This blog article talks about how to calculate how much your position is deviating from a given error threshold and then conditionally rebalancing your portfolio while showcashing how to use assert() calls for protection during renaming/restructuring code phases.

This blogpost fetches historical funding rates for BTCUSDT perpetual futures using Bybit API and incorporates them into the cost calculations of our backtest.

This blogpost shows how to calculate the annual turnover of a simple one instrument, one trading rule strategy to be used during cost calculations.

This blogpost discusses a very basic first prototype of a backtester written in python. It highlights common pitfalls such as not incorporating costs or not catching lookahead bias when calculating your returns.

This article provides an introduction to Operations Security (OpSec), common threats, and practical steps to enhance personal security and privacy, including setting up and configuring a secure Linux-based system as first step.

This blogpost showcases how to transform continuous forecasts into position sizing using an annual risk target of 20%.

This blog post explains how to turn the raw output of a trading rule into a scaled forecast so it means the same across instruments and time using the EMA Crossover (8;32) as example

A blogpost with a very basic primer about data cleaning and processing, showcasing calculating exponentially moving averages from a historical price series.

This blogpost highlights the importance of using an index when searching in SQL database columns and how to use the EXPLAIN statement to pinpoint and resolve performance bottlenecks.

This article summarizes how to build a historical, survivorship bias-free data hub for crypto tokens using multiple service layers with Docker and Docker Compose.

This article gives a solid summary on how to create synthetic price data and its benefits of using for the purpose of backtesting, mainly eliminating overfitting risk.

Backtests are unreliable for research as they often lead to overfitting; instead, use them as sanity checks and focus on developing trading ideas based on fundamental market understanding.

This blog post outlines the essential components and considerations for developing a robust trading strategy, using the analogy of baking a cake to illustrate different approaches and their trade-offs, and then presents an easy framework to memroize to guide you through the steps of creating your own trading strategy.

This blog post explains the benefits and risks of futures trading, emphasizing the importance of leverage management and providing detailed calculations for position sizing, margin requirements, and profit and loss scenarios. It also highlights the differences between futures and spot trading, including the concepts of rolling and backadjusting contracts.

This article explains equity curves, compares two trading strategies using synthetic data, and highlights the importance of considering risk through metrics like the Sharpe Ratio over simple return comparisons.

The 4 F's of backtesting outline a structured approach to backtesting trading strategies, emphasizing the importance of acquiring and validating quality historical data, forecasting performance metrics, and fine-tuning the model while avoiding biases like overfitting to ensure reliable and robust trading outcomes.

This article explores quantifying and managing risk in trading through metrics like Standard Deviation and the Sharpe Ratio, using a basic Bitcoin buy-and-hold strategy to illustrate the concepts, and highlights the need for further strategy optimization for better performance.

The blog post argues that trading is often mislabeled as excessively risky due to its comparison to gambling, but through understanding and applying concepts such as probability, risk versus reward, and expected value, the risks can be effectively managed, potentially making trading less risky than some other small businesses when approached with adequate knowledge and capital.

Horoshi バガボンド shares his journey from being an avid strategy game player and self-taught developer to becoming the founder of Vagabond Research, an educational trading firm specializing in quantitative trading. He delves into his experiences with automation in various domains, including online poker and Print on Demand, and ultimately finds his passion in developing profitable automated trading systems.