The Random Path to Stock-Market Riches

Plus, more links to make you a little bit smarter today.

Creating A Halloween Game But I Made It Too Scary...

Why You Hate Diversity In Media

When it comes to storytelling, no one hates diversity. People might say they hate diversity, but what they’re talking about is something completely different. Here’s why.

The Nature of Code

Over a decade ago, I self-published The Nature of Code, an online resource and print book exploring the unpredictable evolutionary and emergent properties of nature in software via the creative coding framework Processing. It’s the understatement of the century to say that much has changed in the world of technology and creative media since then, and so here I am again, with a new and rebooted version of this book built around JavaScript and the p5.js library. The book has a few new coding tricks this time, but it’s the same old nature—birds still flap their wings, and apples still fall on our heads.

The Random Path to Stock-Market Riches

How we made 80% in a year without really trying

Is Strategy design due for a shakeup?

As I think about the strategy genre, and the complexity of them, I wonder if it’s time for a structural change to how strategy and tactics games are made.

Hedging American Put Options with Deep Reinforcement Learning

This article leverages deep reinforcement learning (DRL) to hedge American put options, utilizing the deep deterministic policy gradient (DDPG) method. The agents are first trained and tested with Geometric Brownian Motion (GBM) asset paths and demonstrate superior performance over traditional strategies like the Black-Scholes (BS) Delta, particularly in the presence of transaction costs. To assess the real-world applicability of DRL hedging, a second round of experiments uses a market calibrated stochastic volatility model to train DRL agents. Specifically, 80 put options across 8 symbols are collected, stochastic volatility model coefficients are calibrated for each symbol, and a DRL agent is trained for each of the 80 options by simulating paths of the respective calibrated model. Not only do DRL agents outperform the BS Delta method when testing is conducted using the same calibrated stochastic volatility model data from training, but DRL agents achieves better results when hedging the true asset path that occurred between the option sale date and the maturity. As such, not only does this study present the first DRL agents tailored for American put option hedging, but results on both simulated and empirical market testing data also suggest the optimality of DRL agents over the BS Delta method in real-world scenarios. Finally, note that this study employs a model-agnostic Chebyshev interpolation method to provide DRL agents with option prices at each time step when a stochastic volatility model is used, thereby providing a general framework for an easy extension to more complex underlying asset processes.

Adobe Photoshop Source Code

When brothers Thomas and John Knoll began designing and writing an image editing program in the late 1980s, they could not have imagined that they would be adding a word to the dictionary.