Runbot - 無代碼武器可以將比特幣訂單流量不平衡獲利

有時候，市場尖叫，但很少有人知道如何聆聽。突然的比特幣激增，大量的機構資本，但大多數互聯網用戶都錯過了信號。

There are days when markets scream, but few know how to listen. A sudden Bitcoin surge, a flood of institutional capital—and yet, most internet users miss the signal. Why? Because raw information isn’t opportunity until it becomes actionable. In this era ruled by ETFs and bots, one key question emerges: can you monetize these signals without being glued to your screen or learn complex programming languages? The answer is yes—if you have the right tool and a strategy that reads between the lines of the order book.

有時候，市場尖叫，但很少有人知道如何聆聽。突然的比特幣激增，大量的機構資本，但大多數互聯網用戶都錯過了信號。為什麼？因為原始信息直到可以採取行動之前才有機會。在這個時代，由ETF和機器人統治，出現了一個關鍵問題：您能否將這些信號貨幣化而不被粘在屏幕上或學習複雜的編程語言？答案是肯定的 - 如果您有正確的工具和策略，可以在訂單書的線條之間讀取。

Bitcoin ETF Inflows: A Macro Force

比特幣ETF流入：宏觀力量

Bitcoin ETF inflows are a measure of large-scale institutional capital entering the market. When giants like BlackRock report record-breaking inflows, the crypto market reacts nearly instantly. On April 29, 2025, a $590 million surge drove BTC from $93,800 to $95,450—a 1.75% intraday jump.

比特幣ETF流入是衡量進入市場的大規模機構資本的量度。當像貝萊德（Blackrock）這樣的巨人報告破紀錄的流入時，加密貨幣市場幾乎立即做出反應。 2025年4月29日，一筆5.9億美元的激增將BTC從93,800美元提高到95,450美元，這是1.75％的盤中跳躍。

Now, these moves aren’t exactly gradual. They create a sharp imbalance between buy and sell orders, which in turn, creates a strong pressure that can be anticipated. The good news? This data is largely public. Platforms like YCharts display it openly, and it allows traders to react accordingly. But to combine this insight with a bot in real time—that's where the real opportunity lies.

現在，這些動作並不是完全逐漸的。它們在買賣訂單之間造成了巨大的不平衡，這反過來又產生了強大的壓力。好消息？這些數據在很大程度上是公開的。像Ycharts這樣的平台公開顯示它，並允許交易者做出相應的反應。但是，將這種見解與實時的機器人結合起來，這就是真正機會所在的地方。

Orderflow: A Key Indicator for Direct Action

訂單流：直接操作的關鍵指標

The Orderflow indicator tracks the real-time imbalance between market buys and sells. It’s based on a simple formula: Volume of Market Buys / (Volume of Market Buys + Volume of Market Sells). A ratio above 55% signals strong buy pressure.

訂單流指標跟踪市場購買和銷售之間的實時失衡。它基於一個簡單的公式：市場購買量 /（市場購買量 +市​​場銷售量）。比率高於55％的比率強烈購買壓力。

Unlike lagging indicators (like MACD or RSI) which are plotted on a chart with lagging candlesticks, Orderflow reacts instantly. It captures the market's tension as it unfolds, making it ideal for quickly responding to macroeconomic news or massive ETF inflows. Its interpretation is simple: a sustained high ratio (e.g., above 55%) signifies an intensity of buying that can be followed mechanically.

與落後的指示器（例如MACD或RSI）不同，這些指示器在圖表上繪製的燭台滯後，訂單流立即反應。它捕捉到市場的不斷發展時的緊張局勢，使其非常適合快速回應宏觀經濟新聞或大量ETF流入。它的解釋很簡單：持續的高比例（例如，超過55％）表示可以機械遵循的購買強度。

Runbot: The No-Code Weapon for Algo Trading

Runbot：用於算法交易的無代碼武器

Nowadays, automating your trading strategy isn't a skill reserved for Python developers. Runbot offers an intuitive interface where strategies are built like Lego structures. Each block is a condition, a trigger, or an action. Traders can select indicators (Orderflow, VWAP, Liquidations…) and define their rules with a visual logic builder. Order execution is done via Webhook connectors to exchanges like OKX.

