TRADEDEQ was founded in 2023 when our team recognized a growing disparity between the sophisticated tools available to institutional traders and the limited options for retail investors.
After spending years in quantitative finance and seeing firsthand how AI and machine learning were transforming institutional trading, founder Akash Pawar realized these advantages could be made accessible to individual traders — not through complex code, but through intuitive interfaces and educational content.
“The goal isn’t to replace human judgment, but to enhance it with better information and systematic approaches.”
Our team combines experience from quantitative finance, software engineering, and trader education. We’ve worked at firms including Goldman Sachs, Two Sigma, and various prop trading firms, but we’re building TRADEDEQ as a completely independent platform.
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Our beta program is designed around collaboration. Users help shape our roadmap and learn from each other's experiences.
We publish detailed performance metrics, including poor performance periods. Our AI models come with full explanations of signal generation and confidence scoring.
Every feature includes educational content. We believe informed traders make better decisions and achieve more sustainable results.
Retail traders often fail not because they lack intelligence or dedication, but because they lack access to the same quality tools and systematic approaches used by professionals.
Our beta program is designed around collaboration. Users help shape our roadmap and learn from each other's experiences.
We prioritize risk management and realistic expectations over flashy claims. Our goal is sustainable trading success, not get-rich-quick schemes.
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Our team combines experience from quantitative finance, software engineering, and trader education. We’ve worked at firms including Goldman Sachs, Two Sigma, and various prop trading firms, but we’re building TRADEDEQ as a completely independent platform.
Key Team Members:
Former quant developer with 8 years in algorithmic trading
Full-stack engineers with experience in financial data systems
Active traders and former institutional professionals
