The layer beyond price data.

We started with a simple belief: price data alone only gets you so far. Knowing where something traded is useful. Working out what might happen next, which conditions matter, and whether an idea can survive a realistic test takes another layer of work. That is the layer we wanted to build.

We’re a small quantitative trading team. Alphanume began when we opened up the real datasets we were already using internally in a multi-strategy book. They came from questions we needed to answer in our own research, with the practical demands of trading in mind.

Our mission is to shorten the distance between market information and a useful trading insight. We want researchers to spend more time testing ideas and understanding results, with less of that effort lost to reconstructing events or assembling the inputs needed to ask the question in the first place.

We handcraft each dataset around a trading question. Our work spans event histories and model-derived signals: structured observations that help you investigate how an event, a market condition, or a change in behavior relates to what follows. The goal is to make potentially predictive information easier to identify and test.

We design with the end use in mind: uncovering alpha and developing profitable trading strategies. That means thinking about what a researcher could have known at the time, how a signal enters a workflow, and what evidence would make an idea worth pursuing. Profitability is the goal guiding the work, not a promised result.

Our dataset pages describe the inputs, constructed fields, coverage, and observation policies. Our Proof studies present selected historical findings with their conditions and sample periods. Those measured results are distinct from a dataset’s intended use; each strategy still needs its own testing.

You can explore the data through the dashboard and bring it into your workflow through REST or MCP. The documentation explains access and dataset behavior so you can evaluate the fit before building around it.

The useful question for us is always what you can do with an observation. Can it help define a testable entry condition, compare similar events, or understand when an effect disappears? We want the data to make that next step easier, while leaving you room to develop and challenge your own ideas.

We build for people who want to turn a market hypothesis into something they can examine, challenge, and use in a systematic trading process:

• Independent systematic traders developing and testing their own strategies.
• Quantitative researchers investigating events, signals, and their relationship to subsequent market outcomes.
• Multi-strategy trading teams evaluating new inputs across research and internal trading workflows.

We also publish Alphanume News, covering the people and firms behind hedge funds and quantitative and proprietary trading.