Daniel Ushman is the founder and CEO of TrendSpider, with a career as a technology entrepreneur since 2001 and experience in trading since 2008. Prior to establishing TrendSpider, he served as the Chief Marketing Officer at SingleHop, a cloud computing company he co-founded, which was acquired in 2018. At SingleHop, Ushman focused on using software to automate routine technical tasks, often handled by IT departments. His experience as a trader led him to recognize the need for automation in trading processes, which inspired the creation of TrendSpider. This platform allows him to merge his interest in markets with his passion for developing new technologies.
TrendSpider, founded in 2016, was created to address inefficiencies in traditional charting platforms that often led to inconsistent analysis and costly errors. The platform has since evolved into a comprehensive technical analysis tool, distinguished by its fully customizable automation engine and advanced alert systems. TrendSpider empowers traders by providing tools to enhance accuracy and efficiency without replacing their expertise.
What inspired you to create TrendSpider, and what challenges in the trading world were you aiming to address for retail investors?
In my previous businesses, like SingleHop, we identified time-consuming, error-prone tasks and built technology to automate them. When I sold midPhase and got into investing, I noticed a glaring gap: traders and retail investors lacked tools comparable to what professionals in other industries had access to. For example, marketers have thousands of tools to streamline their workflows, and cloud computing became highly automated. Tasks that took weeks, and were time consuming, tedious and error prone, became one-click actions that executed automatically.
Yet, traders and retail investors were still doing everything manually. Technology and automation in trading had disproportionately benefited institutions with vast resources, leaving smaller investors behind. TrendSpider’s mission is to change this by leveling the playing field. We aim to give every trader—whether a seasoned professional or a high school student—the same advanced tools that hedge funds, banks, and large institutions rely on.
How does AI Strategy Lab set itself apart from other AI-driven trading tools in terms of accessibility and functionality?
Most AI trading tools today are little more than black-box algorithms disguised as AI. They don’t actually use machine learning but rely on prebuilt rules marketed as “AI.” That is dishonest.
Worse, these tools are often positioned as buy/sell signal generators, which not only fail to work reliably but also are of questionable legality since their creators are often acting as unlicensed advisors.
TrendSpider’s AI Strategy Lab, by contrast, provides traders with tools to build their own AI models, using true machine learning. It enables them to train their own custom, predictive machine learning models—tailored to their specific markets, data points, and strategies—without needing a technical background.
This point-and-click approach gives traders the power to train and deploy models using the inputs they select, ensuring transparency and adaptability. It’s not a shortcut—it’s a tool designed to empower traders with honest AI capabilities.
This is also fundamentally different from chatbots like ChatGPT. These are general purpose tools designed to write code and do research. They are not designed to analyze vast amounts of market data to generate predictive signals based on custom criteria. TrendSpider’s AI Strategy Lab offers just this capability.
What types of AI models can users create within AI Strategy Lab, and how do these models help optimize trading strategies?
AI Strategy Lab supports four types of machine learning models:
- Logistic Regression: Best for linear relationships between inputs and the probability of an event.
- Naive Bayes: Effective when inputs are independent of each other.
- K-nearest Neighbor (KNN): Useful for directly related input patterns and outcomes.
- Random Forest: Ideal for non-linear, complex relationships in data.
Users can experiment with these models to identify which fits their strategy and goals best. For example, Random Forest works well in scenarios where market behavior is complex and nonlinear, while KNN may suit simpler, more direct relationships. TrendSpider’s retraining functionality allows users to iterate and refine their strategies quickly, even without a technical background.
Could you walk us through the process of creating a custom trading model on AI Strategy Lab? How accessible is it for those without technical expertise?
Creating a custom model is straightforward:
- Define the Market & Timeframe: Choose the asset and timeframe you want to train the model on.
- Select Inputs: Decide on the data points to use—technical indicators, fundamental data, price action, or custom formulas.
- Choose a Machine Learning Model: Select one of the four supported models based on your strategy.
- Train the Model: Use the selected data to teach the model how to predict market movements.
- Test & Refine: Backtest the model on out-of-sample data to ensure it isn’t overfitted, and tweak rules for optimization.
- Cross-breed and tweak: Combine models together to enhance their predictive power, adjust conditions, and then repeat the steps above.
The interface is designed for traders, not engineers, making advanced machine learning accessible with no coding required.
