Inspiration- An inspiration for the Optiver Hackathon Challenge could be the desire to create a more ethical and stable financial market through technology. The project is driven by the vision of integrating advanced sentiment analysis with the fair-price investment method to promote transparency, reduce speculative volatility, and establish a more equitable trading environment. It's about redefining success in the trading world to value both profit and principles, aiming to inspire a shift towards conscientious investing.
What it does
Our trading algorithm, EquiTrade AI, leverages real-time sentiment analysis to execute trades that prioritize both precision and ethical investment. It scours social feeds and market data to gauge sentiment, predicting asset movements before they happen. Unlike traditional algorithms, EquiTrade AI adheres to the fair-price method, buying and selling assets at their true value rather than speculative prices. This approach aims to stabilize the market and promote investor trust. In essence, EquiTrade AI is a tool for smart, responsible trading, balancing profitability with a commitment to fairness and market integrity.
How we built it
EquiTrade AI was constructed through a multi-layered approach. Initially, we designed a sentiment analysis module using natural language processing (NLP) to interpret market moods from real-time feed posts. Concurrently, we developed a fair-price evaluation engine based on financial models to assess intrinsic asset values. These components were integrated into a machine learning framework that adapts to evolving market conditions. Backtesting on historical data refined its predictive accuracy.
Challenges we ran into
During development, our algorithm faced a challenge when the weighted average used for price prediction exceeded expected thresholds. This issue arose from anomalous spikes in sentiment-driven data, which skewed the model's output, leading to an overestimation of fair prices. It highlighted the need for robust outlier detection and recalibration of the weight distribution within our algorithm's predictive analytics.
Accomplishments that we're proud of
we are particularly proud of our breakthrough in integrating real-time sentiment analysis with fair-price calculation. This dual-approach not only enhanced predictive accuracy but also maintained ethical investment standards. Our algorithm's ability to filter out 'noise' and identify genuine market signals represents a significant advancement in trading technology
What we learned
We've learned to harness AI for real-time sentiment analysis in trading, and to apply the fair-price method systematically. This required us to innovate in data processing and financial modeling, ensuring our algorithm remained within ethical investing boundaries while adapting to live market dynamics—a valuable lesson in balancing technological capability with market responsibility.
What's next for EquiTrade
We still need to upgrade our algorithms further and create an even stronger model. The new thing that we can implement would probably be to integrate an api which detects peaks and patterns and gives suggestions after analyzing them.
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