This tool allows Excel to be the main interface for trading through the Alpaca broker Below are two demo videos of the tool This tool allows the following to be done via Excel Some aspects of the tool that can be customized are Known Limitations Please note: this has only been tested on windows based machines Link to tool for purchase
As options traders and learners, we often talk about the Greeks — delta, gamma, theta, vega, rho, and their second-order cousins like vanna and vomma. But rarely do we get to see how they behave in real time. I built this fully interactive, educational dashboard using Python and Streamlit to help visualize and better understand how these risk measures respond to changing market conditions. The goal?To create something...
After running over 3,500 backtests across every S&P 500 stock, I wanted a better way to explore the data interactively. So I built a Streamlit dashboard where you can filter by: The goal is to quickly compare performance across strategies, spot outliers, and analyze trends. You can view the full app locally or host it. A demo of the streamlit app can be found on YouTube Closing This...
In Part I, we compared rule-based vs reinforcement learning (RL) trading strategies on AAPL using a 2-year backtest of daily data. Now, we’re scaling that idea across all 500+ tickers in the S&P 500. What happens when we apply the same logic to the entire market? We keep the same seven models and run a uniform backtest across all tickers. Our goal: uncover which models are consistently effective,...
This is the first post in a 3-part series where I explore how rule-based logic compares to reinforcement learning (RL) when building trading strategies. We’ll start simple by testing a handful of models on AAPL — one of the most traded stocks in the world — using two years of daily price data from Yahoo Finance. You’ll see how different models behave under the same conditions, and we’ll...
I recently came across a forum post where someone asked about creating a scheduling system using Google Sheets. As I thought about it, I realized that while scheduling is important, why stop there? A complete system should also handle time-off requests, payroll calculations, and paystub distribution—all while keeping things simple and automated. That idea led to this restaurant workforce automation system, which uses Google Sheets and Google Apps...
Introduction This final part summarizes trade performance and identifies optimal trade setups. We generate a ranked list of the best opportunities based on historical data. Trade Success Rates by Setup We calculate the average success rate of different trade setups based on entry time, option type, and strike distance. Option Type Moneyness Strike Distance Entry Time % Achieving 5% Profit Call ATM 0 09:31:00 100.0% Call ITM -7...
Introduction In this section, we apply logistic regression and clustering techniques to better understand trade profitability. We analyze whether specific attributes—such as time of entry, moneyness, and strike distance—affect the likelihood of hitting a profit target. We also explore Cohen’s d effect size measurement and perform survival analysis to determine trade longevity. Logistic Regression for Profit Prediction To predict the probability of an option reaching its profit target,...
Overview This post explores whether options reach predefined profit targets and identifies optimal trade entry times. Key Question Explored Key Functions & Outputs Findings from this section inform strategy development, helping traders maximize returns.
Overview Before diving into hypothesis testing, we need to clean, preprocess, and analyze our dataset. This post walks through the process of merging options and underlying asset data, labeling moneyness, and adding technical indicators. Key Steps Key Functions & Outputs This stage provides an intuitive understanding of how options behave relative to the underlying asset, setting the stage for deeper analysis.
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