Example Tools Used.

Tradestation

I use the TradeStation platform as a market analysis and screening tool across equities, futures, and other financial instruments. I use it to run screens, monitor technical indicators, and track price trends, volatility, and market behavior across different asset classes. I also use TradeStation’s API to export market data and integrate it into my Python and Excel-based models for further analysis and research. This allows me to combine market data with my own indicators, forecasting models, and analytics workflows. The platform is an important part of my overall process for market monitoring, data collection, and quantitative analysis.

Visual Studio

I use Python in Visual Studio to build custom analysis tools, automate data collection, and connect different parts of my workflow across Excel, PowerPoint, Word, and Outlook. Using API connections, Python can pull financial and economic data from external data providers, process and organize the data, and then push the results into Excel for modeling, charting, and further analysis. From there, Python can automatically update PowerPoint presentations with new charts and data, generate Word reports with updated tables and commentary, and help prepare Outlook emails with attachments or summaries for distribution. This allows data to move seamlessly between programs and significantly reduces the amount of manual work required to update reports, presentations, and investor materials. The goal of this system is to create a repeatable workflow where data is collected, analyzed, and turned into reports and presentations efficiently, which supports research, investor communication, and business development activities.

Excel Python

I use Python directly within Excel to manage data, run custom analysis, and integrate data from external API data sources. This allows me to pull financial and economic data directly into Excel, process and structure the data using Python, and then use Excel for modeling, analysis, and charting. Using Python within Excel makes it possible to clean data, run statistical analysis, build custom indicators, and automate repetitive data tasks without leaving the Excel environment. This is particularly useful for working with large financial datasets, time-series data, and models that need to be updated frequently. Combining Python and Excel creates a flexible environment for financial modeling, data analysis, and building repeatable research and reporting workflows.

FRED API Plugin

I use the Federal Reserve Economic Data (FRED) API within Excel to pull macroeconomic and interest rate data directly into my models and research dashboards. This allows me to automatically update datasets such as Treasury yields, inflation data, housing indicators, employment data, and other economic time series without manual data entry. The data can then be used in Excel models, charts, and market indicators that track changes in economic conditions and capital markets. Using the FRED API ensures that macroeconomic data is always current and allows economic indicators to be integrated directly into financial models and research reports. This is an important part of my workflow for tracking interest rates, economic trends, and broader market conditions.

Apollo.io CRM

I use Apollo.io as an AI-assisted outreach and CRM platform to manage business development campaigns and investor communication. The platform allows me to segment contacts based on firm type, investment focus, geography, and other criteria, and then generate customized emails and outreach messages tailored to each group. This is particularly useful when sharing research reports, presentations, and investment materials, because messages can be framed differently depending on whether the recipient is a family office, RIA, institutional investor, or operating partner. The platform also helps track communication history, manage follow-ups, and maintain an organized outreach pipeline. This creates a more structured and scalable process for business development, investor outreach, and relationship management.

Connecting MS Office w/Python

I use Python together with Microsoft Excel, PowerPoint, and Word to automate repetitive tasks and speed up the process of turning data into analysis and presentation materials. Data can be pulled from external sources using APIs, processed and analyzed in Python, sent into Excel for modeling and charts, and then exported into PowerPoint and Word for reports and presentations. This allows analysis, charts, and written materials to be updated quickly and consistently without manually rebuilding reports each time data changes. This type of workflow is especially useful when preparing research reports, investor presentations, and other materials used for business development and strategic discussions. The overall goal is to create a system where data flows efficiently from analysis to finished materials, making it easier to communicate ideas, opportunities, and market insights.

Preqin Database

I use Preqin as a research and business development tool to identify and research institutional investors, family offices, private credit funds, and real estate investors. The platform is useful for understanding investor focus, allocation strategies, fund activity, and key contacts, which helps support targeted outreach and capital raising efforts. It is also helpful for researching competing funds, understanding how different strategies are positioned in the market, and identifying potential investors and strategic partners. Preqin is used as part of a broader process for investor research, relationship development, and business development planning.

New Projects in Development.

Itellizence Database

I’ve recently been working with the Intellizence API, which provides near real-time business signals such as M&A activity, startup funding, leadership changes, expansions, and layoffs. I’m interested in using this type of data to build API-driven analytics that track activity across venture capital and private equity markets. What I find interesting is that it turns unstructured information like news and company announcements into structured data that can actually be tracked and analyzed over time. For me, this is part of a broader interest in using APIs, data, and automation to build better research tools and better ways to monitor what is happening across markets and industries.

CDR.fyi Database

I use the cdr.fyi platform and API as part of my carbon market research and carbon price forecasting work. The API allows me to pull data on carbon removal transactions, pricing, and market activity, which I then use to build forecasting models and analytics related to ICE futures exchange–traded carbon contracts. I’m particularly interested in this area because carbon markets are still developing, and I think there is a significant opportunity to apply data analysis, forecasting, and market research tools to better understand pricing trends and potential market opportunities.

Open Weather Database

I use the OpenWeather API as part of my work on natural gas and energy market forecasting. Weather plays a major role in short-term natural gas demand, particularly through heating and cooling demand, so I use weather data as an input alongside traditional market data when building price forecasting models. The goal isn’t to replace direct price forecasting tools, but to supplement them with weather-driven demand indicators that can help explain short-term price movements and improve forecasting accuracy. This same weather data is also useful for energy market analysis more broadly and for building weather-related derivative models, since temperature, storms, and seasonal patterns can have a significant impact on energy consumption and pricing.

