artificially increases long performance and detracts from short-selling …
You can pay to get the S&P 500 historical constituents or use various free sources. Due to survivorship bias, getting an accurate list of the S&P500 components over time is critical when developing a trading strategy. Getting the S&P500 Historical …