Using machine learning to determine future asset prices is challenging for many reasons. Outside of the difficulty of forecasting where the market is headed and its impact on the price of a given asset, the management and joining of all of the data that can influence this price is a major difficulty. To start with the price history and trading volume of the asset (from Bloomberg for example) needs to be gathered and consolidated to understand both the current price and historical trends. Next, the history and current macroeconomic environment needs to be included from someplace like FRED. For equities and corporate bonds, the SEC filings should be included. Beyond these standard data sources, many investment decisions are beginning to be made with the help of alternative data. These can include weather for agricultural commodities or satellite imagery to understand the state of the current agricultural crop. All this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.