Pyth Network Database

I use data from the Pyth Network as part of my work tracking liquidity conditions and market stress across financial markets. Pyth provides real-time price data across a wide range of asset classes, and I use this data to monitor price movements, spreads, and market behavior across different markets to help identify periods of tightening liquidity or increased market stress. By tracking how different asset classes move relative to each other and how quickly prices adjust during volatile periods, I use this data as part of a broader framework for measuring liquidity conditions and financial market stress. This information is incorporated into my broader market monitoring and leading-indicator reports to help identify changes in market conditions and risk sentiment over time.

CoinMarket Cap Database

I use data from CoinMarketCap as part of my work tracking digital asset markets, ETF flows, and broader market sentiment. In particular, I track ETF flow activity and use data from the Crypto Fear and Greed Index as a way to monitor sentiment and risk appetite in digital asset markets over time. I also follow several of CoinMarketCap’s proprietary indices, including the CMC Crypto 200 Index, the CMC Altcoin Season Index, and Bitcoin Dominance data, which help provide a broader view of market trends, sector rotation, and capital flows within the digital asset ecosystem. I view digital asset markets as another useful area for understanding liquidity conditions, speculative behavior, and overall risk appetite, and I incorporate this data into my broader market monitoring and leading-indicator framework.

CarGurus Database

I also work with used car pricing data from CarGurus as part of my broader interest in economic indicators and consumer behavior. I’ve been particularly interested in studying periods where used car prices act as a leading indicator for other economic data, especially inflation and consumer price trends. Because used car prices respond quickly to changes in consumer demand, financing conditions, and supply constraints, they can sometimes provide early signals about broader consumer price patterns and overall market health. By tracking used car price trends and comparing them to other economic indicators, I use this data as part of a broader framework for understanding consumer behavior, inflation trends, and the direction of the economy.

Chrono24 Chronopulse Database

I work with luxury watch pricing data from Chrono24, specifically through their ChronoPulse index data, as part of my interest in alternative asset prices and consumer behavior at the high end of the market. Luxury watch prices can be an interesting indicator because they often reflect discretionary spending, wealth sentiment, and liquidity conditions among higher-income consumers and collectors. By tracking price trends across major watch brands and models, I use this data to monitor trends in luxury asset prices and to study how high-end consumer markets respond to changes in financial markets, interest rates, and overall economic conditions. I view this type of data as another useful piece of the puzzle when trying to understand broader market sentiment, asset price cycles, and consumer behavior across different segments of the economy.

Polymarket Database

I use the Polymarket API to pull data from economy-based event contracts and prediction markets, and I incorporate this probability data directly into my reports and research. This includes market-implied probabilities for events such as Federal Reserve interest rate decisions, inflation (CPI) outcomes, unemployment rate outcomes, recession probabilities, government shutdown risk, debt ceiling outcomes, and Treasury yield range expectations. The goal is to track how these market-implied probabilities change over time and use them as forward-looking indicators, since prediction markets often reflect real-time expectations from market participants. I include this data as part of my broader leading-indicator framework to help monitor shifts in economic expectations, policy outlook, and overall market sentiment.

Statista Database

I use Statista consumer survey data as part of my leading-indicator work to incorporate consumer sentiment, housing intentions, and financial stress indicators into my reports as forward-looking measures of demand and credit health. Consumer surveys can be useful because they often capture changes in behavior and expectations before those changes show up in hard economic data. In addition to survey data, I also track quantitative data from major news sources, such as the number of articles mentioning credit tightening, defaults, foreclosures, refinancing activity, distressed sales, and liquidity conditions. By tracking the frequency of these topics over time, it is possible to build simple news-based indices that help support or challenge headline market narratives and provide another way to monitor changes in credit conditions, housing markets, and overall financial stress in the economy.

News API Database

I use news-based data feeds through the News API to build qualitative, text-based indices that track market activity, sentiment, and stress levels across different parts of the market, particularly in private credit. One of the challenges in private credit is that a large portion of the activity takes place through non-bank and non-traditional channels, which means a lot of important information does not show up in standard economic datasets right away. By pulling data from news sources, press releases, and industry publications, I can track things like deal announcements, lender activity, fundraising announcements, defaults, restructurings, and general market commentary. Using word counts, sentiment analysis, and the frequency of certain terms and topics being mentioned, it’s possible to build qualitative indices that measure market activity, sentiment, and stress levels based on what people in the industry are actually talking about, rather than relying only on traditional quantitative data. I use this as part of my broader effort to build leading indicators that help track changes in private credit conditions and overall market health.

Currency Array Analyzer

I use the Currency Array indicator to monitor the relative strength and weakness of major global currencies by tracking all 28 major currency pairs in a single interface. This allows me to quickly identify which currencies are strengthening or weakening, which pairs are trending, and which markets are moving sideways or showing signs of consolidation. By viewing currencies as a group rather than as individual pairs, the indicator helps identify broader macro trends, shifts in risk sentiment, and divergence between currencies within the same currency complex. This is particularly useful for understanding global capital flows, interest rate expectations, and macroeconomic sentiment across regions. The tool is used as part of my broader macro and cross-asset analysis to help monitor global financial market trends and currency-driven macro conditions.

ATTOM Data API

I use ATTOM Data’s real estate API platform to access detailed, property-level data across residential assets by querying specific properties using addresses, parcel numbers, or geographic coordinates. This allows me to retrieve structured information on ownership history, transaction activity, tax assessments, and property characteristics, which I integrate directly into my underwriting and analytical workflows. The platform helps streamline deal evaluation by enabling automated validation of property details, identification of potential risks, and more efficient screening of opportunities. By combining this bottom-up property data with broader macroeconomic, housing, and credit market analysis, it supports a more comprehensive, data-driven approach to assessing risk, pricing, and investment opportunities within real estate credit.