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The execution half of the problem — and why it no longer requires you to write the code yourself.
You already know what you want to buy and when you want out. The problem is that the setup fires at 3am, or during a meeting, or on the one day you stepped away from the screen. Algorithmic trading is the part of the problem that solves this — and only this.
Algorithmic trading is letting a program place and manage your orders according to rules you defined in advance: the condition triggers, the order goes out, no one has to be watching. It answers the execution question — can this rule be carried out accurately and consistently? — rather than the question of whether the rule is any good.
The two terms get used interchangeably, but they emphasise different halves of the same pipeline.
| Quant trading | Algorithmic trading | |
|---|---|---|
| Focus | How the decision is made — finding rules from data and statistics | How the decision is executed — turning rules into orders |
| Core question | Does this rule have an edge? | Can this rule be executed accurately and reliably? |
| Fails when | The edge was never there, or stopped existing | The connection drops, the order partially fills, the data feed lies |
In practice they overlap almost entirely: a quant strategy eventually needs something to run it, and the rules an algo executes are usually found through quantitative analysis. The useful way to hold it is that algo trading is the part of quant trading that gets executed. If you have not read what quant trading is, start there — the decision half comes first.
A note on vocabulary, because it causes avoidable confusion: "algorithmic trading", "algo trading", "automated trading", and — in Chinese-language markets — 程式交易 and 程序化交易 all point at the same thing. They are not four different methods to learn separately. What actually needs separating is the row in the table above: whether a rule has an edge and whether it can be executed reliably are independent problems. Getting one right and the other wrong both end in losses, but the fix is in a completely different place.
This is what stops most people, and it is worth being precise about why it is wrong.
Platforms like MultiCharts, TradeStation and TradingView let people build automated strategies through graphical builders or simplified scripting languages — PowerLanguage, EasyLanguage, Pine — with no servers to rent and nothing to deploy. (Execution differs: MultiCharts and TradeStation place orders themselves, while a TradingView strategy needs a connected broker or a webhook relay to actually trade.) Either way, the premise fails at the retail end.
But the more interesting case is the one where you can write code. Building your own execution stack still means data licensing and ingestion, an order library per venue, a backtest engine, deployment, and monitoring that survives a dropped websocket at 4am. That is months of engineering followed by permanent maintenance.
Concretely: broker attribution alone — the part that tags an order as coming from your integration — is handled differently at every venue. Across the venues Blave maintains it is a request header at some, a body field at others, a client-order-id prefix at Binance, and at KuCoin a per-request partner signature. That is one small subsystem out of many, and it never stops needing attention.
If you genuinely have an edge, your time is better spent finding the next one than fixing an API disconnection.
You describe the strategy in conversation — Telegram, a browser-based VS Code workspace, or SSH — and the agent researches it, writes the code, backtests it, and deploys it to your own dedicated server, where it runs and monitors itself 24/7. You do not write a line of code unless you want to.
Binance, BingX, OKX and Gate.io are officially supported with shipped, tested order libraries covering both spot and futures; Taiwan equities route through SinoPac. Market data — crypto, Taiwan equities, futures — comes from Blave's own API, so there is no separate feed to license and maintain.
You do not have to move platforms to use the research side. Blave Agent can export a strategy to the platform you already trade on:
| Platform | Exported as |
|---|---|
| XQ (嘉實 全球贏家) | XS trading script |
| MultiCharts | PowerLanguage |
| TradingView | Pine Script v6 |
The workflow is deliberately narrow. The Python version has to have a current backtest first — the agent will not translate logic that has not been validated. The export then adapts a known-good template rather than writing the target language from scratch, and runs a static lint before it hands you the file.
Are fees and slippage in the backtest? Automation usually raises trade frequency, and frequency is where costs compound. One real Blave Agent backtest paid 22% of its starting capital in fees over four years.
Does the signal use data it could actually have had? If the rule reads a bar's close and fills at that same bar, the backtest is using information the live system will not have. This is the most common silent inflation of a result.
Have you handled the data discontinuities? Futures roll, dividends and splits create jumps that are not price moves. A strategy that treats them as moves will trade them.
What happens when the order fails? Rejected orders, partial fills, a dropped connection mid-position. An automated strategy without an answer to these will eventually hold a position it does not know about.
Can you sit through the worst drawdown in the report? Size to the drawdown you can hold, not the return you want. The one you cannot hold is the one you switch off at the bottom.
It will not turn a rule with no edge into one that has an edge — that is a research problem, not an execution problem.
It will not keep working after the market it was built for changes. Automation makes that failure quieter, because nobody is watching the position when it stops making sense.
And an exported script is a translation, not a guarantee. Compile it, backtest it on the target platform, and compare its numbers against the Python original before you put money behind it.
Every official Blave Agent strategy is free and ships with its real backtest. Deploy one, watch how it executes, and modify it into your own version once its behaviour makes sense to you.