如今，使您的交易策略自動化並不是Python開發人員保留的技能。 Runbot提供了一個直觀的界面，其中構建了樂高結構的策略。每個塊是條件，觸發器或動作。交易者可以選擇指標（訂單流，VWAP，清算…），並使用視覺邏輯構建器定義其規則。訂單執行是通過Webhook連接器完成的，以便像OKX這樣的交換。

This design allows even beginners to create robust strategies, test them with backtesting modules, and launch automation—all without writing a single line of code. It's a complete democratization of algorithmic trading.

這種設計甚至使初學者可以創建強大的策略，通過回測模塊測試它們並啟動自動化 - 所有人都沒有編寫一行代碼。這是算法交易的完全民主化。

Tutorial: Creating an Orderflow Bot

教程：創建訂單流機器人

Step 1: Open Runbot.io and create a new strategy.

步驟1：打開Runbot.io並創建一個新策略。

Step 2: Add the Orderflow indicator from the "Indicators" library.

步驟2：從“指示器”庫中添加訂單流指示器。

Step 3: Define a trigger condition: "Orderflow ratio > 55%."

步驟3：定義觸發條件：“訂單流比> 55％”。

Step 4: Set up a buy order with a specific position size, ideally limited to 2% of your capital.

步驟4：設置具有特定位置規模的買入訂單，理想情況下限制為您的資本的2％。

Step 5: Define the stop loss and take profit levels (to be chosen according to the risk you wish to take).

步驟5：定義停止損失並取得利潤水平（根據您希望承擔的風險選擇）。

Step 6: Run a 30-day backtest to validate the strategy's relevance in various market conditions.

步驟6：進行30天的回測，以驗證該策略在各種市場條件下的相關性。

Case Study: April 29, 2025

案例研究：2025年4月29日

Now, let's apply this. April 29 is a textbook case. As Bitcoin ETFs recorded a massive $590 million inflow, Bitcoin surged more than $1,600 in just a few hours. If a Runbot bot had been programmed to be triggered by a 59% Orderflow spike and react with a buy order, an entry at $94,000 and exit at $95,200 would have been realistic.

現在，讓我們應用此。 4月29日是教科書。隨著比特幣ETF的巨額投資5.9億美元的流入，比特幣在短短幾個小時內飆升了1,600美元以上。如果已經對Runbot機器人進行了編程，以通過59％的訂單流峰觸發並通過買入訂單做出反應，則條目為94,000美元，退出為95,200美元，將是現實的。

With a $500 position on OKX and 2x leverage, the net gain would be around $12, or a 2.4% return in less than half a day. A small gain, but crucial in a scalping strategy. If we repeat this on three similar signals, we achieve a 7.2% return in 24 hours—a promising start for a semi-passive scalping strategy. And it all pivots around a powerful fundamental: large-scale institutional capital.

OKX的售價為500美元和2倍槓桿，淨收益約為12美元，或者在不到半天的時間內回報2.4％。一筆小收益，但在剝頭皮策略中至關重要。如果我們在三個類似的信號上重複這一點，我們將在24小時內實現7.2％的回報，這是半完全剝頭皮策略的有希望的開始。所有這些都圍繞著強大的基本：大規模的機構資本。

Going Further: Combining Indicators for Precision

進一步：將指標合併為精確

Orderflow imbalance isn't always directly linked to institutional influx. It could also signal a massive closure of shorts, i.e., a short squeeze. To refine our reading, we can use a second indicator: Open Interest. If this rises at the same time as Orderflow exceeds 55%, we can deduce that new long positions are being opened. In this case, we’ll need

訂單流不平衡並不總是直接與機構湧入有關。它也可能標誌著短褲的大量封閉，即短擠壓。為了完善我們的閱讀，我們可以使用第二個指標：開放興趣。如果與OrderFlow超過55％的同時上升，我們可以推斷出新的長位置正在打開。在這種情況下，我們需要