TradeFlash

Standardizing Liquidity. Accelerating Execution.

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Inspiration

The institutional syndicated loan market is a $1.2 Trillion beast that still moves at the speed of the 1990s. Despite the scale, 90% of secondary trading execution still relies on manual phone calls, fragmented emails, and opaque spreadsheets. We were inspired by the contrast between the lightning-fast equity markets and the "slug-like" pace of loan trading, where a simple "Bids Wanted in Competition" (BWIC) can take 90 minutes just to clear, and settlement can drag on for 21 days due to manual compliance checks. We built TradeFlash to prove that transparency and velocity are possible in the private credit space.

What it does

TradeFlash is a comprehensive trading terminal that digitizes the entire secondary loan lifecycle:

  • Instant Price Discovery: Replaces opaque dealer quotes with a live, simulated bid/ask order book.
  • Automated BWIC Workflow: Reduces execution time from 90 minutes to under 15 minutes through a standardized blind-bidding engine.
  • AI-Powered Analytics: Uses linear regression models to predict price movements and D3.js to visualize market depth and liquidity heatmaps.
  • Due Diligence Automation: Pre-trade compliance checks (KYC/AML, transfer restrictions) are automated via a simulated credit agreement parser, generating LSTA-style documentation instantly upon trade match.

How I built it

TradeFlash is built on a modern Vite + React stack, designed with a high-density "Financial Terminal" aesthetic.

  • Visualizations: We used D3.js for real-time market depth charts and liquidity heatmaps.
  • Logic: All financial calculations (Par, Discount, Yield) are powered by Math.js. We implemented a custom WebSocket simulation to ensure real-time UI updates without a backend.
  • Mathematics: Transparent pricing is core to TradeFlash. We use LaTeX-style formulas to calculate metrics like Yield to Maturity: $$Y = \frac{C + \frac{100-P}{T}}{P} \times 100$$ Where $Y$ is the annual yield, $C$ is the coupon rate, $P$ is the current price, and $T$ is the years to maturity.

Challenges I ran into

One of the biggest hurdles was simulating the complexity of LSTA (Loan Syndications and Trading Association) protocols. Translating manual legal workflows—like checking "Eligible Assignee" status or "Minimum Assignment Amounts"—into automated logical checks required building a sophisticated mock service layer. Additionally, maintaining a high-performance UI while orchestrating 5 simultaneous AI market participants required careful React context optimization.

Accomplishments that I'm proud of

  • 6x Execution Velocity: Successfully demonstrating a BWIC workflow that completes in a fraction of the traditional time.
  • Automated Settlement: Building a system that auto-generates Trade Confirmations and Assignment Agreements the second a trade is matched.
  • AI Participant Engine: Developing realistic AI traders that respond to market events with distinct strategies.

What I learned

This project was a deep dive into the world of shadow banking and secondary market mechanics. We learned that the "bottleneck" in financial markets isn't always technology—it's often the lack of standardized data protocols. Building TradeFlash taught us how to bridge the gap between "Move Fast and Break Things" and the rigid requirements of institutional legal compliance.

What's next for TradeFlash

  • OCR Integration: Moving from simulated parsing to real LLM-powered extraction of covenants from 500-page Credit Agreements.
  • Dark Pool Implementation: Enabling completely anonymous block trading for large institucional orders.
  • Real-Time Dealer API: Connecting to real FIX protocols for live institutional liquidity.

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