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Quantamental Datasets

Artificial intelligence driven price and volatility signals on Wilshire 5000 US Equities.

Artificial intelligence driven price and volatility signals on Wilshire 5000 US Equities for short/mid term. With price predictions being a measure of reward and volatility being a proxy of risk, this dataset provides the ideal foundation for advanced trading strategies to improve on their reward/risk metrics.

THE TRUERISK LABS ADVANTAGE

Aggregated Data using AI models that are Self- Learning and Autonomous

Adaptive

Our team has spent years developing our AI models which are able to aggregate thousands of financial and quantitative metrics along with Big Data, and rank and sort that information faster and more consistently than human analysts or quantitative traders.

Decisions Based on Statistical Mathematics and Arbitrage

Systematic

Our team has spent years developing our AI models which are able to aggregate thousands of financial and quantitative metrics along with Big Data, and rank and sort that information faster and more consistently than human analysts or quantitative traders.

Decisions Based on Company & Macro Fundamentals

Judgement-based

The TrueRisk Labs Quantamental Dataset is real-time, alpha-generating analysis of Big Data.

AI MODELS EMPLOYED

We use ensemble meta-algorithms for primarily reducing bias and variance in supervised learning, and to convert a family of moderately-strong learners to a strong and robust one.

Our choice of method for each incoming dataset is dependent upon the structure of the raw data and proceeds through a system of checks and balances to ensure accuracy. We then employ deep-learning algorithms to layer generative models into feedback loops which are able to learn and adapt to changing conditions.

Our system was developed in collaboration with experience portfolio managers and traders from the worldʼs top firms. We understand risk and we guide our AI to produce the best risk-adjusted outputs.

GOOD INPUTS = GOOD OUTPUTS

We use ensemble meta-algorithms for primarily reducing bias and variance in supervised learning, and to convert a family of moderately-strong learners to a strong and robust one.

Our choice of method for each incoming dataset is dependent upon the structure of the raw data and proceeds through a system of checks and balances to ensure accuracy. We then employ deep-learning algorithms to layer generative models into feedback loops which are able to learn and adapt to changing conditions.

Our system was developed in collaboration with experience portfolio managers and traders from the worldʼs top firms. We understand risk and we guide our AI to produce the best risk-adjusted outputs.

Price/volume data across multiple timeframes from domestic and international securities

Quarterly released fundamental metrics and self-calculated ratios from every company in our coverage

Price/volume and technical studies of global currency, commodity, and bond markets

Quantitative correlations and patterns between all data points and feedback loops of our model outputs

Sell-side analyst forecasts of price, revenue and earnings on every security in our coverage

Technical analysis indicators across multiple timeframes on every security

Money-flows from domestic mutual funds and international equity exchanges.

OUT-OF-SAMPLE DAILY WALK FORWARD TESTING ACCURACY 2013-2018 ON WILSHIRE 5000 EQUITIES

TRL's proprietary Ai models and deep-learning algorithms allow us to provide unique insights into expected stock price and volatility movements in 3-month, 6-month, and 1-year time horizons.

Our AI models began learning in 2011 and out-out-sample testing began in 2013. There has been no fitting of the data.

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