Build the Next Generation of Crypto Tools

Most developers look at an API documentation and see endpoints. A visionary looks at the same documentation and sees what hasn't been built yet.

We are currently standing at a unique moment in crypto history. The "infrastructure layer" (L1s, L2s, Wallets) is mostly built. But the "insight layer"—the tools that help humans understand the chaos—is surprisingly empty. We are still using tools built in 2017 to analyze markets in 2025.

TickCatcher was built to fill this void. By providing institutional-grade data without the institutional price tag, we aren't just selling data. We are selling clay. Here are three "Invisible Empires"—three tools that don't exist yet, but should—that you can build today.

The Problem: Algorithmic trading courses are currently theoretical. Students watch videos about "Moving Averages," but they never test them on real crashes because obtaining clean historical data is too hard/expensive for a classroom setting.

The Vision: Imagine a platform where every student logs in and is instantly dropped into a simulation of The Latest Flash Crash.

• They don't scrape data.
• They don't set up databases.
• They write Python code in the browser, and it runs against TickCatcher's granular history instantly.

The Build: A simple Next.js frontend, a Monaco Editor, and a backend that proxies requests to TickCatcher.proCandles(). You aren't building a course. You are building a Flight Simulator for Quants.

The Problem: Twitter and Discord move faster than price. By the time a candle prints green, the narrative has often already shifted. Traders have to stare at TweetDeck (for news), the Economic Calendar (for CPI prints), and TradingView (for price) simultaneously.

The Vision: A "Context Engine." Imagine a chart where you don't just see price, you see Cause and Effect. You overlay price action with Economic Events. You typically see a massive candle wicking down, but your chart annotation explains why: "US CPI Released: 6.2% (Forecast: 5.8%)." You combine this with Sector Heatmaps to see that while BTC dumped on the news, the "DeFi Sector" actually held support.

The Build: Use TickCatcher.calendar() to fetch high-impact events (USD, Rate Decisions) and overlay them as vertical markers on your TickCatcher.megaCandles() charts. You aren't building a chart; you are building a Narrative Machine.

The Problem: There are millions of traders who have ideas ("Buy when RSI < 30 and Volume > Average") but cannot code Python. Current no-code tools are clunky, slow, and use daily data.

The Vision: A drag-and-drop interface that feels like magic. User drags a "Bollinger Band" block onto the canvas. User clicks "Run." 100ms later, they see the result of a backtest on the last 6 months of 1-minute data. The speed is the feature. Because TickCatcher handles the heavy lifting, your UI feels instant.

You couldn't build these tools two years ago. To build the "Market Laboratory," you would have needed to run your own archive node (cost: $3k/mo). To build the "Macro-Aware Terminal," you would have needed to scrape government websites for economic release dates.

TickCatcher removes the quarry. We give you the bricks. The question isn't "strategies." The question is: What will you architect?

Here is the seed code for the "Context Engine." It fetches high-impact economic events to correlate with market moves.

import { Configuration, CalendarApi } from "tickcatcher";
 
const config = new Configuration({ apiKey: process.env.TICKCATCHER_API_KEY });
const calendarApi = new CalendarApi(config);
 
async function getContextForMove() {
  const now = Date.now();
  const lastWeek = now - (86400000 * 7);
 
  // 1. Fetch Economic Events (The "External" Context)
  const events = await calendarApi.calendar({ 
    startDate: lastWeek, 
    endDate: now 
  });
  
  // Filter for High-Impact USD Events (CPI, NFP, Fed Rates)
  const shocks = events.filter(e => 
    e.country === 'USA' && 
    e.importance === 1
  );
 
  return shocks;
}

The world doesn't need another generic portfolio tracker. It needs specialized, high-performance tools that solve deep problems. The data bottleneck is gone. The foundation is poured.

Go build the cathedral.