STRATxAI is an AI Fintech pushing the boundaries in investing and asset management with AI-powered Model Portfolios and Multi-asset Investing Tools. Developing proprietary technology since 2021, including our AI investing engine, portfolio construction, backtest and analytics platforms. Our powerful investing engine leverages AI to generate signals from millions of data points, that are backtested over 2 decades of historical data. With STRATxAI you can run cutting-edge smart portfolios, that continuously adapt to beat the benchmark

STRATEGY is the bedrock of successful investing and it is often the most complicated and unavailable element. Harnessing the power of data and computing to bring trusted and proven strategies is what sets STRATxAI apart from the competition. Our Artificial Intelligence specialists are now bringing the power of machine learning to the platform with very exciting things to come in AI.

With our technology, the complex STRATA, the many layers of financial historical data are combined and refined to inform your unique trading strategy.

STRATxAI follows the Quantitative investing method, designed by humans, constructed on algorithms and proven by data over time to produce higher risk-adjusted returns than traditional active managers.

STRATxAI focuses on a form of quantitative investing known as Factor Investing. Quants extract intelligence from financial data to make smarter asset allocation decisions ie. exploit data to produce outperformance - alpha.

There are multiple benefits to the STRATxAI investing method

• Enhanced diversification

• Uncorrelated market outperformance (alpha)

• Better risk management

Backtesting is a method for seeing how well an investment strategy might have performed historically. Backtesting assesses the viability of a quantitative strategy by discovering how it would play out using data. If backtesting works, investors may have the confidence to employ it going forward. Backtesting simulates a strategy as if it were used at the time.

STRATxAI backtests over the last two decades to ensure the long-term performance of the investment strategies provided. We have in-house quantitative architecture and use Factset as our data provider, providing us with 116 gigabytes of global financial information, across 326 exchanges, going back to 1980.

There are a few important factors/biases that should be addressed and accounted for when building a backtester. Those are:

1. Point-in-time Data
2. Survivorship Bias
3. Bid-offer Spreads and Fees
4. Cost model
5. Impact model
6. Inaccurate Price Simulation