Runbot - 无代码武器可以将比特币订单流量不平衡获利

有时候，市场尖叫，但很少有人知道如何聆听。突然的比特币激增，大量的机构资本，但大多数互联网用户都错过了信号。

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.

有时候，市场尖叫，但很少有人知道如何聆听。突然的比特币激增，大量的机构资本，但大多数互联网用户都错过了信号。为什么？因为原始信息直到可以采取行动之前才有机会。在这个时代，由ETF和机器人统治，出现了一个关键问题：您能否将这些信号货币化而不被粘在屏幕上或学习复杂的编程语言？答案是肯定的 - 如果您有正确的工具和策略，可以在订单书的线条之间读取。

Bitcoin ETF Inflows: A Macro Force

比特币ETF流入：宏观力量

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.

比特币ETF流入是衡量进入市场的大规模机构资本的量度。当像贝莱德（Blackrock）这样的巨人报告破纪录的流入时，加密货币市场几乎立即做出反应。 2025年4月29日，一笔5.9亿美元的激增将BTC从93,800美元提高到95,450美元，这是1.75％的盘中跳跃。

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.

现在，这些动作并不是完全逐渐的。它们在买卖订单之间造成了巨大的不平衡，这反过来又产生了强大的压力。好消息？这些数据在很大程度上是公开的。像Ycharts这样的平台公开显示它，并允许交易者做出相应的反应。但是，将这种见解与实时的机器人结合起来，这就是真正机会所在的地方。

Orderflow: A Key Indicator for Direct Action

订单流：直接操作的关键指标

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.

订单流指标跟踪市场购买和销售之间的实时失衡。它基于一个简单的公式：市场购买量 /（市场购买量 +市​​场销售量）。比率高于55％的比率强烈购买压力。

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.

与落后的指示器（例如MACD或RSI）不同，这些指示器在图表上绘制的烛台滞后，订单流立即反应。它捕捉到市场的不断发展时的紧张局势，使其非常适合快速回应宏观经济新闻或大量ETF流入。它的解释很简单：持续的高比例（例如，超过55％）表示可以机械遵循的购买强度。

Runbot: The No-Code Weapon for Algo Trading

Runbot：用于算法交易的无代码武器

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，使您的交易策略自动化并不是Python开发人员保留的技能。 Runbot提供了一个直观的界面，其中构建了乐高结构的策略。每个块是条件，触发器或动作。交易者可以选择指标（订单流，VWAP，清算…），并使用视觉逻辑构建器定义其规则。订单执行是通过Webhook连接器完成的，以便像OKX这样的交换。

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.

这种设计甚至使初学者可以创建强大的策略，通过回测模块测试它们并启动自动化 - 所有人都没有编写一行代码。这是算法交易的完全民主化。

Tutorial: Creating an Orderflow Bot

教程：创建订单流机器人

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

步骤1：打开Runbot.io并创建一个新策略。

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

步骤2：从“指示器”库中添加订单流指示器。

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

步骤3：定义触发条件：“订单流比> 55％”。

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

步骤4：设置具有特定位置规模的买入订单，理想情况下限制为您的资本的2％。

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

步骤5：定义停止损失并取得利润水平（根据您希望承担的风险选择）。

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

步骤6：进行30天的回测，以验证该策略在各种市场条件下的相关性。

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.

现在，让我们应用此。 4月29日是教科书。随着比特币ETF的巨额投资5.9亿美元的流入，比特币在短短几个小时内飙升了1,600美元以上。如果已经对Runbot机器人进行了编程，以通过59％的订单流峰触发并通过买入订单做出反应，则条目为94,000美元，退出为95,200美元，将是现实的。

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的售价为500美元和2倍杠杆，净收益约为12美元，或者在不到半天的时间内回报2.4％。一笔小收益，但在剥头皮策略中至关重要。如果我们在三个类似的信号上重复这一点，我们将在24小时内实现7.2％的回报，这是半完全剥头皮策略的有希望的开始。所有这些都围绕着强大的基本：大规模的机构资本。

Going Further: Combining Indicators for Precision

进一步：将指标合并为精确

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

订单流不平衡并不总是直接与机构涌入有关。它也可能标志着短裤的大量封闭，即短挤压。为了完善我们的阅读，我们可以使用第二个指标：开放兴趣。如果与OrderFlow超过55％的同时上升，我们可以推断出新的长位置正在打开。在这种情况下，我们需要