Hive Devlog 2026.001 - Rolling Your Own Trading Tools

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Hive Devlog 2026.001 - Rolling Your Own Trading Tools

Hello everyone! Welcome to the first devlog of 2026 -- if you've read last year's devlog you know the format: I sit down and just write, top-of-mind, about something that's been occupying quit some of my coding hours lately.

The itch to build

OK so, for as long as I can remember trading crypto -- and that goes back long before 2017 when I originally joined "My First Graphene" chain -- I have relied on third-party tools to do the actual charting and analysis. TradingView mostly, later also Cryptowatch (which was sadly sunsetted in 2022, written in Rust blazing fast interval keyboard triggers for re-rendering), and for order management various exchange-specific UIs. And they're fine. They work. Millions of people use them and are perfectly happy.

But am I happy with them?

Not really. And I think every developer who trades eventually reaches this exact moment of critisism towards their own tooling: you KNOW you could build something that does exactly what you need, without the 47 features you never use, without the subscription fee, without someone else's design decisions dictating your workflow. Having said that, knowing you could build it and actually building it are two very different beasts ;-)

The build-vs-buy dilemma (for developers)

Let's do some quick napkin math because this is where it gets interesting from a financial perspective. A TradingView Pro subscription runs you about $15/month, so $180/year. The Pro+ tier is $30/month ($360/year) and for Premium you're looking at $60/month ($720/year). And that's just charting -- if you want automated execution you're adding 3Commas ($49/month), Cornix ($30/month), or similar.

So a serious retail trader who wants decent charting + some automation is easily spending $100-150/month on tools. That's $1,200-1,800 per year. Not nothing.

Now, how long would it take a competent developer to build a basic candlestick chart with a few indicators from scratch? Depends on the tech stack obviously, but here's my rough estimate:

• Basic OHLCV chart rendering (candlesticks, volume bars, time axis): 40-60 hours
• Technical indicators (moving averages, RSI, MACD -- the essentials): 20-30 hours
• Exchange API integration (websocket order book, real-time price feeds): 15-25 hours
• Order placement UI (limit, market, stop-loss): 20-30 hours

So ballpark 100-150 hours for something usable. At a freelance rate of, say, $50/hour that's $5,000-7,500 worth of your time. Which means purely from a cost perspective, you'd need to use your custom tool for 3-5 years before it "pays for itself" compared to just subscribing to existing tools.

So why would any sane person do this?

Control, learning, and the compounding factor

Because cost is not the only variable. When you build your own tools you get three things no subscription service can ever give you:

1. Total control over the data pipeline. You decide which exchange APIs to connect, how to normalize the data, what timeframes to aggregate. No rate limit surprises because TradingView's backend had a hiccup. No "this feature is only available on Premium tier". Your data, your rules.

2. Deep understanding of the math. I cannot stress this enough -- when you implement a Bollinger Band from scratch, or an RSI, even an SMA or EMA forces you to think beyond -- you understand it at a level that simply dragging an indicator onto a chart will never give you. You learn that a 20-period SMA with 2 standard deviations is just a convention, and that maybe for your specific trading style a 14-period EMA with 1.5 deviations works better. You learn this because you built it, you can tweak one parameter and instantly see the effect. This is compounding knowledge -- it makes you a better trader AND a better developer simultaniously.

3. Extensibility. Once the foundation is there, adding new features is incremental. Want to overlay on-chain data (whale wallet movements, exchange inflows)? Plug it in. Want to add a custom screener that filters by your own propriatary criteria? Build it. Want to connect it to a Telegram bot that alerts you at 3am when HIVE/USDT breaks a key level? A few hundred lines of Python and you're done.

The tech stack rabbit hole

Now here's where I predictably went down the rabbit hole (some things never change). The sensible approach would be to build a web-based dashboard with React or Vue, use lightweight-charts by TradingView (yes they open-sourced their charting library, which is actually excellent), connect to exchange websockets, and call it a day.

But no. My brain went: "What if we render the charts in WebGL using custom GLSL shaders?"

Why? Because:

• GPU-accelerated rendering means you can display thousands of candles without frame drops
• Custom shaders give you complete control over visual presentation (color gradients based on volume, animated transitions, custom grid overlays)
• It's cool. I'm sorry but it's just cool ;-)

Is it practical? Absolutely not for 90% of traders. Is it fun? Holy Macaroni yes it is. And fun matters when you're spending your evenings on a side project instead of watching Netflix.

The downside -- and this is a real caveat -- is that WebGL/GLSL development has a very steep learning curve if you've never done graphics programming before. You're suddenly dealing with vertex buffers, fragment shaders, coordinate space transformations, and GPU memory management. It's a completely different world from slapping a <canvas> element on a page and calling ctx.fillRect().

Here's a tiny example just to illustrate. This is a basic GLSL fragment shader that colors a candlestick based on whether it's bullish or bearish:

precision mediump float;

uniform float u_open;
uniform float u_close;

void main() {
    float bullish = step(u_open, u_close);
    vec3 green = vec3(0.18, 0.8, 0.44);
    vec3 red = vec3(0.91, 0.3, 0.24);
    vec3 color = mix(red, green, bullish);
    gl_FragColor = vec4(color, 1.0);
}

Six lines. But to get to the point where those six lines actually render a visible candle on screen, you need a vertex shader, a buffer with the candle's geometry (body rectangle + wick lines), a projection matrix that maps price/time coordinates to screen pixels, and a render loop that draws potentially thousands of these per frame. It adds up fast.

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The economics of time (a honest reflection)

I want to be honest about this because the developer community (myself included) has a tendency to romanticize "building from scratch" while quietly ignoring the opportunity cost.

Every hour I spend on charting tools is an hour I'm NOT:

• Actually trading (and potentially making money)
• Writing tutorials (which has its own compounding value on this very platform)
• Building other projects
• Or, you know, sleeping

And this is the real dilemma. If you're a full-time developer with a stable income and trading is a hobby, then building your own tools is a fantastic learning exercise with no real downside. But if trading is (or aspires to be) a significant income source, then the fastest path to revenue is using proven tools and spending your time on strategy, not infrastructure.

I reason that the answer, as with most things in life, is somewhere in the middle. Use existing tools for the 80% that's commoditized (basic charting, standard indicators, order management). Build custom for the 20% that gives you a genuine edge -- the specific analysis that no off-the-shelf tool provides because it's unique to your strategy.

What's next

I'll probaly write more about specific implementation details in future devlogs -- the WebGL rendering pipeline, how exchange websocket connections work under the hood, and maybe some thoughts on Python vs JavaScript for the data processing backend. No promises on when though, I learned my lesson about announcing future episodes from my Learn Python Series days (where I have about 20 drafted episodes sitting on my disk, mocking me silently).

For now, if you're a developer who trades: do you build your own tools or do you use off-the-shelf solutions? And if you build, what's your stack? I'm genuinely curious.

Thanks for reading!