What benefits does the backtesting and forward testing functionality provide, and how can traders use it to refine their strategies?
TrendSpider makes it easy to test models using vast historical data. Backtesting allows traders to validate their model with the benefit of 50 years of hindsight, to check its accuracy and performance. Forward testing takes this a step further by testing the model in live market conditions without risking real money. These functionalities help traders:
- Identify strengths and weaknesses in their models.
- Optimize entry and exit rules, add filter conditions to minimize bad entries.
- Test using different data than the model was trained on (out of sample) to ensure models are not curve-fit.
- Build confidence in strategy robustness before deployment.
With AI Strategy Lab’s focus on adaptability, how does it ensure that models respond effectively to changing market conditions?
TrendSpider allows users to retrain and crossbreed models with updated conditions, inputs, goals, or inputs. Users can also switch to different machine learning models as market conditions evolve. Advanced features like probability analysis further help refine strategies to stay relevant, ensuring models remain adaptive rather than static. Users also have the ability to train multiple models using different types of market behavior and then choose which one to employ given the conditions in the market itself, which is how many large systematic trading hedge funds operate as well.
Can you provide examples of trading strategies that retail investors might automate using the AI Strategy Lab?
The sky’s the limit here. The structure of the AI Strategy Lab affords users extensive flexibility to design models that predict market behavior in many different ways. Some examples include (but are not limited to):
- Technical Analysis Strategies: Users can build models that focus on indicators, price action, or other types of data
- Mean Reversion Strategies: Identifying when prices deviate too far from the average.
- Breakout Strategies: Predicting price movements when an asset breaks through support or resistance levels.
- Reversal Strategies: Models can be trained to attempt to predict reversals in price action.
- Continuation Strategies: Models can be designed to identify price continuation and jump in to ride waves.
- Momentum-Based Strategies: Detecting trends and riding them until they reverse.
- Hybrid Models: Combining price action with indicators like RSI or MACD for a multifaceted approach.
The cross-breeding feature in AI Strategy Lab sounds intriguing—how does it work, and what advantages does it bring to model performance?
Cross-breeding allows users to combine two or more trained models into a single hybrid model. For example, a Random Forest model trained on one set of data can be blended with another Random Forest model. This technique can improve the offspring models performance by integrating complementary strengths, reducing overfitting, and increasing robustness to different market scenarios. This is especially useful if you have discovered two or more models that have functional alpha in their probability matrix. By combining them, you can improve the resulting models ability to predict market behavior by essentially teaching it to look at the market several different ways.
How does TrendSpider address potential risks of automated trading for retail investors, particularly those who are new to AI-driven strategies?
TrendSpider incorporates safeguards to minimize risks:
- User in Control:Model outputs are essentially indicators that generate probability scores on signals. Users can choose what to do with these models. They can view them on their charts, backtest them, scan with them, and launch trading bots with them. But they are not required to do anything. We do not recommend that anyone immediately jump into live trading AI Model signals. They should take time to understand them, to backtest them, and use AI Models as part of their trading system, not its entirety.
- Transparency: Models are fully customizable and interpretable, so traders understand how decisions are made. Traders select what to predict, what market to train on, what inputs to use, and are in complete control of everything at all times.
- Backtesting & Forward Testing: These features ensure traders validate strategies before deploying them. Backtesting recommendations are to use out-of-sample data from training (e.g. never run a backtest on an AI Model using the same data it was trained on.) The system will actively warn users of this when they select a model in the Strategy Tester.
- Native Risk Management: Built-in options to set stop losses, risk tolerance, and profit targets. Models are designed to predict a fixed reward/risk ratio. If a trader trains a model to aim for a 5% gain and a maximum loss of 1%, the model will create the same conditions in the Backtesitng and Forward testing Strategies later. This provides the user with a fixed risk profile, and allows the models to focus only on entry signal generation.
- Education: Tutorials and resources to help traders navigate AI-based strategies responsibly.
- Training: TrendSpider’s team will provide 1-on-1 training to any customer who wants to learn how to use the AI Strategy Lab for free with their subscription to the platform. Part of training is to discuss the risks of how AI Models work and the best practices for employing them.
These measures ensure users retain control and avoid blindly relying on automated tools.
Thank you for the great interview, readers who wish to learn more should visit TrendSpider.
