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TrendSpider Launches AI Strategy Lab to Empower Investors to Build Custom AI-Powered Trading Models with Ease

TrendSpider Launches AI Strategy Lab to Empower Investors to Build Custom AI-Powered Trading Models with Ease

AI Strategy Lab empowers investors to seamlessly create and refine custom AI trading strategies that help identify the best time to enter trades with an optimized risk/reward profile – without a computer science degree

TrendSpider, a leading developer of cloud-based market research and trading software for active investors, announced the launch of AI Strategy Lab, a new tool that empowers traders to create, customize, train and deploy their own custom AI-powered trading strategies. The first-of-its-kind tool will provide every investor with the same advanced machine learning capabilities that were previously only accessible to large institutions, in an easy-to-use point-and-click web interface.

“AI is all the rage right now, and investors are naturally wondering how they can leverage it in their trading strategies. Unfortunately, the technical barriers to doing so have been reserved for the most sophisticated institutions – until now. TrendSpider’s AI Strategy Lab puts the power of true, purpose-built AI into the hands of every active investor,” said Brad Ritter, VP of Marketing for TrendSpider. “TrendSpider’s mission is to level the playing field for all investors, and with our new AI Strategy Lab, we are doing so in a big way.”

Unlike large language models, which are general purpose AI models designed to perform research and write code, TrendSpider’s AI Strategy Lab offers a simple point-and-click interface for training individually tailored AIs designed to identify the ideal time to enter trades. Users can train their models on any market, on any timeframe, using any goals and inputs they like. The tool allows users to define a range of trading criteria—such as timeframe, risk tolerance, and target outcomes—and quickly develops a dedicated AI model to suit these specifications.

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Once trained, AI models can be used in back testing, charts, scanners, alerts and strategies. Models are trained to generate predictive entry and controlled-risk exit signals. AI Strategy Lab empowers investors to create their own private robo-advisors, each focused on a specific asset, while staying in full control of their portfolios, with no AUM fees.

“Retail investors have always faced significant disadvantages in the markets,” said Dan Ushman, CEO of TrendSpider. “Without access to the institutional data, tools, and resources that hedge funds and other big-money players enjoy, they take on higher levels of risk with every trade. With AI Strategy Lab, we’re empowering these investors by putting decades of predictive machine learning research at their fingertips, enabling them to create models that reflect their unique strategies.”

Other Key Features of AI Strategy Lab Include:

  • Customizable Model Creation: Users can select any market (including all US stocks, ETFs, Futures contracts, Cryptocurrency pairs or Forex pairs), set trading goals, and adjust risk parameters, choose from multiple model types, and specify the exact data to use for training.
  • Support for Multiple AI Model types: Train Random Forest, Native Bayesian, KNN and Logarithmic Regression models – without any programming or technical skills needed.
  • Integration Into the Entire TrendSpider Platform: Use AI models generated in the AI Strategy Lab across TrendSpider. Paint them on charts, scan the market, incorporate custom strategies, back test and forward test them, use them in alerts and even deploy fully automated trading bots.
  • Cross-Breeding Strategies: Traders can combine multiple models together and retrain strategies to reduce bias and enhance performance for adaptable and optimized trading.
  • Automated Trading Capabilities: Once trained, models can be deployed as bots, which generate trading signals directly on TrendSpider’s charts.

Looking ahead, TrendSpider plans to expand AI Strategy Lab with a feature that will allow traders to share or sell access to their custom models. This potential marketplace aims to create a collaborative community where retail investors can leverage each other’s strategies and insights, adding a shared dimension to the platform’s robust, individualized tools.

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