Concise, Multi-Dimensional Explanations

of Price Action. Powered by AI.

All market participants need to know WHAT assets are moving and WHY.


What assets are moving in a way that warrants extra attention?


Why are those assets moving?

MarketReader technology answers both of these questions automatically, with high-quality results, for every tradable asset in the US. 

1. What assets are moving unusually?
MarketReader technology watches every asset in the market, filters out the noise, and highlights significant price action in real time.

2. What is causing the price action?
After identifying a significant price move, MarketReader’s specialized, AI-based models instantly deliver a concise, data-driven explanation of the most likely root cause.

Leveraging Generative AI

Simple Explanations of Market Moves At Scale

MarketReader uses generative AI models to translate complex, comprehensive market insights into clear, concise text that any investor can understand.

> See solutions for individuals

> See solutions for enterprises 

How it Works: Multi-Dimensional Explanations

MarketReader searches for explanations of asset volatility across volume and price data, factor modeling, news & social media, macro event calendars, and more.


Volume and Price Data
Factor Modeling
News & Social Media
Calendar Events

Extracting Signals from Market Data

MarketReader analyzes all intraday price movement and determines what real-time price action is historically significant. 

State-Of-The-Art Data Ingestion And Normalization System

Our proprietary system continually monitors and statistically contextualizes market data across asset classes: Equities, Crypto, Commodities, Currencies, Options, and Fixed Income.

This allows us to automatically determine what real-time price action is historically significant in the unique context of each individual asset. We are able to pinpoint unusual trade activity by analyzing tick-level trade volume and price data as well as ETF and index flow data.

Clarity from Context

By comparing each new price move to its own historical context, MarketReader is able to tell investors how unusual a price move really is—as it happens. This allows users to understand when they are looking at a truly significant price change that warrants further attention in the moment.

Processing this data allows MarketReader to establish relationships between assets and identify causal sympathy moves across the market. We paint a clear picture of how assets are moving together, revealing potential trade opportunities only a team of analysts would be able to identify otherwise. 


Mapping the Market

MarketReader answers the question, "what assets are moving together, right now, in real time?"

Providing an Advanced Wall Street Tool to Individual Investors

Factor Modeling compares two or more assets to determine if their price trajectories are statistically linked with one another. These complex calculations reveal the effects of market-moving events between assets, sectors, and entire markets.

Due to the resources required, Factor Modeling is primarily used by hedge funds and institutional investors with teams allocated to the task. The MarketReader team is putting our decades of institutional-side experience into building a similar tool accessible to individual investors at a fraction of the cost.

Mapping the Market

MarketReader answers the question, “what assets are moving together, right now, in real time?”

For each significant price move detected, our systems look at the entire market and calculate how many times in history other assets were at the same points in their contextual curves. We then surface only the most significant relationships that are relevant in real time.

Pinpointing these relationships helps identify causality, e.g. if poor earnings results for one tech company causes other tech stocks to drop in price. Overlaying our News, Social Media, and Calendar models with this data allows us to determine the most plausible root cause of the original price move.

Highlighting Relevant News And Social Media Content In Real Time

More than just a noisy social media feed.

Knowing Where to Look

Because MarketReader systems are continually monitoring all price moves across the market, we are able to filter by price action to surface only the most relevant news and social media content that is actually moving markets. 

Rather than searching endless content feeds hoping to find valuable information, we focus first on unusual price activity. Once we flag a significant price movement, we conduct an informed search to identify the most plausible explanation for the price move in near real time.

Adding Valuable Context

The rapid pace of social media serves as a valuable tool for our systems to determine what headlines were actually interesting to market participants. Of the myriad topics being discussed in a given moment, which were relevant to price movement in a specific asset or sector? 

By applying Natural Language Processing (NLP) only in specific, targeted areas of our analysis, we are able to simplify the problem and use real-time, real world signaling to identify what topics actually had an effect on the market.

Pinpointing Market Drivers Using Detailed Market Events Calendars

We search all potential market-moving events so you don’t have to.

High-Impact Macro Events

The MarketReader Calendar Model is designed to search for macro events with the potential for high impact on asset prices and pinpoint those that had a causal effect.

We look at wide-scale events including, but not limited to, FOMC announcements, CPI prints, Jobs reports and more, as well as events like dividends, stock splits, earnings announcements, filings, equities offerings from individual companies. These disparate sources of calendar information serve to paint a clear picture of what was happening during the time a significant price movement occurred.

Calendar Causality

When a large proportion of stocks are moving unusually across the entire market, our system is able to identify the macro calendar events that are truly “market moving.” When we flag unusual price or volatility movements in a disproportionate number of assets, the system scans all available data sources and attempts to match those price movements to a logical root cause.

The output of this model alerts users to movements in individual stocks and larger groups of stocks and then contextualizes those market movements around a vast array of possible calendar events occurring at the time.

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